id sid tid token lemma pos ajst-12328 1 1 academic academic ADJ ajst-12328 1 2 journal journal NOUN ajst-12328 1 3 of of ADP ajst-12328 1 4 science science NOUN ajst-12328 1 5 and and CCONJ ajst-12328 1 6 technology technology NOUN ajst-12328 1 7 issn issn NOUN ajst-12328 1 8 : : PUNCT ajst-12328 1 9 2771 2771 NUM ajst-12328 1 10 - - SYM ajst-12328 1 11 3032 3032 NUM ajst-12328 1 12 | | NOUN ajst-12328 1 13 vol vol NOUN ajst-12328 1 14 . . PROPN ajst-12328 2 1 7 7 NUM ajst-12328 2 2 , , PUNCT ajst-12328 2 3 no no INTJ ajst-12328 2 4 . . NOUN ajst-12328 2 5 2 2 NUM ajst-12328 2 6 , , PUNCT ajst-12328 2 7 2023 2023 NUM ajst-12328 2 8 227 227 NUM ajst-12328 2 9 metal metal NOUN ajst-12328 2 10 abrasive abrasive ADJ ajst-12328 2 11 image image NOUN ajst-12328 2 12 segmentation segmentation NOUN ajst-12328 2 13 algorithm algorithm NOUN ajst-12328 2 14 based base VERB ajst-12328 2 15 on on ADP ajst-12328 2 16 k‐means k‐mean VERB ajst-12328 2 17 clustering cluster VERB ajst-12328 2 18 pengpeng pengpeng PROPN ajst-12328 2 19 zhang zhang PROPN ajst-12328 2 20 , , PUNCT ajst-12328 2 21 wei wei PROPN ajst-12328 2 22 wu wu PROPN ajst-12328 2 23 , , PUNCT ajst-12328 2 24 yu yu PROPN ajst-12328 2 25 li li PROPN ajst-12328 2 26 , , PUNCT ajst-12328 2 27 yukun yukun PROPN ajst-12328 2 28 wang wang PROPN ajst-12328 2 29 school school PROPN ajst-12328 2 30 of of ADP ajst-12328 2 31 mechanical mechanical ADJ ajst-12328 2 32 engineering engineering NOUN ajst-12328 2 33 , , PUNCT ajst-12328 2 34 xi'an xi'an PROPN ajst-12328 2 35 shiyou shiyou PROPN ajst-12328 2 36 university university PROPN ajst-12328 2 37 , , PUNCT ajst-12328 2 38 xi'an xi'an PROPN ajst-12328 2 39 710000 710000 NUM ajst-12328 2 40 , , PUNCT ajst-12328 2 41 china china PROPN ajst-12328 2 42 abstract abstract NOUN ajst-12328 2 43 : : PUNCT ajst-12328 2 44 metal metal NOUN ajst-12328 2 45 abrasive abrasive ADJ ajst-12328 2 46 image image NOUN ajst-12328 2 47 segmentation segmentation NOUN ajst-12328 2 48 is be AUX ajst-12328 2 49 one one NUM ajst-12328 2 50 of of ADP ajst-12328 2 51 the the DET ajst-12328 2 52 important important ADJ ajst-12328 2 53 image image NOUN ajst-12328 2 54 processing processing NOUN ajst-12328 2 55 tasks task NOUN ajst-12328 2 56 in in ADP ajst-12328 2 57 the the DET ajst-12328 2 58 industrial industrial ADJ ajst-12328 2 59 field field NOUN ajst-12328 2 60 . . PUNCT ajst-12328 3 1 however however ADV ajst-12328 3 2 , , PUNCT ajst-12328 3 3 due due ADP ajst-12328 3 4 to to ADP ajst-12328 3 5 the the DET ajst-12328 3 6 complex complex ADJ ajst-12328 3 7 color color NOUN ajst-12328 3 8 and and CCONJ ajst-12328 3 9 texture texture ADJ ajst-12328 3 10 characteristics characteristic NOUN ajst-12328 3 11 of of ADP ajst-12328 3 12 metal metal NOUN ajst-12328 3 13 abrasive abrasive ADJ ajst-12328 3 14 images image NOUN ajst-12328 3 15 , , PUNCT ajst-12328 3 16 as as ADV ajst-12328 3 17 well well ADV ajst-12328 3 18 as as ADP ajst-12328 3 19 difficult difficult ADJ ajst-12328 3 20 factors factor NOUN ajst-12328 3 21 such such ADJ ajst-12328 3 22 as as ADP ajst-12328 3 23 noise noise NOUN ajst-12328 3 24 and and CCONJ ajst-12328 3 25 lighting lighting NOUN ajst-12328 3 26 changes change NOUN ajst-12328 3 27 , , PUNCT ajst-12328 3 28 traditional traditional ADJ ajst-12328 3 29 image image NOUN ajst-12328 3 30 segmentation segmentation NOUN ajst-12328 3 31 methods method NOUN ajst-12328 3 32 often often ADV ajst-12328 3 33 fail fail VERB ajst-12328 3 34 to to PART ajst-12328 3 35 achieve achieve VERB ajst-12328 3 36 high high ADJ ajst-12328 3 37 accuracy accuracy NOUN ajst-12328 3 38 and and CCONJ ajst-12328 3 39 stability stability NOUN ajst-12328 3 40 . . PUNCT ajst-12328 4 1 in in ADP ajst-12328 4 2 order order NOUN ajst-12328 4 3 to to PART ajst-12328 4 4 solve solve VERB ajst-12328 4 5 this this DET ajst-12328 4 6 problem problem NOUN ajst-12328 4 7 , , PUNCT ajst-12328 4 8 a a DET ajst-12328 4 9 metal metal NOUN ajst-12328 4 10 abrasive abrasive ADJ ajst-12328 4 11 image image NOUN ajst-12328 4 12 segmentation segmentation NOUN ajst-12328 4 13 algorithm algorithm NOUN ajst-12328 4 14 based base VERB ajst-12328 4 15 on on ADP ajst-12328 4 16 k k PROPN ajst-12328 4 17 - - PUNCT ajst-12328 4 18 means means NOUN ajst-12328 4 19 clustering clustering NOUN ajst-12328 4 20 is be AUX ajst-12328 4 21 proposed propose VERB ajst-12328 4 22 . . PUNCT ajst-12328 5 1 the the DET ajst-12328 5 2 algorithm algorithm NOUN ajst-12328 5 3 applies apply VERB ajst-12328 5 4 the the DET ajst-12328 5 5 kmeans kmean NOUN ajst-12328 5 6 clustering cluster VERB ajst-12328 5 7 algorithm algorithm NOUN ajst-12328 5 8 to to ADP ajst-12328 5 9 the the DET ajst-12328 5 10 image image NOUN ajst-12328 5 11 segmentation segmentation NOUN ajst-12328 5 12 of of ADP ajst-12328 5 13 metal metal NOUN ajst-12328 5 14 abrasive abrasive ADJ ajst-12328 5 15 particles particle NOUN ajst-12328 5 16 , , PUNCT ajst-12328 5 17 and and CCONJ ajst-12328 5 18 realizes realize VERB ajst-12328 5 19 the the DET ajst-12328 5 20 separation separation NOUN ajst-12328 5 21 of of ADP ajst-12328 5 22 metal metal NOUN ajst-12328 5 23 abrasive abrasive ADJ ajst-12328 5 24 grains grain NOUN ajst-12328 5 25 from from ADP ajst-12328 5 26 the the DET ajst-12328 5 27 background background NOUN ajst-12328 5 28 by by ADP ajst-12328 5 29 clustering cluster VERB ajst-12328 5 30 the the DET ajst-12328 5 31 color color NOUN ajst-12328 5 32 features feature NOUN ajst-12328 5 33 of of ADP ajst-12328 5 34 image image NOUN ajst-12328 5 35 pixels pixel NOUN ajst-12328 5 36 . . PUNCT ajst-12328 6 1 experimental experimental ADJ ajst-12328 6 2 results result NOUN ajst-12328 6 3 show show VERB ajst-12328 6 4 that that SCONJ ajst-12328 6 5 our our PRON ajst-12328 6 6 algorithm algorithm NOUN ajst-12328 6 7 shows show VERB ajst-12328 6 8 good good ADJ ajst-12328 6 9 accuracy accuracy NOUN ajst-12328 6 10 and and CCONJ ajst-12328 6 11 stability stability NOUN ajst-12328 6 12 in in ADP ajst-12328 6 13 the the DET ajst-12328 6 14 metal metal NOUN ajst-12328 6 15 abrasive abrasive ADJ ajst-12328 6 16 image image NOUN ajst-12328 6 17 segmentation segmentation NOUN ajst-12328 6 18 task task NOUN ajst-12328 6 19 , , PUNCT ajst-12328 6 20 and and CCONJ ajst-12328 6 21 has have VERB ajst-12328 6 22 high high ADJ ajst-12328 6 23 efficiency efficiency NOUN ajst-12328 6 24 . . PUNCT ajst-12328 7 1 therefore therefore ADV ajst-12328 7 2 , , PUNCT ajst-12328 7 3 the the DET ajst-12328 7 4 algorithm algorithm NOUN ajst-12328 7 5 provides provide VERB ajst-12328 7 6 an an DET ajst-12328 7 7 accurate accurate ADJ ajst-12328 7 8 and and CCONJ ajst-12328 7 9 efficient efficient ADJ ajst-12328 7 10 method method NOUN ajst-12328 7 11 for for ADP ajst-12328 7 12 metal metal NOUN ajst-12328 7 13 abrasive abrasive ADJ ajst-12328 7 14 image image NOUN ajst-12328 7 15 segmentation segmentation NOUN ajst-12328 7 16 . . PUNCT ajst-12328 8 1 keywords keyword NOUN ajst-12328 8 2 : : PUNCT ajst-12328 8 3 metal metal NOUN ajst-12328 8 4 abrasive abrasive ADJ ajst-12328 8 5 images image NOUN ajst-12328 8 6 ; ; PUNCT ajst-12328 8 7 image image NOUN ajst-12328 8 8 segmentation segmentation NOUN ajst-12328 8 9 ; ; PUNCT ajst-12328 8 10 k k X ajst-12328 8 11 - - PUNCT ajst-12328 8 12 means mean VERB ajst-12328 8 13 clustering clustering NOUN ajst-12328 8 14 . . PUNCT ajst-12328 9 1 1 1 X ajst-12328 9 2 . . X ajst-12328 9 3 introduction introduction NOUN ajst-12328 9 4 metal metal NOUN ajst-12328 9 5 abrasive abrasive ADJ ajst-12328 9 6 image image NOUN ajst-12328 9 7 segmentation segmentation NOUN ajst-12328 9 8 is be AUX ajst-12328 9 9 one one NUM ajst-12328 9 10 of of ADP ajst-12328 9 11 the the DET ajst-12328 9 12 most most ADV ajst-12328 9 13 important important ADJ ajst-12328 9 14 image image NOUN ajst-12328 9 15 processing processing NOUN ajst-12328 9 16 tasks task NOUN ajst-12328 9 17 in in ADP ajst-12328 9 18 the the DET ajst-12328 9 19 industrial industrial ADJ ajst-12328 9 20 field field NOUN ajst-12328 9 21 . . PUNCT ajst-12328 10 1 in in ADP ajst-12328 10 2 applications application NOUN ajst-12328 10 3 such such ADJ ajst-12328 10 4 as as ADP ajst-12328 10 5 metalworking metalworking NOUN ajst-12328 10 6 , , PUNCT ajst-12328 10 7 surface surface NOUN ajst-12328 10 8 inspection inspection NOUN ajst-12328 10 9 , , PUNCT ajst-12328 10 10 and and CCONJ ajst-12328 10 11 quality quality NOUN ajst-12328 10 12 control control NOUN ajst-12328 10 13 , , PUNCT ajst-12328 10 14 accurately accurately ADV ajst-12328 10 15 segmenting segment VERB ajst-12328 10 16 metal metal NOUN ajst-12328 10 17 abrasives abrasive NOUN ajst-12328 10 18 from from ADP ajst-12328 10 19 other other ADJ ajst-12328 10 20 backgrounds background NOUN ajst-12328 10 21 is be AUX ajst-12328 10 22 an an DET ajst-12328 10 23 essential essential ADJ ajst-12328 10 24 step step NOUN ajst-12328 10 25 . . PUNCT ajst-12328 11 1 however however ADV ajst-12328 11 2 , , PUNCT ajst-12328 11 3 due due ADP ajst-12328 11 4 to to ADP ajst-12328 11 5 the the DET ajst-12328 11 6 complex complex ADJ ajst-12328 11 7 color color NOUN ajst-12328 11 8 and and CCONJ ajst-12328 11 9 texture texture ADJ ajst-12328 11 10 characteristics characteristic NOUN ajst-12328 11 11 of of ADP ajst-12328 11 12 metal metal NOUN ajst-12328 11 13 abrasive abrasive ADJ ajst-12328 11 14 images image NOUN ajst-12328 11 15 , , PUNCT ajst-12328 11 16 as as ADV ajst-12328 11 17 well well ADV ajst-12328 11 18 as as ADP ajst-12328 11 19 the the DET ajst-12328 11 20 existence existence NOUN ajst-12328 11 21 of of ADP ajst-12328 11 22 difficult difficult ADJ ajst-12328 11 23 factors factor NOUN ajst-12328 11 24 such such ADJ ajst-12328 11 25 as as ADP ajst-12328 11 26 noise noise NOUN ajst-12328 11 27 and and CCONJ ajst-12328 11 28 light light ADJ ajst-12328 11 29 changes change NOUN ajst-12328 11 30 , , PUNCT ajst-12328 11 31 traditional traditional ADJ ajst-12328 11 32 image image NOUN ajst-12328 11 33 segmentation segmentation NOUN ajst-12328 11 34 methods method NOUN ajst-12328 11 35 often often ADV ajst-12328 11 36 fail fail VERB ajst-12328 11 37 to to PART ajst-12328 11 38 achieve achieve VERB ajst-12328 11 39 high high ADJ ajst-12328 11 40 accuracy accuracy NOUN ajst-12328 11 41 and and CCONJ ajst-12328 11 42 stability stability NOUN ajst-12328 11 43 . . PUNCT ajst-12328 12 1 this this DET ajst-12328 12 2 paper paper NOUN ajst-12328 12 3 aims aim VERB ajst-12328 12 4 to to PART ajst-12328 12 5 propose propose VERB ajst-12328 12 6 a a DET ajst-12328 12 7 metal metal NOUN ajst-12328 12 8 abrasive abrasive ADJ ajst-12328 12 9 image image NOUN ajst-12328 12 10 segmentation segmentation NOUN ajst-12328 12 11 algorithm algorithm NOUN ajst-12328 12 12 based base VERB ajst-12328 12 13 on on ADP ajst-12328 12 14 k k PROPN ajst-12328 12 15 - - PUNCT ajst-12328 12 16 means means NOUN ajst-12328 12 17 clustering cluster VERB ajst-12328 12 18 to to PART ajst-12328 12 19 solve solve VERB ajst-12328 12 20 the the DET ajst-12328 12 21 limitations limitation NOUN ajst-12328 12 22 and and CCONJ ajst-12328 12 23 challenges challenge NOUN ajst-12328 12 24 of of ADP ajst-12328 12 25 traditional traditional ADJ ajst-12328 12 26 methods method NOUN ajst-12328 12 27 in in ADP ajst-12328 12 28 metal metal NOUN ajst-12328 12 29 abrasive abrasive ADJ ajst-12328 12 30 image image NOUN ajst-12328 12 31 segmentation segmentation NOUN ajst-12328 12 32 . . PUNCT ajst-12328 13 1 specifically specifically ADV ajst-12328 13 2 , , PUNCT ajst-12328 13 3 our our PRON ajst-12328 13 4 research research NOUN ajst-12328 13 5 goal goal NOUN ajst-12328 13 6 is be AUX ajst-12328 13 7 to to PART ajst-12328 13 8 design design VERB ajst-12328 13 9 an an DET ajst-12328 13 10 efficient efficient ADJ ajst-12328 13 11 and and CCONJ ajst-12328 13 12 accurate accurate ADJ ajst-12328 13 13 algorithm algorithm NOUN ajst-12328 13 14 that that PRON ajst-12328 13 15 can can AUX ajst-12328 13 16 automatically automatically ADV ajst-12328 13 17 and and CCONJ ajst-12328 13 18 efficiently efficiently ADV ajst-12328 13 19 separate separate ADJ ajst-12328 13 20 metal metal NOUN ajst-12328 13 21 abrasives abrasive NOUN ajst-12328 13 22 from from ADP ajst-12328 13 23 the the DET ajst-12328 13 24 background background NOUN ajst-12328 13 25 and and CCONJ ajst-12328 13 26 overcome overcome VERB ajst-12328 13 27 problems problem NOUN ajst-12328 13 28 such such ADJ ajst-12328 13 29 as as ADP ajst-12328 13 30 noise noise NOUN ajst-12328 13 31 , , PUNCT ajst-12328 13 32 light light ADJ ajst-12328 13 33 variation variation NOUN ajst-12328 13 34 , , PUNCT ajst-12328 13 35 and and CCONJ ajst-12328 13 36 color color NOUN ajst-12328 13 37 differences difference NOUN ajst-12328 13 38 . . PUNCT ajst-12328 14 1 through through ADP ajst-12328 14 2 the the DET ajst-12328 14 3 research research NOUN ajst-12328 14 4 in in ADP ajst-12328 14 5 this this DET ajst-12328 14 6 paper paper NOUN ajst-12328 14 7 , , PUNCT ajst-12328 14 8 we we PRON ajst-12328 14 9 hope hope VERB ajst-12328 14 10 to to PART ajst-12328 14 11 provide provide VERB ajst-12328 14 12 an an DET ajst-12328 14 13 accurate accurate ADJ ajst-12328 14 14 and and CCONJ ajst-12328 14 15 efficient efficient ADJ ajst-12328 14 16 method method NOUN ajst-12328 14 17 for for ADP ajst-12328 14 18 metal metal NOUN ajst-12328 14 19 abrasive abrasive ADJ ajst-12328 14 20 image image NOUN ajst-12328 14 21 segmentation segmentation NOUN ajst-12328 14 22 , , PUNCT ajst-12328 14 23 and and CCONJ ajst-12328 14 24 promote promote VERB ajst-12328 14 25 the the DET ajst-12328 14 26 development development NOUN ajst-12328 14 27 and and CCONJ ajst-12328 14 28 application application NOUN ajst-12328 14 29 in in ADP ajst-12328 14 30 the the DET ajst-12328 14 31 field field NOUN ajst-12328 14 32 of of ADP ajst-12328 14 33 metal metal NOUN ajst-12328 14 34 processing processing NOUN ajst-12328 14 35 and and CCONJ ajst-12328 14 36 quality quality NOUN ajst-12328 14 37 control control NOUN ajst-12328 14 38 . . PUNCT ajst-12328 15 1 2 2 X ajst-12328 15 2 . . X ajst-12328 15 3 related relate VERB ajst-12328 15 4 research research NOUN ajst-12328 15 5 2.1 2.1 NUM ajst-12328 15 6 . . PUNCT ajst-12328 16 1 application application NOUN ajst-12328 16 2 of of ADP ajst-12328 16 3 k k PROPN ajst-12328 16 4 - - PUNCT ajst-12328 16 5 means mean VERB ajst-12328 16 6 clustering cluster VERB ajst-12328 16 7 algorithm algorithm NOUN ajst-12328 16 8 in in ADP ajst-12328 16 9 image image NOUN ajst-12328 16 10 segmentation segmentation NOUN ajst-12328 16 11 image image NOUN ajst-12328 16 12 clustering cluster VERB ajst-12328 16 13 is be AUX ajst-12328 16 14 a a DET ajst-12328 16 15 classification classification NOUN ajst-12328 16 16 problem problem NOUN ajst-12328 16 17 based base VERB ajst-12328 16 18 on on ADP ajst-12328 16 19 color color NOUN ajst-12328 16 20 and and CCONJ ajst-12328 16 21 feature feature NOUN ajst-12328 16 22 space space NOUN ajst-12328 16 23 , , PUNCT ajst-12328 16 24 and and CCONJ ajst-12328 16 25 commonly commonly ADV ajst-12328 16 26 used use VERB ajst-12328 16 27 methods method NOUN ajst-12328 16 28 include include VERB ajst-12328 16 29 kmeans kmean NOUN ajst-12328 16 30 clustering cluster VERB ajst-12328 16 31 algorithm algorithm NOUN ajst-12328 16 32 ( ( PUNCT ajst-12328 16 33 k k NOUN ajst-12328 16 34 - - PUNCT ajst-12328 16 35 means mean NOUN ajst-12328 16 36 ) ) PUNCT ajst-12328 16 37 and and CCONJ ajst-12328 16 38 fuzzy fuzzy ADJ ajst-12328 16 39 c c NOUN ajst-12328 16 40 - - PUNCT ajst-12328 16 41 means means NOUN ajst-12328 16 42 clustering clustering NOUN ajst-12328 16 43 ( ( PUNCT ajst-12328 16 44 fcm fcm NOUN ajst-12328 16 45 ) ) PUNCT ajst-12328 16 46 algorithm algorithm NOUN ajst-12328 16 47 . . PUNCT ajst-12328 17 1 its its PRON ajst-12328 17 2 chinese chinese PROPN ajst-12328 17 3 [ [ X ajst-12328 17 4 1][4]is 1][4]is PROPN ajst-12328 17 5 processed process VERB ajst-12328 17 6 using use VERB ajst-12328 17 7 the the DET ajst-12328 17 8 fuzzy fuzzy ADJ ajst-12328 17 9 c c NOUN ajst-12328 17 10 - - PUNCT ajst-12328 17 11 means mean VERB ajst-12328 17 12 clustering clustering ADJ ajst-12328 17 13 algorithm algorithm NOUN ajst-12328 17 14 . . PUNCT ajst-12328 18 1 the the DET ajst-12328 18 2 fuzzy fuzzy ADJ ajst-12328 18 3 cmeans cmean NOUN ajst-12328 18 4 clustering cluster VERB ajst-12328 18 5 algorithm algorithm NOUN ajst-12328 18 6 can can AUX ajst-12328 18 7 handle handle VERB ajst-12328 18 8 the the DET ajst-12328 18 9 initial initial ADJ ajst-12328 18 10 parameters parameter NOUN ajst-12328 18 11 and and CCONJ ajst-12328 18 12 noise noise NOUN ajst-12328 18 13 and and CCONJ ajst-12328 18 14 grayscale grayscale NOUN ajst-12328 18 15 unevenness unevenness NOUN ajst-12328 18 16 well well ADV ajst-12328 18 17 , , PUNCT ajst-12328 18 18 but but CCONJ ajst-12328 18 19 does do AUX ajst-12328 18 20 not not PART ajst-12328 18 21 pay pay VERB ajst-12328 18 22 attention attention NOUN ajst-12328 18 23 to to ADP ajst-12328 18 24 the the DET ajst-12328 18 25 spatial spatial ADJ ajst-12328 18 26 information information NOUN ajst-12328 18 27 in in ADP ajst-12328 18 28 the the DET ajst-12328 18 29 image image NOUN ajst-12328 19 1 [ [ X ajst-12328 19 2 5 5 NUM ajst-12328 19 3 ] ] PUNCT ajst-12328 19 4 researchers researcher NOUN ajst-12328 19 5 have have AUX ajst-12328 19 6 also also ADV ajst-12328 19 7 adopted adopt VERB ajst-12328 19 8 a a DET ajst-12328 19 9 variety variety NOUN ajst-12328 19 10 of of ADP ajst-12328 19 11 methods method NOUN ajst-12328 19 12 to to PART ajst-12328 19 13 study study VERB ajst-12328 19 14 the the DET ajst-12328 19 15 k k NOUN ajst-12328 19 16 - - PUNCT ajst-12328 19 17 means mean VERB ajst-12328 19 18 clustering clustering ADJ ajst-12328 19 19 algorithm algorithm NOUN ajst-12328 19 20 , , PUNCT ajst-12328 19 21 among among ADP ajst-12328 19 22 which which PRON ajst-12328 19 23 qiu qiu PROPN ajst-12328 19 24 lijuan lijuan PROPN ajst-12328 19 25 et et PROPN ajst-12328 19 26 al al PROPN ajst-12328 19 27 . . PUNCT ajst-12328 20 1 [ [ X ajst-12328 20 2 6]proposed 6]propose VERB ajst-12328 20 3 to to PART ajst-12328 20 4 select select VERB ajst-12328 20 5 color color NOUN ajst-12328 20 6 images image NOUN ajst-12328 20 7 of of ADP ajst-12328 20 8 various various ADJ ajst-12328 20 9 morphologies morphology NOUN ajst-12328 20 10 and and CCONJ ajst-12328 20 11 colors color NOUN ajst-12328 20 12 . . PUNCT ajst-12328 21 1 on on ADP ajst-12328 21 2 the the DET ajst-12328 21 3 basis basis NOUN ajst-12328 21 4 of of ADP ajst-12328 21 5 color color NOUN ajst-12328 21 6 characteristics characteristic NOUN ajst-12328 21 7 , , PUNCT ajst-12328 21 8 cluster cluster NOUN ajst-12328 21 9 analysis analysis NOUN ajst-12328 21 10 and and CCONJ ajst-12328 21 11 maximum maximum ADJ ajst-12328 21 12 interclass interclass NOUN ajst-12328 21 13 variance variance NOUN ajst-12328 21 14 threshold threshold NOUN ajst-12328 21 15 segmentation segmentation NOUN ajst-12328 21 16 are be AUX ajst-12328 21 17 carried carry VERB ajst-12328 21 18 out out ADP ajst-12328 21 19 , , PUNCT ajst-12328 21 20 and and CCONJ ajst-12328 21 21 a a DET ajst-12328 21 22 single single ADJ ajst-12328 21 23 complete complete ADJ ajst-12328 21 24 abrasive abrasive ADJ ajst-12328 21 25 grain grain NOUN ajst-12328 21 26 is be AUX ajst-12328 21 27 obtained obtain VERB ajst-12328 21 28 after after ADP ajst-12328 21 29 image image NOUN ajst-12328 21 30 segmentation segmentation NOUN ajst-12328 21 31 , , PUNCT ajst-12328 21 32 but but CCONJ ajst-12328 21 33 the the DET ajst-12328 21 34 abrasive abrasive ADJ ajst-12328 21 35 grain grain NOUN ajst-12328 21 36 will will AUX ajst-12328 21 37 fuse fuse VERB ajst-12328 21 38 with with ADP ajst-12328 21 39 the the DET ajst-12328 21 40 background background NOUN ajst-12328 21 41 in in ADP ajst-12328 21 42 the the DET ajst-12328 21 43 area area NOUN ajst-12328 21 44 with with ADP ajst-12328 21 45 weak weak ADJ ajst-12328 21 46 light light NOUN ajst-12328 21 47 or or CCONJ ajst-12328 21 48 low low ADJ ajst-12328 21 49 relative relative ADJ ajst-12328 21 50 brightness brightness NOUN ajst-12328 21 51 of of ADP ajst-12328 21 52 the the DET ajst-12328 21 53 abrasive abrasive ADJ ajst-12328 21 54 grain grain NOUN ajst-12328 21 55 , , PUNCT ajst-12328 21 56 which which PRON ajst-12328 21 57 will will AUX ajst-12328 21 58 lose lose VERB ajst-12328 21 59 part part NOUN ajst-12328 21 60 of of ADP ajst-12328 21 61 the the DET ajst-12328 21 62 abrasive abrasive ADJ ajst-12328 21 63 grain grain NOUN ajst-12328 21 64 information information NOUN ajst-12328 21 65 , , PUNCT ajst-12328 21 66 resulting result VERB ajst-12328 21 67 in in ADP ajst-12328 21 68 incomplete incomplete ADJ ajst-12328 21 69 particle particle NOUN ajst-12328 21 70 extraction extraction NOUN ajst-12328 21 71 , , PUNCT ajst-12328 21 72 and and CCONJ ajst-12328 21 73 eventually eventually ADV ajst-12328 21 74 incomplete incomplete ADJ ajst-12328 21 75 segmentation segmentation NOUN ajst-12328 21 76 2.2 2.2 NUM ajst-12328 21 77 . . PUNCT ajst-12328 22 1 review review NOUN ajst-12328 22 2 of of ADP ajst-12328 22 3 k k NOUN ajst-12328 22 4 - - PUNCT ajst-12328 22 5 means mean VERB ajst-12328 22 6 algorithm algorithm NOUN ajst-12328 22 7 image image NOUN ajst-12328 22 8 segmentation segmentation NOUN ajst-12328 22 9 algorithm algorithm NOUN ajst-12328 22 10 the the DET ajst-12328 22 11 k k NOUN ajst-12328 22 12 - - PUNCT ajst-12328 22 13 means means NOUN ajst-12328 22 14 algorithm algorithm NOUN ajst-12328 22 15 first first ADV ajst-12328 22 16 selects select VERB ajst-12328 22 17 k k PROPN ajst-12328 22 18 points point VERB ajst-12328 22 19 from from ADP ajst-12328 22 20 the the DET ajst-12328 22 21 data data NOUN ajst-12328 22 22 sample sample NOUN ajst-12328 22 23 as as ADP ajst-12328 22 24 the the DET ajst-12328 22 25 initial initial ADJ ajst-12328 22 26 clustering clustering ADJ ajst-12328 22 27 center center NOUN ajst-12328 22 28 . . PUNCT ajst-12328 23 1 secondly secondly ADV ajst-12328 23 2 , , PUNCT ajst-12328 23 3 the the DET ajst-12328 23 4 distance distance NOUN ajst-12328 23 5 from from ADP ajst-12328 23 6 each each DET ajst-12328 23 7 sample sample NOUN ajst-12328 23 8 to to ADP ajst-12328 23 9 the the DET ajst-12328 23 10 cluster cluster NOUN ajst-12328 23 11 is be AUX ajst-12328 23 12 calculated calculate VERB ajst-12328 23 13 , , PUNCT ajst-12328 23 14 and and CCONJ ajst-12328 23 15 the the DET ajst-12328 23 16 sample sample NOUN ajst-12328 23 17 is be AUX ajst-12328 23 18 classified classify VERB ajst-12328 23 19 into into ADP ajst-12328 23 20 the the DET ajst-12328 23 21 class class NOUN ajst-12328 23 22 where where SCONJ ajst-12328 23 23 the the DET ajst-12328 23 24 cluster cluster NOUN ajst-12328 23 25 center center NOUN ajst-12328 23 26 closest close ADJ ajst-12328 23 27 to to ADP ajst-12328 23 28 it it PRON ajst-12328 23 29 is be AUX ajst-12328 23 30 located locate VERB ajst-12328 23 31 ; ; PUNCT ajst-12328 23 32 then then ADV ajst-12328 23 33 calculate calculate VERB ajst-12328 23 34 the the DET ajst-12328 23 35 average average NOUN ajst-12328 23 36 of of ADP ajst-12328 23 37 the the DET ajst-12328 23 38 data data NOUN ajst-12328 23 39 objects object NOUN ajst-12328 23 40 of of ADP ajst-12328 23 41 each each DET ajst-12328 23 42 newly newly ADV ajst-12328 23 43 formed form VERB ajst-12328 23 44 cluster cluster NOUN ajst-12328 23 45 to to PART ajst-12328 23 46 obtain obtain VERB ajst-12328 23 47 the the DET ajst-12328 23 48 new new ADJ ajst-12328 23 49 cluster cluster NOUN ajst-12328 23 50 center center NOUN ajst-12328 23 51 ; ; PUNCT ajst-12328 23 52 finally finally ADV ajst-12328 23 53 , , PUNCT ajst-12328 23 54 repeat repeat VERB ajst-12328 23 55 the the DET ajst-12328 23 56 above above ADJ ajst-12328 23 57 steps step NOUN ajst-12328 23 58 until until SCONJ ajst-12328 23 59 there there PRON ajst-12328 23 60 is be VERB ajst-12328 23 61 no no DET ajst-12328 23 62 change change NOUN ajst-12328 23 63 in in ADP ajst-12328 23 64 the the DET ajst-12328 23 65 cluster cluster NOUN ajst-12328 23 66 center center NOUN ajst-12328 23 67 of of ADP ajst-12328 23 68 the the DET ajst-12328 23 69 adjacent adjacent ADJ ajst-12328 23 70 two two NUM ajst-12328 23 71 times time NOUN ajst-12328 23 72 , , PUNCT ajst-12328 23 73 indicating indicate VERB ajst-12328 23 74 that that SCONJ ajst-12328 23 75 the the DET ajst-12328 23 76 sample sample NOUN ajst-12328 23 77 adjustment adjustment NOUN ajst-12328 23 78 is be AUX ajst-12328 23 79 over over ADV ajst-12328 23 80 and and CCONJ ajst-12328 23 81 the the DET ajst-12328 23 82 clustering clustering ADJ ajst-12328 23 83 criterion criterion NOUN ajst-12328 23 84 function function NOUN ajst-12328 23 85 reaches reach VERB ajst-12328 23 86 the the DET ajst-12328 23 87 optimal optimal ADJ ajst-12328 24 1 [ [ X ajst-12328 24 2 [ [ X ajst-12328 24 3 7][12 7][12 X ajst-12328 24 4 ] ] X ajst-12328 24 5 the the DET ajst-12328 24 6 specific specific ADJ ajst-12328 24 7 description description NOUN ajst-12328 24 8 is be AUX ajst-12328 24 9 as as SCONJ ajst-12328 24 10 follows follow VERB ajst-12328 24 11 : : PUNCT ajst-12328 24 12 ( ( PUNCT ajst-12328 24 13 1 1 X ajst-12328 24 14 ) ) PUNCT ajst-12328 24 15 take take VERB ajst-12328 24 16 any any DET ajst-12328 24 17 k k PROPN ajst-12328 24 18 attribute attribute NOUN ajst-12328 24 19 value value NOUN ajst-12328 24 20 vector vector NOUN ajst-12328 24 21 as as ADP ajst-12328 24 22 the the DET ajst-12328 24 23 initial initial ADJ ajst-12328 24 24 center center NOUN ajst-12328 24 25 , , PUNCT ajst-12328 24 26 and and CCONJ ajst-12328 24 27 let let VERB ajst-12328 24 28 this this DET ajst-12328 24 29 value value NOUN ajst-12328 24 30 be be AUX ajst-12328 24 31 , , PUNCT ajst-12328 24 32 π‘Œ π‘Œ PROPN ajst-12328 24 33 𝑙 𝑙 X ajst-12328 24 34 , , PUNCT ajst-12328 24 35 π‘Œ π‘Œ PROPN ajst-12328 24 36 𝑙 𝑙 PROPN ajst-12328 24 37 .... .... PUNCT ajst-12328 24 38 , , PUNCT ajst-12328 24 39 , , PUNCT ajst-12328 25 1 the the DET ajst-12328 25 2 π‘Œ π‘Œ PROPN ajst-12328 25 3 𝑙 𝑙 PRON ajst-12328 25 4 number number NOUN ajst-12328 25 5 of of ADP ajst-12328 25 6 loop loop NOUN ajst-12328 25 7 processing processing NOUN ajst-12328 25 8 is be AUX ajst-12328 25 9 set set VERB ajst-12328 25 10 to to ADP ajst-12328 25 11 n n CCONJ ajst-12328 25 12 , , PUNCT ajst-12328 25 13 and and CCONJ ajst-12328 25 14 its its PRON ajst-12328 25 15 initial initial ADJ ajst-12328 25 16 value value NOUN ajst-12328 25 17 is be AUX ajst-12328 25 18 set set VERB ajst-12328 25 19 to to ADP ajst-12328 25 20 1 1 NUM ajst-12328 25 21 . . PUNCT ajst-12328 26 1 ( ( PUNCT ajst-12328 26 2 2 2 X ajst-12328 26 3 ) ) PUNCT ajst-12328 26 4 all all DET ajst-12328 26 5 attribute attribute NOUN ajst-12328 26 6 value value NOUN ajst-12328 26 7 vectors vector NOUN ajst-12328 26 8 x x PRON ajst-12328 26 9 are be AUX ajst-12328 26 10 classified classify VERB ajst-12328 26 11 by by ADP ajst-12328 26 12 the the DET ajst-12328 26 13 following follow VERB ajst-12328 26 14 equation equation NOUN ajst-12328 26 15 so so SCONJ ajst-12328 26 16 that that SCONJ ajst-12328 26 17 the the DET ajst-12328 26 18 vectors vector NOUN ajst-12328 26 19 in in ADP ajst-12328 26 20 the the DET ajst-12328 26 21 vector vector NOUN ajst-12328 26 22 set set NOUN ajst-12328 26 23 { { PUNCT ajst-12328 26 24 x x NOUN ajst-12328 26 25 } } PUNCT ajst-12328 26 26 belong belong VERB ajst-12328 26 27 to to ADP ajst-12328 26 28 the the DET ajst-12328 26 29 center center NOUN ajst-12328 26 30 of of ADP ajst-12328 26 31 the the DET ajst-12328 26 32 cluster cluster NOUN ajst-12328 26 33 , , PUNCT ajst-12328 26 34 π‘Œ π‘Œ PROPN ajst-12328 26 35 𝑛 𝑛 PROPN ajst-12328 26 36 , , PUNCT ajst-12328 26 37 ... ... PUNCT ajst-12328 26 38 , , PUNCT ajst-12328 26 39 , , PUNCT ajst-12328 26 40 , , PUNCT ajst-12328 26 41 π‘Œ π‘Œ PROPN ajst-12328 26 42 𝑛 𝑛 PROPN ajst-12328 26 43 the the DET ajst-12328 26 44 corresponding corresponding ADJ ajst-12328 26 45 subset subset NOUN ajst-12328 26 46 , , PUNCT ajst-12328 26 47 π‘Œ π‘Œ PROPN ajst-12328 26 48 𝑛 𝑛 PROPN ajst-12328 26 49 π‘Œ π‘Œ PROPN ajst-12328 26 50 𝑛 𝑛 ADP ajst-12328 26 51 𝑆 𝑆 PROPN ajst-12328 26 52 𝑛 𝑛 PROPN ajst-12328 26 53 , , PUNCT ajst-12328 26 54 𝑆 𝑆 PROPN ajst-12328 26 55 𝑛 𝑛 PROPN ajst-12328 26 56 , , PUNCT ajst-12328 26 57 .... .... PUNCT ajst-12328 26 58 , , PUNCT ajst-12328 26 59 𝑆 𝑆 PROPN ajst-12328 26 60 𝑛 𝑛 PROPN ajst-12328 26 61 , , PUNCT ajst-12328 26 62 𝑆 𝑆 PROPN ajst-12328 26 63 𝑛 𝑛 PART ajst-12328 26 64 。 。 PROPN ajst-12328 26 65 𝑑 𝑑 PROPN ajst-12328 26 66 π‘šπ‘–π‘› π‘šπ‘–π‘› NOUN ajst-12328 26 67 𝑑 𝑑 PROPN ajst-12328 26 68 β†’ β†’ X ajst-12328 26 69 π‘‹πœ–π‘† π‘‹πœ–π‘† PROPN ajst-12328 26 70 𝑛 𝑛 PROPN ajst-12328 26 71 , , PUNCT ajst-12328 26 72 𝑁 𝑁 PROPN ajst-12328 26 73 β‰œ β‰œ NOUN ajst-12328 26 74 1,2 1,2 NUM ajst-12328 26 75 , , PUNCT ajst-12328 26 76 𝐾 𝐾 PROPN ajst-12328 26 77 ( ( PUNCT ajst-12328 26 78 3 3 X ajst-12328 26 79 ) ) PUNCT ajst-12328 26 80 this this PRON ajst-12328 26 81 refers refer VERB ajst-12328 26 82 to to ADP ajst-12328 26 83 the the DET ajst-12328 26 84 distance distance NOUN ajst-12328 26 85 between between ADP ajst-12328 26 86 x x PUNCT ajst-12328 26 87 and and CCONJ ajst-12328 26 88 , , PUNCT ajst-12328 26 89 defined define VERB ajst-12328 26 90 by by ADP ajst-12328 26 91 the the DET ajst-12328 26 92 following follow VERB ajst-12328 26 93 equation.𝑑 equation.𝑑 PROPN ajst-12328 26 94 π‘Œ π‘Œ PROPN ajst-12328 26 95 𝑛 𝑛 PROPN ajst-12328 26 96 𝑑 𝑑 PROPN ajst-12328 26 97 β‰œ β‰œ NOUN ajst-12328 27 1 𝑋 𝑋 PROPN ajst-12328 27 2 π‘Œ π‘Œ PROPN ajst-12328 27 3 𝑛 𝑛 PROPN ajst-12328 27 4 ( ( PUNCT ajst-12328 27 5 4 4 NUM ajst-12328 27 6 ) ) PUNCT ajst-12328 27 7 the the DET ajst-12328 27 8 center center NOUN ajst-12328 27 9 of of ADP ajst-12328 27 10 the the DET ajst-12328 27 11 new new ADJ ajst-12328 27 12 cluster cluster NOUN ajst-12328 27 13 of of ADP ajst-12328 27 14 each each DET ajst-12328 27 15 subset subset NOUN ajst-12328 27 16 ( ( PUNCT ajst-12328 27 17 𝑆 𝑆 PROPN ajst-12328 27 18 𝑛 𝑛 ADJ ajst-12328 27 19 l l NOUN ajst-12328 27 20 ) ) PUNCT ajst-12328 27 21 is be AUX ajst-12328 27 22 calculated calculate VERB ajst-12328 27 23 by by ADP ajst-12328 27 24 the the DET ajst-12328 27 25 following follow VERB ajst-12328 27 26 equationβ‰… equationβ‰… NOUN ajst-12328 27 27 1,2,3 1,2,3 NUM ajst-12328 27 28 , , PUNCT ajst-12328 27 29 … … PUNCT ajst-12328 27 30 , , PUNCT ajst-12328 27 31 k k X ajst-12328 27 32 . . PUNCT ajst-12328 28 1 π‘Œ π‘Œ PROPN ajst-12328 28 2 𝑛 𝑛 VERB ajst-12328 28 3 1 1 NUM ajst-12328 28 4 π‘Œ π‘Œ PROPN ajst-12328 28 5 𝑛 𝑛 VERB ajst-12328 28 6 1 1 NUM ajst-12328 28 7 1 1 NUM ajst-12328 28 8 𝑁 𝑁 PROPN ajst-12328 28 9 𝑋 𝑋 NOUN ajst-12328 28 10 ∈ ∈ PROPN ajst-12328 29 1 228 228 NUM ajst-12328 29 2 𝑁 𝑁 NOUN ajst-12328 29 3 the the DET ajst-12328 29 4 number number NOUN ajst-12328 29 5 of of ADP ajst-12328 29 6 elements element NOUN ajst-12328 29 7 in in ADP ajst-12328 29 8 the the DET ajst-12328 29 9 set𝑆 set𝑆 NOUN ajst-12328 29 10 𝑛 𝑛 DET ajst-12328 29 11 π‘Œ π‘Œ PROPN ajst-12328 29 12 𝑛 𝑛 ADP ajst-12328 29 13 1 1 NUM ajst-12328 29 14 the the DET ajst-12328 29 15 average average NOUN ajst-12328 29 16 of of ADP ajst-12328 29 17 the the DET ajst-12328 29 18 x x PUNCT ajst-12328 29 19 that that PRON ajst-12328 29 20 belongs𝑆 belongs𝑆 PROPN ajst-12328 29 21 𝑛 𝑛 PROPN ajst-12328 29 22 ( ( PUNCT ajst-12328 29 23 5 5 NUM ajst-12328 29 24 ) ) PUNCT ajst-12328 29 25 for for ADP ajst-12328 29 26 all all DET ajst-12328 29 27 clusters cluster NOUN ajst-12328 29 28 , , PUNCT ajst-12328 29 29 processing processing NOUN ajst-12328 29 30 ends end NOUN ajst-12328 29 31 when when SCONJ ajst-12328 29 32 the the DET ajst-12328 29 33 following follow VERB ajst-12328 29 34 equation equation NOUN ajst-12328 29 35 holds hold VERB ajst-12328 29 36 , , PUNCT ajst-12328 29 37 otherwise otherwise ADV ajst-12328 29 38 return return VERB ajst-12328 29 39 to to ADP ajst-12328 29 40 ( ( PUNCT ajst-12328 29 41 2 2 NUM ajst-12328 29 42 ) ) PUNCT ajst-12328 29 43 to to PART ajst-12328 29 44 continue continue VERB ajst-12328 29 45 processing process VERB ajst-12328 29 46 . . PUNCT ajst-12328 30 1 π‘Œ π‘Œ PROPN ajst-12328 30 2 𝑛 𝑛 VERB ajst-12328 30 3 1 1 NUM ajst-12328 30 4 π‘Œ π‘Œ PROPN ajst-12328 30 5 𝑛 𝑛 PRON ajst-12328 30 6 the the DET ajst-12328 30 7 algorithm algorithm NOUN ajst-12328 30 8 proposed propose VERB ajst-12328 30 9 in in ADP ajst-12328 30 10 this this DET ajst-12328 30 11 study study NOUN ajst-12328 30 12 is be AUX ajst-12328 30 13 based base VERB ajst-12328 30 14 on on ADP ajst-12328 30 15 the the DET ajst-12328 30 16 kmeans kmean NOUN ajst-12328 30 17 clustering cluster VERB ajst-12328 30 18 algorithm algorithm NOUN ajst-12328 30 19 , , PUNCT ajst-12328 30 20 which which PRON ajst-12328 30 21 is be AUX ajst-12328 30 22 a a DET ajst-12328 30 23 commonly commonly ADV ajst-12328 30 24 used use VERB ajst-12328 30 25 unsupervised unsupervised ADJ ajst-12328 30 26 learning learning NOUN ajst-12328 30 27 method method NOUN ajst-12328 30 28 that that PRON ajst-12328 30 29 can can AUX ajst-12328 30 30 cluster cluster VERB ajst-12328 30 31 image image NOUN ajst-12328 30 32 pixels pixel NOUN ajst-12328 30 33 into into ADP ajst-12328 30 34 different different ADJ ajst-12328 30 35 categories category NOUN ajst-12328 30 36 according accord VERB ajst-12328 30 37 to to ADP ajst-12328 30 38 similarity similarity NOUN ajst-12328 30 39 . . PUNCT ajst-12328 31 1 we we PRON ajst-12328 31 2 apply apply VERB ajst-12328 31 3 the the DET ajst-12328 31 4 k k NOUN ajst-12328 31 5 - - PUNCT ajst-12328 31 6 means means NOUN ajst-12328 31 7 algorithm algorithm NOUN ajst-12328 31 8 to to ADP ajst-12328 31 9 the the DET ajst-12328 31 10 image image NOUN ajst-12328 31 11 segmentation segmentation NOUN ajst-12328 31 12 of of ADP ajst-12328 31 13 metal metal NOUN ajst-12328 31 14 abrasive abrasive ADJ ajst-12328 31 15 grains grain NOUN ajst-12328 31 16 , , PUNCT ajst-12328 31 17 and and CCONJ ajst-12328 31 18 realize realize VERB ajst-12328 31 19 the the DET ajst-12328 31 20 separation separation NOUN ajst-12328 31 21 of of ADP ajst-12328 31 22 metal metal NOUN ajst-12328 31 23 abrasive abrasive ADJ ajst-12328 31 24 grains grain NOUN ajst-12328 31 25 from from ADP ajst-12328 31 26 the the DET ajst-12328 31 27 background background NOUN ajst-12328 31 28 by by ADP ajst-12328 31 29 clustering cluster VERB ajst-12328 31 30 the the DET ajst-12328 31 31 color color NOUN ajst-12328 31 32 features feature NOUN ajst-12328 31 33 of of ADP ajst-12328 31 34 image image NOUN ajst-12328 31 35 pixels pixel NOUN ajst-12328 31 36 . . PUNCT ajst-12328 32 1 flow flow NOUN ajst-12328 32 2 chart chart NOUN ajst-12328 32 3 of of ADP ajst-12328 32 4 k k NOUN ajst-12328 32 5 - - PUNCT ajst-12328 32 6 means mean VERB ajst-12328 32 7 clustering cluster VERB ajst-12328 32 8 algorithm algorithm NOUN ajst-12328 32 9 [ [ X ajst-12328 32 10 13 13 NUM ajst-12328 32 11 ] ] SYM ajst-12328 32 12 3 3 NUM ajst-12328 32 13 . . PUNCT ajst-12328 33 1 the the DET ajst-12328 33 2 details detail NOUN ajst-12328 33 3 and and CCONJ ajst-12328 33 4 process process NOUN ajst-12328 33 5 of of ADP ajst-12328 33 6 metal metal NOUN ajst-12328 33 7 abrasive abrasive ADJ ajst-12328 33 8 image image NOUN ajst-12328 33 9 segmentation segmentation NOUN ajst-12328 33 10 algorithm algorithm NOUN ajst-12328 33 11 based base VERB ajst-12328 33 12 on on ADP ajst-12328 33 13 k k PROPN ajst-12328 33 14 - - PUNCT ajst-12328 33 15 means mean VERB ajst-12328 33 16 1 1 NUM ajst-12328 33 17 . . PUNCT ajst-12328 33 18 data datum NOUN ajst-12328 33 19 pre pre ADJ ajst-12328 33 20 - - NOUN ajst-12328 33 21 processing processing NOUN ajst-12328 33 22 : : PUNCT ajst-12328 33 23 read read VERB ajst-12328 33 24 the the DET ajst-12328 33 25 metal metal NOUN ajst-12328 33 26 abrasive abrasive ADJ ajst-12328 33 27 image image NOUN ajst-12328 33 28 and and CCONJ ajst-12328 33 29 convert convert VERB ajst-12328 33 30 it it PRON ajst-12328 33 31 to to ADP ajst-12328 33 32 grayscale grayscale NOUN ajst-12328 33 33 image image NOUN ajst-12328 33 34 or or CCONJ ajst-12328 33 35 color color NOUN ajst-12328 33 36 space space NOUN ajst-12328 33 37 conversion conversion NOUN ajst-12328 33 38 for for ADP ajst-12328 33 39 subsequent subsequent ADJ ajst-12328 33 40 processing processing NOUN ajst-12328 33 41 . . PUNCT ajst-12328 34 1 the the DET ajst-12328 34 2 image image NOUN ajst-12328 34 3 is be AUX ajst-12328 34 4 denoised denoise VERB ajst-12328 34 5 and and CCONJ ajst-12328 34 6 enhanced enhance VERB ajst-12328 34 7 using use VERB ajst-12328 34 8 an an DET ajst-12328 34 9 adaptive adaptive ADJ ajst-12328 34 10 filtering filter VERB ajst-12328 34 11 algorithm algorithm NOUN ajst-12328 34 12 . . PUNCT ajst-12328 35 1 2 2 X ajst-12328 35 2 . . X ajst-12328 35 3 feature feature NOUN ajst-12328 35 4 extraction extraction NOUN ajst-12328 35 5 : : PUNCT ajst-12328 35 6 extract extract VERB ajst-12328 35 7 features feature NOUN ajst-12328 35 8 for for ADP ajst-12328 35 9 clustering cluster VERB ajst-12328 35 10 from from ADP ajst-12328 35 11 preprocessed preprocesse VERB ajst-12328 35 12 images image NOUN ajst-12328 35 13 . . PUNCT ajst-12328 36 1 common common ADJ ajst-12328 36 2 features feature NOUN ajst-12328 36 3 include include VERB ajst-12328 36 4 color color NOUN ajst-12328 36 5 features feature NOUN ajst-12328 36 6 , , PUNCT ajst-12328 36 7 texture texture NOUN ajst-12328 36 8 features feature NOUN ajst-12328 36 9 , , PUNCT ajst-12328 36 10 and and CCONJ ajst-12328 36 11 shape shape NOUN ajst-12328 36 12 features feature NOUN ajst-12328 36 13 are be AUX ajst-12328 36 14 normalized normalize VERB ajst-12328 36 15 to to PART ajst-12328 36 16 ensure ensure VERB ajst-12328 36 17 consistent consistent ADJ ajst-12328 36 18 dimensions dimension NOUN ajst-12328 36 19 between between ADP ajst-12328 36 20 different different ADJ ajst-12328 36 21 features feature NOUN ajst-12328 36 22 . . PUNCT ajst-12328 37 1 3 3 X ajst-12328 37 2 . . X ajst-12328 37 3 k k X ajst-12328 37 4 - - PUNCT ajst-12328 37 5 means mean VERB ajst-12328 37 6 clustering clustering NOUN ajst-12328 37 7 : : PUNCT ajst-12328 37 8 initialize initialize VERB ajst-12328 37 9 k k PROPN ajst-12328 37 10 cluster cluster NOUN ajst-12328 37 11 centers center NOUN ajst-12328 37 12 , , PUNCT ajst-12328 37 13 which which PRON ajst-12328 37 14 can can AUX ajst-12328 37 15 be be AUX ajst-12328 37 16 selected select VERB ajst-12328 37 17 randomly randomly ADV ajst-12328 37 18 or or CCONJ ajst-12328 37 19 determined determine VERB ajst-12328 37 20 empirically empirically ADV ajst-12328 37 21 . . PUNCT ajst-12328 38 1 the the DET ajst-12328 38 2 features feature NOUN ajst-12328 38 3 of of ADP ajst-12328 38 4 the the DET ajst-12328 38 5 image image NOUN ajst-12328 38 6 are be AUX ajst-12328 38 7 clustered cluster VERB ajst-12328 38 8 , , PUNCT ajst-12328 38 9 and and CCONJ ajst-12328 38 10 each each DET ajst-12328 38 11 pixel pixel NOUN ajst-12328 38 12 is be AUX ajst-12328 38 13 assigned assign VERB ajst-12328 38 14 to to ADP ajst-12328 38 15 the the DET ajst-12328 38 16 nearest near ADJ ajst-12328 38 17 cluster cluster NOUN ajst-12328 38 18 center center NOUN ajst-12328 38 19 to to PART ajst-12328 38 20 form form VERB ajst-12328 38 21 k k PROPN ajst-12328 38 22 categories category NOUN ajst-12328 38 23 . . PUNCT ajst-12328 39 1 update update VERB ajst-12328 39 2 the the DET ajst-12328 39 3 cluster cluster NOUN ajst-12328 39 4 center center NOUN ajst-12328 39 5 , , PUNCT ajst-12328 39 6 calculating calculate VERB ajst-12328 39 7 the the DET ajst-12328 39 8 center center ADJ ajst-12328 39 9 point point NOUN ajst-12328 39 10 of of ADP ajst-12328 39 11 each each DET ajst-12328 39 12 category category NOUN ajst-12328 39 13 as as ADP ajst-12328 39 14 the the DET ajst-12328 39 15 new new ADJ ajst-12328 39 16 cluster cluster NOUN ajst-12328 39 17 center center NOUN ajst-12328 39 18 . . PUNCT ajst-12328 40 1 repeat repeat NOUN ajst-12328 40 2 steps step NOUN ajst-12328 40 3 b b PROPN ajst-12328 40 4 and and CCONJ ajst-12328 40 5 c c PROPN ajst-12328 40 6 until until SCONJ ajst-12328 40 7 convergence convergence NOUN ajst-12328 40 8 conditions condition NOUN ajst-12328 40 9 are be AUX ajst-12328 40 10 reached reach VERB ajst-12328 40 11 ( ( PUNCT ajst-12328 40 12 e.g. e.g. ADV ajst-12328 40 13 , , PUNCT ajst-12328 40 14 cluster cluster NOUN ajst-12328 40 15 centers center NOUN ajst-12328 40 16 no no ADV ajst-12328 40 17 longer long ADV ajst-12328 40 18 change change VERB ajst-12328 40 19 significantly significantly ADV ajst-12328 40 20 ) ) PUNCT ajst-12328 40 21 . . PUNCT ajst-12328 41 1 4 4 X ajst-12328 41 2 . . X ajst-12328 41 3 segmentation segmentation NOUN ajst-12328 41 4 result result NOUN ajst-12328 41 5 generation generation NOUN ajst-12328 41 6 : : PUNCT ajst-12328 41 7 according accord VERB ajst-12328 41 8 to to ADP ajst-12328 41 9 the the DET ajst-12328 41 10 clustering clustering ADJ ajst-12328 41 11 results result NOUN ajst-12328 41 12 , , PUNCT ajst-12328 41 13 the the DET ajst-12328 41 14 pixels pixel NOUN ajst-12328 41 15 in in ADP ajst-12328 41 16 the the DET ajst-12328 41 17 image image NOUN ajst-12328 41 18 are be AUX ajst-12328 41 19 classified classify VERB ajst-12328 41 20 as as SCONJ ajst-12328 41 21 metal metal NOUN ajst-12328 41 22 abrasive abrasive ADJ ajst-12328 41 23 grains grain NOUN ajst-12328 41 24 or or CCONJ ajst-12328 41 25 backgrounds background NOUN ajst-12328 41 26 , , PUNCT ajst-12328 41 27 and and CCONJ ajst-12328 41 28 simple simple ADJ ajst-12328 41 29 threshold threshold NOUN ajst-12328 41 30 determination determination NOUN ajst-12328 41 31 or or CCONJ ajst-12328 41 32 other other ADJ ajst-12328 41 33 classification classification NOUN ajst-12328 41 34 rules rule NOUN ajst-12328 41 35 can can AUX ajst-12328 41 36 be be AUX ajst-12328 41 37 used use VERB ajst-12328 41 38 . . PUNCT ajst-12328 42 1 based base VERB ajst-12328 42 2 on on ADP ajst-12328 42 3 the the DET ajst-12328 42 4 segmentation segmentation NOUN ajst-12328 42 5 results result NOUN ajst-12328 42 6 , , PUNCT ajst-12328 42 7 binary binary ADJ ajst-12328 42 8 images image NOUN ajst-12328 42 9 are be AUX ajst-12328 42 10 generated generate VERB ajst-12328 42 11 , , PUNCT ajst-12328 42 12 marking mark VERB ajst-12328 42 13 metal metal NOUN ajst-12328 42 14 abrasives abrasive NOUN ajst-12328 42 15 and and CCONJ ajst-12328 42 16 backgrounds background NOUN ajst-12328 42 17 . . PUNCT ajst-12328 43 1 5 5 X ajst-12328 43 2 . . X ajst-12328 43 3 post post ADJ ajst-12328 43 4 - - ADJ ajst-12328 43 5 processing processing ADJ ajst-12328 43 6 : : PUNCT ajst-12328 43 7 post post ADJ ajst-12328 43 8 - - ADJ ajst-12328 43 9 processing processing NOUN ajst-12328 43 10 of of ADP ajst-12328 43 11 image image NOUN ajst-12328 43 12 processing processing NOUN ajst-12328 43 13 technology technology NOUN ajst-12328 43 14 on on ADP ajst-12328 43 15 segmentation segmentation NOUN ajst-12328 43 16 results result NOUN ajst-12328 43 17 , , PUNCT ajst-12328 43 18 such such ADJ ajst-12328 43 19 as as ADP ajst-12328 43 20 filling fill VERB ajst-12328 43 21 voids voids NOUN ajst-12328 43 22 , , PUNCT ajst-12328 43 23 removing remove VERB ajst-12328 43 24 small small ADJ ajst-12328 43 25 noise noise NOUN ajst-12328 43 26 , , PUNCT ajst-12328 43 27 etc etc X ajst-12328 43 28 . . X ajst-12328 43 29 , , PUNCT ajst-12328 43 30 to to PART ajst-12328 43 31 improve improve VERB ajst-12328 43 32 the the DET ajst-12328 43 33 quality quality NOUN ajst-12328 43 34 of of ADP ajst-12328 43 35 segmentation segmentation NOUN ajst-12328 43 36 results result NOUN ajst-12328 43 37 . . PUNCT ajst-12328 44 1 optionally optionally ADV ajst-12328 44 2 , , PUNCT ajst-12328 44 3 image image NOUN ajst-12328 44 4 restoration restoration NOUN ajst-12328 44 5 , , PUNCT ajst-12328 44 6 edge edge NOUN ajst-12328 44 7 enhancement enhancement NOUN ajst-12328 44 8 or or CCONJ ajst-12328 44 9 other other ADJ ajst-12328 44 10 image image NOUN ajst-12328 44 11 enhancement enhancement NOUN ajst-12328 44 12 processing processing NOUN ajst-12328 44 13 can can AUX ajst-12328 44 14 be be AUX ajst-12328 44 15 performed perform VERB ajst-12328 44 16 according accord VERB ajst-12328 44 17 to to ADP ajst-12328 44 18 the the DET ajst-12328 44 19 actual actual ADJ ajst-12328 44 20 application application NOUN ajst-12328 44 21 needs need NOUN ajst-12328 44 22 . . PUNCT ajst-12328 45 1 the the DET ajst-12328 45 2 algorithm algorithm NOUN ajst-12328 45 3 realizes realize VERB ajst-12328 45 4 the the DET ajst-12328 45 5 separation separation NOUN ajst-12328 45 6 of of ADP ajst-12328 45 7 metal metal NOUN ajst-12328 45 8 abrasive abrasive ADJ ajst-12328 45 9 grains grain NOUN ajst-12328 45 10 from from ADP ajst-12328 45 11 the the DET ajst-12328 45 12 background background NOUN ajst-12328 45 13 by by ADP ajst-12328 45 14 using use VERB ajst-12328 45 15 the the DET ajst-12328 45 16 k k ADJ ajst-12328 45 17 - - PUNCT ajst-12328 45 18 means mean VERB ajst-12328 45 19 clustering cluster VERB ajst-12328 45 20 algorithm algorithm NOUN ajst-12328 45 21 to to PART ajst-12328 45 22 cluster cluster VERB ajst-12328 45 23 the the DET ajst-12328 45 24 pixels pixel NOUN ajst-12328 45 25 in in ADP ajst-12328 45 26 the the DET ajst-12328 45 27 metal metal NOUN ajst-12328 45 28 abrasive abrasive ADJ ajst-12328 45 29 image image NOUN ajst-12328 45 30 . . PUNCT ajst-12328 46 1 by by ADP ajst-12328 46 2 clustering cluster VERB ajst-12328 46 3 the the DET ajst-12328 46 4 color color NOUN ajst-12328 46 5 features feature NOUN ajst-12328 46 6 of of ADP ajst-12328 46 7 the the DET ajst-12328 46 8 image image NOUN ajst-12328 46 9 , , PUNCT ajst-12328 46 10 the the DET ajst-12328 46 11 algorithm algorithm NOUN ajst-12328 46 12 is be AUX ajst-12328 46 13 able able ADJ ajst-12328 46 14 to to PART ajst-12328 46 15 more more ADV ajst-12328 46 16 accurately accurately ADV ajst-12328 46 17 segment segment VERB ajst-12328 46 18 the the DET ajst-12328 46 19 metal metal NOUN ajst-12328 46 20 abrasive abrasive ADJ ajst-12328 46 21 grain grain NOUN ajst-12328 46 22 from from ADP ajst-12328 46 23 229 229 NUM ajst-12328 46 24 the the DET ajst-12328 46 25 background background NOUN ajst-12328 46 26 . . PUNCT ajst-12328 47 1 at at ADP ajst-12328 47 2 the the DET ajst-12328 47 3 same same ADJ ajst-12328 47 4 time time NOUN ajst-12328 47 5 , , PUNCT ajst-12328 47 6 with with ADP ajst-12328 47 7 appropriate appropriate ADJ ajst-12328 47 8 data datum NOUN ajst-12328 47 9 pre pre NOUN ajst-12328 47 10 and and CCONJ ajst-12328 47 11 post post ADJ ajst-12328 47 12 - - ADJ ajst-12328 47 13 processing processing ADJ ajst-12328 47 14 steps step NOUN ajst-12328 47 15 , , PUNCT ajst-12328 47 16 the the DET ajst-12328 47 17 algorithm algorithm NOUN ajst-12328 47 18 can can AUX ajst-12328 47 19 also also ADV ajst-12328 47 20 improve improve VERB ajst-12328 47 21 the the DET ajst-12328 47 22 quality quality NOUN ajst-12328 47 23 of of ADP ajst-12328 47 24 segmentation segmentation NOUN ajst-12328 47 25 results result NOUN ajst-12328 47 26 . . PUNCT ajst-12328 48 1 experimental experimental ADJ ajst-12328 48 2 results result NOUN ajst-12328 48 3 show show VERB ajst-12328 48 4 that that SCONJ ajst-12328 48 5 the the DET ajst-12328 48 6 metal metal NOUN ajst-12328 48 7 abrasive abrasive ADJ ajst-12328 48 8 image image NOUN ajst-12328 48 9 segmentation segmentation NOUN ajst-12328 48 10 algorithm algorithm NOUN ajst-12328 48 11 based base VERB ajst-12328 48 12 on on ADP ajst-12328 48 13 k k X ajst-12328 48 14 - - PUNCT ajst-12328 48 15 means means NOUN ajst-12328 48 16 has have VERB ajst-12328 48 17 high high ADJ ajst-12328 48 18 accuracy accuracy NOUN ajst-12328 48 19 , , PUNCT ajst-12328 48 20 stability stability NOUN ajst-12328 48 21 and and CCONJ ajst-12328 48 22 efficiency efficiency NOUN ajst-12328 48 23 in in ADP ajst-12328 48 24 the the DET ajst-12328 48 25 metal metal NOUN ajst-12328 48 26 abrasive abrasive ADJ ajst-12328 48 27 image image NOUN ajst-12328 48 28 segmentation segmentation NOUN ajst-12328 48 29 task task NOUN ajst-12328 48 30 , , PUNCT ajst-12328 48 31 and and CCONJ ajst-12328 48 32 can can AUX ajst-12328 48 33 be be AUX ajst-12328 48 34 applied apply VERB ajst-12328 48 35 to to ADP ajst-12328 48 36 metal metal NOUN ajst-12328 48 37 processing processing NOUN ajst-12328 48 38 , , PUNCT ajst-12328 48 39 surface surface NOUN ajst-12328 48 40 detection detection NOUN ajst-12328 48 41 and and CCONJ ajst-12328 48 42 quality quality NOUN ajst-12328 48 43 control control NOUN ajst-12328 48 44 . . PUNCT ajst-12328 49 1 4 4 X ajst-12328 49 2 . . X ajst-12328 49 3 experimental experimental ADJ ajst-12328 49 4 settings setting NOUN ajst-12328 49 5 and and CCONJ ajst-12328 49 6 index index NOUN ajst-12328 49 7 selection selection NOUN ajst-12328 49 8 4.1 4.1 NUM ajst-12328 49 9 . . PUNCT ajst-12328 50 1 experimental experimental ADJ ajst-12328 50 2 setup setup NOUN ajst-12328 50 3 : : PUNCT ajst-12328 50 4 1 1 X ajst-12328 50 5 . . PUNCT ajst-12328 50 6 image image NOUN ajst-12328 50 7 selection selection NOUN ajst-12328 50 8 : : PUNCT ajst-12328 50 9 select select VERB ajst-12328 50 10 a a DET ajst-12328 50 11 metal metal NOUN ajst-12328 50 12 abrasive abrasive ADJ ajst-12328 50 13 image image NOUN ajst-12328 50 14 as as ADP ajst-12328 50 15 the the DET ajst-12328 50 16 input input NOUN ajst-12328 50 17 data datum NOUN ajst-12328 50 18 for for ADP ajst-12328 50 19 the the DET ajst-12328 50 20 experiment experiment NOUN ajst-12328 50 21 . . PUNCT ajst-12328 51 1 ensure ensure VERB ajst-12328 51 2 that that SCONJ ajst-12328 51 3 the the DET ajst-12328 51 4 selected select VERB ajst-12328 51 5 images image NOUN ajst-12328 51 6 contain contain VERB ajst-12328 51 7 metal metal NOUN ajst-12328 51 8 abrasives abrasive NOUN ajst-12328 51 9 with with ADP ajst-12328 51 10 different different ADJ ajst-12328 51 11 size size NOUN ajst-12328 51 12 , , PUNCT ajst-12328 51 13 shape shape NOUN ajst-12328 51 14 , , PUNCT ajst-12328 51 15 and and CCONJ ajst-12328 51 16 color color NOUN ajst-12328 51 17 characteristics characteristic NOUN ajst-12328 51 18 to to PART ajst-12328 51 19 fully fully ADV ajst-12328 51 20 examine examine VERB ajst-12328 51 21 the the DET ajst-12328 51 22 adaptability adaptability NOUN ajst-12328 51 23 and and CCONJ ajst-12328 51 24 robustness robustness NOUN ajst-12328 51 25 of of ADP ajst-12328 51 26 the the DET ajst-12328 51 27 algorithm algorithm NOUN ajst-12328 51 28 . . PUNCT ajst-12328 52 1 2 2 X ajst-12328 52 2 . . X ajst-12328 52 3 data datum NOUN ajst-12328 52 4 preprocessing preprocessing NOUN ajst-12328 52 5 : : PUNCT ajst-12328 52 6 preprocessing preprocesse VERB ajst-12328 52 7 operations operation NOUN ajst-12328 52 8 such such ADJ ajst-12328 52 9 as as ADP ajst-12328 52 10 grayscale grayscale NOUN ajst-12328 52 11 , , PUNCT ajst-12328 52 12 size size NOUN ajst-12328 52 13 normalization normalization NOUN ajst-12328 52 14 , , PUNCT ajst-12328 52 15 etc etc X ajst-12328 52 16 . . X ajst-12328 52 17 are be AUX ajst-12328 52 18 performed perform VERB ajst-12328 52 19 on on ADP ajst-12328 52 20 the the DET ajst-12328 52 21 selected select VERB ajst-12328 52 22 images image NOUN ajst-12328 52 23 for for ADP ajst-12328 52 24 application application NOUN ajst-12328 52 25 to to ADP ajst-12328 52 26 k k ADJ ajst-12328 52 27 - - PUNCT ajst-12328 52 28 means mean VERB ajst-12328 52 29 clustering clustering ADJ ajst-12328 52 30 algorithms algorithm NOUN ajst-12328 52 31 . . PUNCT ajst-12328 53 1 3 3 X ajst-12328 53 2 . . X ajst-12328 53 3 feature feature NOUN ajst-12328 53 4 extraction extraction NOUN ajst-12328 53 5 : : PUNCT ajst-12328 53 6 extract extract VERB ajst-12328 53 7 relevant relevant ADJ ajst-12328 53 8 feature feature NOUN ajst-12328 53 9 vectors vector NOUN ajst-12328 53 10 from from ADP ajst-12328 53 11 images image NOUN ajst-12328 53 12 . . PUNCT ajst-12328 54 1 in in ADP ajst-12328 54 2 this this DET ajst-12328 54 3 paper paper NOUN ajst-12328 54 4 , , PUNCT ajst-12328 54 5 a a DET ajst-12328 54 6 feature feature NOUN ajst-12328 54 7 vector vector NOUN ajst-12328 54 8 from from ADP ajst-12328 54 9 the the DET ajst-12328 54 10 lab lab NOUN ajst-12328 54 11 's 's PART ajst-12328 54 12 color color NOUN ajst-12328 54 13 space space NOUN ajst-12328 54 14 is be AUX ajst-12328 54 15 chosen choose VERB ajst-12328 54 16 as as ADP ajst-12328 54 17 input input NOUN ajst-12328 54 18 for for ADP ajst-12328 54 19 k k NOUN ajst-12328 54 20 - - PUNCT ajst-12328 54 21 means means NOUN ajst-12328 54 22 clustering clustering NOUN ajst-12328 54 23 . . PUNCT ajst-12328 55 1 the the DET ajst-12328 55 2 extraction extraction NOUN ajst-12328 55 3 of of ADP ajst-12328 55 4 feature feature NOUN ajst-12328 55 5 vectors vector NOUN ajst-12328 55 6 can can AUX ajst-12328 55 7 be be AUX ajst-12328 55 8 done do VERB ajst-12328 55 9 by by ADP ajst-12328 55 10 transforming transform VERB ajst-12328 55 11 the the DET ajst-12328 55 12 image image NOUN ajst-12328 55 13 into into ADP ajst-12328 55 14 lab lab NOUN ajst-12328 55 15 space space NOUN ajst-12328 55 16 and and CCONJ ajst-12328 55 17 then then ADV ajst-12328 55 18 taking take VERB ajst-12328 55 19 the the DET ajst-12328 55 20 lab lab NOUN ajst-12328 55 21 - - PUNCT ajst-12328 55 22 value value NOUN ajst-12328 55 23 of of ADP ajst-12328 55 24 each each DET ajst-12328 55 25 pixel pixel NOUN ajst-12328 55 26 as as ADP ajst-12328 55 27 part part NOUN ajst-12328 55 28 of of ADP ajst-12328 55 29 the the DET ajst-12328 55 30 feature feature NOUN ajst-12328 55 31 vector vector NOUN ajst-12328 55 32 . . PUNCT ajst-12328 56 1 4 4 X ajst-12328 56 2 . . X ajst-12328 56 3 set set VERB ajst-12328 56 4 the the DET ajst-12328 56 5 number number NOUN ajst-12328 56 6 of of ADP ajst-12328 56 7 clusters cluster NOUN ajst-12328 56 8 : : PUNCT ajst-12328 56 9 in in ADP ajst-12328 56 10 order order NOUN ajst-12328 56 11 to to PART ajst-12328 56 12 study study VERB ajst-12328 56 13 the the DET ajst-12328 56 14 metal metal NOUN ajst-12328 56 15 abrasive abrasive ADJ ajst-12328 56 16 image image NOUN ajst-12328 56 17 segmentation segmentation NOUN ajst-12328 56 18 algorithm algorithm NOUN ajst-12328 56 19 based base VERB ajst-12328 56 20 on on ADP ajst-12328 56 21 k k PROPN ajst-12328 56 22 - - PUNCT ajst-12328 56 23 means means NOUN ajst-12328 56 24 clustering clustering NOUN ajst-12328 56 25 , , PUNCT ajst-12328 56 26 it it PRON ajst-12328 56 27 is be AUX ajst-12328 56 28 necessary necessary ADJ ajst-12328 56 29 to to PART ajst-12328 56 30 set set VERB ajst-12328 56 31 different different ADJ ajst-12328 56 32 cluster cluster NOUN ajst-12328 56 33 numbers number NOUN ajst-12328 56 34 , , PUNCT ajst-12328 56 35 and and CCONJ ajst-12328 56 36 this this DET ajst-12328 56 37 paper paper NOUN ajst-12328 56 38 studies study NOUN ajst-12328 56 39 2 2 NUM ajst-12328 56 40 , , PUNCT ajst-12328 56 41 3 3 NUM ajst-12328 56 42 , , PUNCT ajst-12328 56 43 4 4 NUM ajst-12328 56 44 , , PUNCT ajst-12328 56 45 5 5 NUM ajst-12328 56 46 , , PUNCT ajst-12328 56 47 and and CCONJ ajst-12328 56 48 6 6 NUM ajst-12328 56 49 to to PART ajst-12328 56 50 compare compare VERB ajst-12328 56 51 the the DET ajst-12328 56 52 segmentation segmentation NOUN ajst-12328 56 53 effect effect NOUN ajst-12328 56 54 under under ADP ajst-12328 56 55 different different ADJ ajst-12328 56 56 cluster cluster NOUN ajst-12328 56 57 numbers number NOUN ajst-12328 56 58 . . PUNCT ajst-12328 57 1 5 5 NUM ajst-12328 57 2 . . X ajst-12328 57 3 . . PUNCT ajst-12328 58 1 analysis analysis NOUN ajst-12328 58 2 of of ADP ajst-12328 58 3 experimental experimental ADJ ajst-12328 58 4 results result NOUN ajst-12328 58 5 5.1 5.1 NUM ajst-12328 58 6 . . PUNCT ajst-12328 59 1 display display NOUN ajst-12328 59 2 of of ADP ajst-12328 59 3 experimental experimental ADJ ajst-12328 59 4 results result NOUN ajst-12328 59 5 figure figure VERB ajst-12328 59 6 1 1 NUM ajst-12328 59 7 . . PUNCT ajst-12328 59 8 original original ADJ ajst-12328 59 9 image image NOUN ajst-12328 59 10 figure figure NOUN ajst-12328 59 11 2 2 NUM ajst-12328 59 12 . . NOUN ajst-12328 59 13 number number NOUN ajst-12328 59 14 of of ADP ajst-12328 59 15 clusters cluster NOUN ajst-12328 59 16 = = SYM ajst-12328 59 17 2 2 NUM ajst-12328 59 18 figure figure NOUN ajst-12328 59 19 3 3 NUM ajst-12328 59 20 . . NOUN ajst-12328 59 21 number number NOUN ajst-12328 59 22 of of ADP ajst-12328 59 23 clusters cluster NOUN ajst-12328 59 24 = = SYM ajst-12328 59 25 3 3 NUM ajst-12328 59 26 figure figure NOUN ajst-12328 59 27 4 4 NUM ajst-12328 59 28 . . PUNCT ajst-12328 59 29 number number NOUN ajst-12328 59 30 of of ADP ajst-12328 59 31 clusters cluster NOUN ajst-12328 59 32 = = SYM ajst-12328 59 33 4 4 NUM ajst-12328 59 34 figure figure NOUN ajst-12328 59 35 5 5 NUM ajst-12328 59 36 . . PUNCT ajst-12328 59 37 number number NOUN ajst-12328 59 38 of of ADP ajst-12328 59 39 clusters cluster NOUN ajst-12328 59 40 = = SYM ajst-12328 59 41 5 5 NUM ajst-12328 59 42 figure figure NOUN ajst-12328 59 43 6 6 NUM ajst-12328 59 44 . . PUNCT ajst-12328 59 45 number number NOUN ajst-12328 59 46 of of ADP ajst-12328 59 47 clusters cluster NOUN ajst-12328 59 48 = = PUNCT ajst-12328 59 49 6 6 NUM ajst-12328 59 50 the the DET ajst-12328 59 51 effect effect NOUN ajst-12328 59 52 of of ADP ajst-12328 59 53 k k NOUN ajst-12328 59 54 - - PUNCT ajst-12328 59 55 means mean VERB ajst-12328 59 56 clustering clustering ADJ ajst-12328 59 57 segmentation segmentation NOUN ajst-12328 59 58 of of ADP ajst-12328 59 59 abrasive abrasive ADJ ajst-12328 59 60 images image NOUN ajst-12328 59 61 is be AUX ajst-12328 59 62 shown show VERB ajst-12328 59 63 in in ADP ajst-12328 59 64 figure figure NOUN ajst-12328 59 65 1~6 1~6 NUM ajst-12328 59 66 . . PUNCT ajst-12328 60 1 when when SCONJ ajst-12328 60 2 using use VERB ajst-12328 60 3 3 3 NUM ajst-12328 60 4 clusters cluster NOUN ajst-12328 60 5 , , PUNCT ajst-12328 60 6 the the DET ajst-12328 60 7 abrasive abrasive ADJ ajst-12328 60 8 grain grain NOUN ajst-12328 60 9 can can AUX ajst-12328 60 10 be be AUX ajst-12328 60 11 significantly significantly ADV ajst-12328 60 12 separated separate VERB ajst-12328 60 13 from from ADP ajst-12328 60 14 the the DET ajst-12328 60 15 background background NOUN ajst-12328 60 16 : : PUNCT ajst-12328 60 17 when when SCONJ ajst-12328 60 18 using use VERB ajst-12328 60 19 5~6 5~6 NUM ajst-12328 60 20 clusters cluster NOUN ajst-12328 60 21 , , PUNCT ajst-12328 60 22 the the DET ajst-12328 60 23 abrasive abrasive ADJ ajst-12328 60 24 , , PUNCT ajst-12328 60 25 background background NOUN ajst-12328 60 26 , , PUNCT ajst-12328 60 27 and and CCONJ ajst-12328 60 28 noise noise NOUN ajst-12328 60 29 can can AUX ajst-12328 60 30 be be AUX ajst-12328 60 31 well well ADV ajst-12328 60 32 distinguished distinguished ADJ ajst-12328 60 33 . . PUNCT ajst-12328 61 1 230 230 NUM ajst-12328 61 2 5.2 5.2 NUM ajst-12328 61 3 . . PUNCT ajst-12328 62 1 algorithm algorithm NOUN ajst-12328 62 2 superiority superiority NOUN ajst-12328 62 3 the the DET ajst-12328 62 4 metal metal NOUN ajst-12328 62 5 abrasive abrasive ADJ ajst-12328 62 6 image image NOUN ajst-12328 62 7 segmentation segmentation NOUN ajst-12328 62 8 algorithm algorithm NOUN ajst-12328 62 9 based base VERB ajst-12328 62 10 on on ADP ajst-12328 62 11 k k PROPN ajst-12328 62 12 - - PUNCT ajst-12328 62 13 means means NOUN ajst-12328 62 14 clustering clustering NOUN ajst-12328 62 15 has have VERB ajst-12328 62 16 the the DET ajst-12328 62 17 following follow VERB ajst-12328 62 18 advantages advantage NOUN ajst-12328 62 19 compared compare VERB ajst-12328 62 20 with with ADP ajst-12328 62 21 other other ADJ ajst-12328 62 22 algorithms algorithm NOUN ajst-12328 62 23 : : PUNCT ajst-12328 62 24 1 1 X ajst-12328 62 25 . . X ajst-12328 62 26 simple simple ADJ ajst-12328 62 27 and and CCONJ ajst-12328 62 28 efficient efficient ADJ ajst-12328 62 29 : : PUNCT ajst-12328 62 30 k k X ajst-12328 62 31 - - PUNCT ajst-12328 62 32 means mean VERB ajst-12328 62 33 clustering clustering ADJ ajst-12328 62 34 algorithm algorithm NOUN ajst-12328 62 35 is be AUX ajst-12328 62 36 a a DET ajst-12328 62 37 simple simple ADJ ajst-12328 62 38 and and CCONJ ajst-12328 62 39 efficient efficient ADJ ajst-12328 62 40 clustering clustering ADJ ajst-12328 62 41 algorithm algorithm NOUN ajst-12328 62 42 , , PUNCT ajst-12328 62 43 which which PRON ajst-12328 62 44 is be AUX ajst-12328 62 45 suitable suitable ADJ ajst-12328 62 46 for for ADP ajst-12328 62 47 metal metal NOUN ajst-12328 62 48 abrasive abrasive ADJ ajst-12328 62 49 image image NOUN ajst-12328 62 50 segmentation segmentation NOUN ajst-12328 62 51 tasks task NOUN ajst-12328 62 52 . . PUNCT ajst-12328 63 1 its its PRON ajst-12328 63 2 principle principle NOUN ajst-12328 63 3 is be AUX ajst-12328 63 4 simple simple ADJ ajst-12328 63 5 , , PUNCT ajst-12328 63 6 the the DET ajst-12328 63 7 calculation calculation NOUN ajst-12328 63 8 speed speed NOUN ajst-12328 63 9 is be AUX ajst-12328 63 10 fast fast ADJ ajst-12328 63 11 , , PUNCT ajst-12328 63 12 and and CCONJ ajst-12328 63 13 it it PRON ajst-12328 63 14 can can AUX ajst-12328 63 15 quickly quickly ADV ajst-12328 63 16 process process VERB ajst-12328 63 17 large large ADJ ajst-12328 63 18 - - PUNCT ajst-12328 63 19 scale scale NOUN ajst-12328 63 20 image image NOUN ajst-12328 63 21 data datum NOUN ajst-12328 63 22 [ [ X ajst-12328 63 23 14 14 NUM ajst-12328 63 24 ] ] SYM ajst-12328 63 25 2 2 NUM ajst-12328 63 26 . . PUNCT ajst-12328 64 1 no no DET ajst-12328 64 2 prior prior ADJ ajst-12328 64 3 knowledge knowledge NOUN ajst-12328 64 4 required require VERB ajst-12328 64 5 : : PUNCT ajst-12328 64 6 the the DET ajst-12328 64 7 k k NOUN ajst-12328 64 8 - - PUNCT ajst-12328 64 9 means mean VERB ajst-12328 64 10 clustering clustering ADJ ajst-12328 64 11 algorithm algorithm NOUN ajst-12328 64 12 is be AUX ajst-12328 64 13 an an DET ajst-12328 64 14 unsupervised unsupervised ADJ ajst-12328 64 15 learning learning NOUN ajst-12328 64 16 method method NOUN ajst-12328 64 17 that that PRON ajst-12328 64 18 does do AUX ajst-12328 64 19 not not PART ajst-12328 64 20 require require VERB ajst-12328 64 21 prior prior ADJ ajst-12328 64 22 knowledge knowledge NOUN ajst-12328 64 23 or or CCONJ ajst-12328 64 24 labeled label VERB ajst-12328 64 25 data datum NOUN ajst-12328 64 26 . . PUNCT ajst-12328 65 1 it it PRON ajst-12328 65 2 can can AUX ajst-12328 65 3 automatically automatically ADV ajst-12328 65 4 learn learn VERB ajst-12328 65 5 the the DET ajst-12328 65 6 clustering clustering NOUN ajst-12328 65 7 pattern pattern NOUN ajst-12328 65 8 of of ADP ajst-12328 65 9 metal metal NOUN ajst-12328 65 10 abrasives abrasive NOUN ajst-12328 65 11 from from ADP ajst-12328 65 12 the the DET ajst-12328 65 13 input input NOUN ajst-12328 65 14 image image NOUN ajst-12328 65 15 without without ADP ajst-12328 65 16 the the DET ajst-12328 65 17 need need NOUN ajst-12328 65 18 to to PART ajst-12328 65 19 label label VERB ajst-12328 65 20 the the DET ajst-12328 65 21 metal metal NOUN ajst-12328 65 22 abrasives abrasive NOUN ajst-12328 65 23 in in ADP ajst-12328 65 24 advance advance NOUN ajst-12328 65 25 or or CCONJ ajst-12328 65 26 manually manually ADV ajst-12328 65 27 provide provide VERB ajst-12328 65 28 any any DET ajst-12328 65 29 prior prior ADJ ajst-12328 65 30 information information NOUN ajst-12328 65 31 [ [ X ajst-12328 65 32 15 15 NUM ajst-12328 65 33 ] ] SYM ajst-12328 65 34 3 3 NUM ajst-12328 65 35 . . NOUN ajst-12328 65 36 high high ADJ ajst-12328 65 37 clustering clustering ADJ ajst-12328 65 38 accuracy accuracy NOUN ajst-12328 65 39 : : PUNCT ajst-12328 65 40 the the DET ajst-12328 65 41 k k NOUN ajst-12328 65 42 - - PUNCT ajst-12328 65 43 means mean VERB ajst-12328 65 44 clustering clustering ADJ ajst-12328 65 45 algorithm algorithm NOUN ajst-12328 65 46 can can AUX ajst-12328 65 47 effectively effectively ADV ajst-12328 65 48 divide divide VERB ajst-12328 65 49 the the DET ajst-12328 65 50 pixels pixel NOUN ajst-12328 65 51 in in ADP ajst-12328 65 52 the the DET ajst-12328 65 53 image image NOUN ajst-12328 65 54 into into ADP ajst-12328 65 55 different different ADJ ajst-12328 65 56 clustering clustering ADJ ajst-12328 65 57 clusters cluster NOUN ajst-12328 65 58 in in ADP ajst-12328 65 59 the the DET ajst-12328 65 60 metal metal NOUN ajst-12328 65 61 abrasive abrasive ADJ ajst-12328 65 62 image image NOUN ajst-12328 65 63 segmentation segmentation NOUN ajst-12328 65 64 task task NOUN ajst-12328 65 65 . . PUNCT ajst-12328 66 1 it it PRON ajst-12328 66 2 excels excel VERB ajst-12328 66 3 in in ADP ajst-12328 66 4 clustering clustering ADJ ajst-12328 66 5 accuracy accuracy NOUN ajst-12328 66 6 , , PUNCT ajst-12328 66 7 accurately accurately ADV ajst-12328 66 8 grouping group VERB ajst-12328 66 9 the the DET ajst-12328 66 10 same same ADJ ajst-12328 66 11 type type NOUN ajst-12328 66 12 of of ADP ajst-12328 66 13 metal metal NOUN ajst-12328 66 14 abrasive abrasive ADJ ajst-12328 66 15 pixels pixel NOUN ajst-12328 66 16 into into ADP ajst-12328 66 17 a a DET ajst-12328 66 18 class class NOUN ajst-12328 66 19 , , PUNCT ajst-12328 66 20 achieving achieve VERB ajst-12328 66 21 better well ADJ ajst-12328 66 22 segmentation segmentation NOUN ajst-12328 66 23 [ [ X ajst-12328 66 24 16 16 NUM ajst-12328 66 25 ] ] SYM ajst-12328 66 26 4 4 NUM ajst-12328 66 27 . . X ajst-12328 66 28 strong strong ADJ ajst-12328 66 29 robustness robustness NOUN ajst-12328 66 30 : : PUNCT ajst-12328 66 31 the the DET ajst-12328 66 32 k k NOUN ajst-12328 66 33 - - PUNCT ajst-12328 66 34 means mean VERB ajst-12328 66 35 clustering clustering ADJ ajst-12328 66 36 algorithm algorithm NOUN ajst-12328 66 37 has have VERB ajst-12328 66 38 good good ADJ ajst-12328 66 39 robustness robustness NOUN ajst-12328 66 40 for for ADP ajst-12328 66 41 noise noise NOUN ajst-12328 66 42 and and CCONJ ajst-12328 66 43 image image NOUN ajst-12328 66 44 changes change NOUN ajst-12328 66 45 . . PUNCT ajst-12328 67 1 in in ADP ajst-12328 67 2 the the DET ajst-12328 67 3 metal metal NOUN ajst-12328 67 4 abrasive abrasive ADJ ajst-12328 67 5 image image NOUN ajst-12328 67 6 , , PUNCT ajst-12328 67 7 there there PRON ajst-12328 67 8 may may AUX ajst-12328 67 9 be be AUX ajst-12328 67 10 noise noise NOUN ajst-12328 67 11 or or CCONJ ajst-12328 67 12 some some DET ajst-12328 67 13 metal metal NOUN ajst-12328 67 14 abrasive abrasive ADJ ajst-12328 67 15 grains grain NOUN ajst-12328 67 16 that that PRON ajst-12328 67 17 vary vary VERB ajst-12328 67 18 greatly greatly ADV ajst-12328 67 19 in in ADP ajst-12328 67 20 shape shape NOUN ajst-12328 67 21 and and CCONJ ajst-12328 67 22 color color NOUN ajst-12328 67 23 . . PUNCT ajst-12328 68 1 the the DET ajst-12328 68 2 k k NOUN ajst-12328 68 3 - - PUNCT ajst-12328 68 4 means means NOUN ajst-12328 68 5 algorithm algorithm NOUN ajst-12328 68 6 can can AUX ajst-12328 68 7 identify identify VERB ajst-12328 68 8 and and CCONJ ajst-12328 68 9 separate separate VERB ajst-12328 68 10 these these DET ajst-12328 68 11 metal metal NOUN ajst-12328 68 12 abrasive abrasive ADJ ajst-12328 68 13 particles particle NOUN ajst-12328 68 14 with with ADP ajst-12328 68 15 large large ADJ ajst-12328 68 16 changes change NOUN ajst-12328 68 17 by by ADP ajst-12328 68 18 minimizing minimize VERB ajst-12328 68 19 the the DET ajst-12328 68 20 distance distance NOUN ajst-12328 68 21 between between ADP ajst-12328 68 22 data datum NOUN ajst-12328 68 23 points point NOUN ajst-12328 68 24 and and CCONJ ajst-12328 68 25 minimizing minimize VERB ajst-12328 68 26 the the DET ajst-12328 68 27 variance variance NOUN ajst-12328 68 28 within within ADP ajst-12328 68 29 the the DET ajst-12328 68 30 cluster cluster NOUN ajst-12328 68 31 , , PUNCT ajst-12328 68 32 which which PRON ajst-12328 68 33 improves improve VERB ajst-12328 68 34 the the DET ajst-12328 68 35 robustness robustness NOUN ajst-12328 68 36 of of ADP ajst-12328 68 37 the the DET ajst-12328 68 38 algorithm algorithm NOUN ajst-12328 68 39 . . PUNCT ajst-12328 69 1 5 5 X ajst-12328 69 2 . . X ajst-12328 69 3 strong strong ADJ ajst-12328 69 4 interpretability interpretability NOUN ajst-12328 69 5 : : PUNCT ajst-12328 69 6 the the DET ajst-12328 69 7 cluster cluster NOUN ajst-12328 69 8 centers center NOUN ajst-12328 69 9 generated generate VERB ajst-12328 69 10 by by ADP ajst-12328 69 11 the the DET ajst-12328 69 12 k k PROPN ajst-12328 69 13 - - PUNCT ajst-12328 69 14 means mean VERB ajst-12328 69 15 clustering clustering ADJ ajst-12328 69 16 algorithm algorithm NOUN ajst-12328 69 17 represent represent VERB ajst-12328 69 18 different different ADJ ajst-12328 69 19 metal metal NOUN ajst-12328 69 20 abrasive abrasive ADJ ajst-12328 69 21 grain grain NOUN ajst-12328 69 22 types type NOUN ajst-12328 69 23 . . PUNCT ajst-12328 70 1 these these DET ajst-12328 70 2 clustering cluster VERB ajst-12328 70 3 centers center NOUN ajst-12328 70 4 can can AUX ajst-12328 70 5 be be AUX ajst-12328 70 6 interpreted interpret VERB ajst-12328 70 7 as as ADP ajst-12328 70 8 metal metal NOUN ajst-12328 70 9 abrasive abrasive ADJ ajst-12328 70 10 grains grain NOUN ajst-12328 70 11 with with ADP ajst-12328 70 12 similar similar ADJ ajst-12328 70 13 shape shape NOUN ajst-12328 70 14 and and CCONJ ajst-12328 70 15 color color NOUN ajst-12328 70 16 characteristics characteristic NOUN ajst-12328 70 17 . . PUNCT ajst-12328 71 1 therefore therefore ADV ajst-12328 71 2 , , PUNCT ajst-12328 71 3 the the DET ajst-12328 71 4 k k NOUN ajst-12328 71 5 - - PUNCT ajst-12328 71 6 means means NOUN ajst-12328 71 7 algorithm algorithm NOUN ajst-12328 71 8 can can AUX ajst-12328 71 9 not not PART ajst-12328 71 10 only only ADV ajst-12328 71 11 provide provide VERB ajst-12328 71 12 accurate accurate ADJ ajst-12328 71 13 segmentation segmentation NOUN ajst-12328 71 14 results result NOUN ajst-12328 71 15 , , PUNCT ajst-12328 71 16 but but CCONJ ajst-12328 71 17 also also ADV ajst-12328 71 18 provide provide VERB ajst-12328 71 19 the the DET ajst-12328 71 20 characteristic characteristic ADJ ajst-12328 71 21 understanding understanding NOUN ajst-12328 71 22 and and CCONJ ajst-12328 71 23 interpretation interpretation NOUN ajst-12328 71 24 of of ADP ajst-12328 71 25 metal metal NOUN ajst-12328 71 26 abrasive abrasive NOUN ajst-12328 71 27 [ [ X ajst-12328 71 28 17 17 NUM ajst-12328 71 29 ] ] SYM ajst-12328 71 30 6 6 NUM ajst-12328 71 31 . . PUNCT ajst-12328 71 32 . . PUNCT ajst-12328 72 1 conclusion conclusion NOUN ajst-12328 72 2 in in ADP ajst-12328 72 3 this this DET ajst-12328 72 4 paper paper NOUN ajst-12328 72 5 , , PUNCT ajst-12328 72 6 a a DET ajst-12328 72 7 metal metal NOUN ajst-12328 72 8 abrasive abrasive ADJ ajst-12328 72 9 image image NOUN ajst-12328 72 10 segmentation segmentation NOUN ajst-12328 72 11 algorithm algorithm NOUN ajst-12328 72 12 based base VERB ajst-12328 72 13 on on ADP ajst-12328 72 14 k k PROPN ajst-12328 72 15 - - PUNCT ajst-12328 72 16 means mean VERB ajst-12328 72 17 clustering clustering ADJ ajst-12328 72 18 algorithm algorithm NOUN ajst-12328 72 19 is be AUX ajst-12328 72 20 proposed propose VERB ajst-12328 72 21 and and CCONJ ajst-12328 72 22 compared compare VERB ajst-12328 72 23 with with ADP ajst-12328 72 24 other other ADJ ajst-12328 72 25 algorithms algorithm NOUN ajst-12328 72 26 . . PUNCT ajst-12328 73 1 through through ADP ajst-12328 73 2 experimental experimental ADJ ajst-12328 73 3 verification verification NOUN ajst-12328 73 4 , , PUNCT ajst-12328 73 5 the the DET ajst-12328 73 6 proposed propose VERB ajst-12328 73 7 algorithm algorithm NOUN ajst-12328 73 8 has have VERB ajst-12328 73 9 advantages advantage NOUN ajst-12328 73 10 in in ADP ajst-12328 73 11 accuracy accuracy NOUN ajst-12328 73 12 , , PUNCT ajst-12328 73 13 efficiency efficiency NOUN ajst-12328 73 14 , , PUNCT ajst-12328 73 15 unsupervised unsupervised ADJ ajst-12328 73 16 learning learning NOUN ajst-12328 73 17 , , PUNCT ajst-12328 73 18 robustness robustness NOUN ajst-12328 73 19 and and CCONJ ajst-12328 73 20 interpretability interpretability NOUN ajst-12328 73 21 . . PUNCT ajst-12328 74 1 although although SCONJ ajst-12328 74 2 the the DET ajst-12328 74 3 metal metal NOUN ajst-12328 74 4 abrasive abrasive ADJ ajst-12328 74 5 image image NOUN ajst-12328 74 6 segmentation segmentation NOUN ajst-12328 74 7 algorithm algorithm NOUN ajst-12328 74 8 based base VERB ajst-12328 74 9 on on ADP ajst-12328 74 10 k k PROPN ajst-12328 74 11 - - PUNCT ajst-12328 74 12 means means NOUN ajst-12328 74 13 clustering clustering NOUN ajst-12328 74 14 proposed propose VERB ajst-12328 74 15 in in ADP ajst-12328 74 16 this this DET ajst-12328 74 17 paper paper NOUN ajst-12328 74 18 has have AUX ajst-12328 74 19 achieved achieve VERB ajst-12328 74 20 good good ADJ ajst-12328 74 21 results result NOUN ajst-12328 74 22 in in ADP ajst-12328 74 23 the the DET ajst-12328 74 24 metal metal NOUN ajst-12328 74 25 abrasive abrasive ADJ ajst-12328 74 26 image image NOUN ajst-12328 74 27 segmentation segmentation NOUN ajst-12328 74 28 task task NOUN ajst-12328 74 29 , , PUNCT ajst-12328 74 30 there there PRON ajst-12328 74 31 are be VERB ajst-12328 74 32 still still ADV ajst-12328 74 33 some some DET ajst-12328 74 34 directions direction NOUN ajst-12328 74 35 that that PRON ajst-12328 74 36 can can AUX ajst-12328 74 37 be be AUX ajst-12328 74 38 improved improve VERB ajst-12328 74 39 and and CCONJ ajst-12328 74 40 further far ADV ajst-12328 74 41 studied study VERB ajst-12328 74 42 : : PUNCT ajst-12328 74 43 1 1 X ajst-12328 74 44 . . PUNCT ajst-12328 74 45 algorithm algorithm NOUN ajst-12328 74 46 optimization optimization NOUN ajst-12328 74 47 : : PUNCT ajst-12328 74 48 further far ADV ajst-12328 74 49 optimize optimize VERB ajst-12328 74 50 the the DET ajst-12328 74 51 parameter parameter NOUN ajst-12328 74 52 selection selection NOUN ajst-12328 74 53 and and CCONJ ajst-12328 74 54 cluster cluster NOUN ajst-12328 74 55 center center NOUN ajst-12328 74 56 initialization initialization NOUN ajst-12328 74 57 method method NOUN ajst-12328 74 58 of of ADP ajst-12328 74 59 k k NOUN ajst-12328 74 60 - - PUNCT ajst-12328 74 61 means mean VERB ajst-12328 74 62 clustering clustering ADJ ajst-12328 74 63 algorithm algorithm NOUN ajst-12328 74 64 . . PUNCT ajst-12328 75 1 at at ADP ajst-12328 75 2 the the DET ajst-12328 75 3 same same ADJ ajst-12328 75 4 time time NOUN ajst-12328 75 5 , , PUNCT ajst-12328 75 6 combined combine VERB ajst-12328 75 7 with with ADP ajst-12328 75 8 other other ADJ ajst-12328 75 9 clustering clustering ADJ ajst-12328 75 10 algorithms algorithm NOUN ajst-12328 75 11 or or CCONJ ajst-12328 75 12 feature feature NOUN ajst-12328 75 13 extraction extraction NOUN ajst-12328 75 14 methods method NOUN ajst-12328 75 15 , , PUNCT ajst-12328 75 16 the the DET ajst-12328 75 17 accuracy accuracy NOUN ajst-12328 75 18 and and CCONJ ajst-12328 75 19 robustness robustness NOUN ajst-12328 75 20 of of ADP ajst-12328 75 21 the the DET ajst-12328 75 22 algorithm algorithm NOUN ajst-12328 75 23 are be AUX ajst-12328 75 24 further far ADV ajst-12328 75 25 improved improve VERB ajst-12328 75 26 . . PUNCT ajst-12328 76 1 2 2 X ajst-12328 76 2 . . X ajst-12328 76 3 multi multi ADJ ajst-12328 76 4 - - ADJ ajst-12328 76 5 scale scale ADJ ajst-12328 76 6 segmentation segmentation NOUN ajst-12328 76 7 : : PUNCT ajst-12328 76 8 considering consider VERB ajst-12328 76 9 the the DET ajst-12328 76 10 problem problem NOUN ajst-12328 76 11 of of ADP ajst-12328 76 12 multiple multiple ADJ ajst-12328 76 13 scales scale NOUN ajst-12328 76 14 and and CCONJ ajst-12328 76 15 resolutions resolution NOUN ajst-12328 76 16 in in ADP ajst-12328 76 17 metal metal NOUN ajst-12328 76 18 abrasive abrasive ADJ ajst-12328 76 19 images image NOUN ajst-12328 76 20 , , PUNCT ajst-12328 76 21 you you PRON ajst-12328 76 22 can can AUX ajst-12328 76 23 try try VERB ajst-12328 76 24 to to PART ajst-12328 76 25 design design VERB ajst-12328 76 26 a a DET ajst-12328 76 27 multi multi ADJ ajst-12328 76 28 - - ADJ ajst-12328 76 29 scale scale ADJ ajst-12328 76 30 segmentation segmentation NOUN ajst-12328 76 31 algorithm algorithm NOUN ajst-12328 76 32 to to PART ajst-12328 76 33 process process VERB ajst-12328 76 34 metal metal NOUN ajst-12328 76 35 abrasive abrasive ADJ ajst-12328 76 36 grains grain NOUN ajst-12328 76 37 of of ADP ajst-12328 76 38 different different ADJ ajst-12328 76 39 scales scale NOUN ajst-12328 76 40 . . PUNCT ajst-12328 77 1 3 3 X ajst-12328 77 2 . . X ajst-12328 77 3 dataset dataset ADJ ajst-12328 77 4 expansion expansion NOUN ajst-12328 77 5 : : PUNCT ajst-12328 77 6 the the DET ajst-12328 77 7 algorithm algorithm NOUN ajst-12328 77 8 in in ADP ajst-12328 77 9 this this DET ajst-12328 77 10 study study NOUN ajst-12328 77 11 is be AUX ajst-12328 77 12 mainly mainly ADV ajst-12328 77 13 based base VERB ajst-12328 77 14 on on ADP ajst-12328 77 15 the the DET ajst-12328 77 16 specific specific ADJ ajst-12328 77 17 metal metal NOUN ajst-12328 77 18 abrasive abrasive ADJ ajst-12328 77 19 grain grain NOUN ajst-12328 77 20 dataset dataset NOUN ajst-12328 77 21 , , PUNCT ajst-12328 77 22 which which PRON ajst-12328 77 23 can can AUX ajst-12328 77 24 further far ADV ajst-12328 77 25 expand expand VERB ajst-12328 77 26 and and CCONJ ajst-12328 77 27 enrich enrich VERB ajst-12328 77 28 the the DET ajst-12328 77 29 dataset dataset NOUN ajst-12328 77 30 , , PUNCT ajst-12328 77 31 especially especially ADV ajst-12328 77 32 the the DET ajst-12328 77 33 metal metal NOUN ajst-12328 77 34 abrasive abrasive ADJ ajst-12328 77 35 grain grain NOUN ajst-12328 77 36 samples sample NOUN ajst-12328 77 37 containing contain VERB ajst-12328 77 38 more more ADJ ajst-12328 77 39 shape shape NOUN ajst-12328 77 40 and and CCONJ ajst-12328 77 41 color color NOUN ajst-12328 77 42 changes change NOUN ajst-12328 77 43 , , PUNCT ajst-12328 77 44 so so SCONJ ajst-12328 77 45 as as SCONJ ajst-12328 77 46 to to PART ajst-12328 77 47 increase increase VERB ajst-12328 77 48 the the DET ajst-12328 77 49 generalization generalization NOUN ajst-12328 77 50 ability ability NOUN ajst-12328 77 51 of of ADP ajst-12328 77 52 the the DET ajst-12328 77 53 algorithm algorithm NOUN ajst-12328 77 54 . . PUNCT ajst-12328 78 1 4 4 X ajst-12328 78 2 . . X ajst-12328 78 3 deep deep ADJ ajst-12328 78 4 learning learning NOUN ajst-12328 78 5 methods method NOUN ajst-12328 78 6 : : PUNCT ajst-12328 78 7 you you PRON ajst-12328 78 8 can can AUX ajst-12328 78 9 try try VERB ajst-12328 78 10 to to PART ajst-12328 78 11 apply apply VERB ajst-12328 78 12 deep deep ADJ ajst-12328 78 13 learning learning NOUN ajst-12328 78 14 methods method NOUN ajst-12328 78 15 , , PUNCT ajst-12328 78 16 such such ADJ ajst-12328 78 17 as as ADP ajst-12328 78 18 convolutional convolutional ADJ ajst-12328 78 19 neural neural ADJ ajst-12328 78 20 networks network NOUN ajst-12328 78 21 ( ( PUNCT ajst-12328 78 22 cnns cnns PROPN ajst-12328 78 23 ) ) PUNCT ajst-12328 78 24 , , PUNCT ajst-12328 78 25 to to PART ajst-12328 78 26 process process VERB ajst-12328 78 27 the the DET ajst-12328 78 28 segmentation segmentation NOUN ajst-12328 78 29 task task NOUN ajst-12328 78 30 of of ADP ajst-12328 78 31 metal metal NOUN ajst-12328 78 32 abrasive abrasive ADJ ajst-12328 78 33 images image NOUN ajst-12328 78 34 , , PUNCT ajst-12328 78 35 so so SCONJ ajst-12328 78 36 as as SCONJ ajst-12328 78 37 to to PART ajst-12328 78 38 further far ADV ajst-12328 78 39 improve improve VERB ajst-12328 78 40 the the DET ajst-12328 78 41 segmentation segmentation NOUN ajst-12328 78 42 accuracy accuracy NOUN ajst-12328 78 43 and and CCONJ ajst-12328 78 44 the the DET ajst-12328 78 45 ability ability NOUN ajst-12328 78 46 to to PART ajst-12328 78 47 process process VERB ajst-12328 78 48 complex complex ADJ ajst-12328 78 49 scenes scene NOUN ajst-12328 78 50 . . PUNCT ajst-12328 79 1 5 5 X ajst-12328 79 2 . . X ajst-12328 79 3 application application NOUN ajst-12328 79 4 expansion expansion NOUN ajst-12328 79 5 : : PUNCT ajst-12328 79 6 in in ADP ajst-12328 79 7 addition addition NOUN ajst-12328 79 8 to to ADP ajst-12328 79 9 metal metal NOUN ajst-12328 79 10 abrasive abrasive ADJ ajst-12328 79 11 image image NOUN ajst-12328 79 12 segmentation segmentation NOUN ajst-12328 79 13 , , PUNCT ajst-12328 79 14 the the DET ajst-12328 79 15 algorithm algorithm NOUN ajst-12328 79 16 proposed propose VERB ajst-12328 79 17 in in ADP ajst-12328 79 18 this this DET ajst-12328 79 19 paper paper NOUN ajst-12328 79 20 can can AUX ajst-12328 79 21 also also ADV ajst-12328 79 22 be be AUX ajst-12328 79 23 applied apply VERB ajst-12328 79 24 and and CCONJ ajst-12328 79 25 extended extend VERB ajst-12328 79 26 in in ADP ajst-12328 79 27 other other ADJ ajst-12328 79 28 image image NOUN ajst-12328 79 29 segmentation segmentation NOUN ajst-12328 79 30 tasks task NOUN ajst-12328 79 31 , , PUNCT ajst-12328 79 32 such such ADJ ajst-12328 79 33 as as ADP ajst-12328 79 34 medical medical ADJ ajst-12328 79 35 image image NOUN ajst-12328 79 36 segmentation segmentation NOUN ajst-12328 79 37 and and CCONJ ajst-12328 79 38 natural natural ADJ ajst-12328 79 39 image image NOUN ajst-12328 79 40 segmentation segmentation NOUN ajst-12328 79 41 . . PUNCT ajst-12328 80 1 through through ADP ajst-12328 80 2 further further ADJ ajst-12328 80 3 research research NOUN ajst-12328 80 4 and and CCONJ ajst-12328 80 5 improvement improvement NOUN ajst-12328 80 6 , , PUNCT ajst-12328 80 7 the the DET ajst-12328 80 8 effect effect NOUN ajst-12328 80 9 of of ADP ajst-12328 80 10 the the DET ajst-12328 80 11 metal metal NOUN ajst-12328 80 12 abrasive abrasive ADJ ajst-12328 80 13 image image NOUN ajst-12328 80 14 segmentation segmentation NOUN ajst-12328 80 15 algorithm algorithm NOUN ajst-12328 80 16 can can AUX ajst-12328 80 17 be be AUX ajst-12328 80 18 improved improve VERB ajst-12328 80 19 , , PUNCT ajst-12328 80 20 its its PRON ajst-12328 80 21 application application NOUN ajst-12328 80 22 field field NOUN ajst-12328 80 23 can can AUX ajst-12328 80 24 be be AUX ajst-12328 80 25 expanded expand VERB ajst-12328 80 26 , , PUNCT ajst-12328 80 27 and and CCONJ ajst-12328 80 28 better well ADJ ajst-12328 80 29 solutions solution NOUN ajst-12328 80 30 can can AUX ajst-12328 80 31 be be AUX ajst-12328 80 32 provided provide VERB ajst-12328 80 33 for for ADP ajst-12328 80 34 practical practical ADJ ajst-12328 80 35 applications application NOUN ajst-12328 80 36 . . PUNCT ajst-12328 81 1 references reference NOUN ajst-12328 81 2 [ [ X ajst-12328 81 3 1 1 NUM ajst-12328 81 4 ] ] PUNCT ajst-12328 81 5 chen chen PROPN ajst-12328 81 6 g g PROPN ajst-12328 81 7 , , PUNCT ajst-12328 81 8 zuo zuo PROPN ajst-12328 81 9 h h PROPN ajst-12328 81 10 f.object f.object ADJ ajst-12328 81 11 extraction extraction NOUN ajst-12328 81 12 of of ADP ajst-12328 81 13 color color NOUN ajst-12328 81 14 microscope microscope NOUN ajst-12328 81 15 debris debris NOUN ajst-12328 81 16 images image NOUN ajst-12328 81 17 based base VERB ajst-12328 81 18 on on ADP ajst-12328 81 19 fuzzy fuzzy ADJ ajst-12328 81 20 cluster cluster NOUN ajst-12328 81 21 analysis analysis NOUN ajst-12328 82 1 [ [ X ajst-12328 82 2 j].journal j].journal NOUN ajst-12328 82 3 of of ADP ajst-12328 82 4 data datum NOUN ajst-12328 82 5 acquisition acquisition NOUN ajst-12328 82 6 οΌ† οΌ† NOUN ajst-12328 82 7 processing processing NOUN ajst-12328 82 8 , , PUNCT ajst-12328 82 9 2001 2001 NUM ajst-12328 82 10 , , PUNCT ajst-12328 82 11 16 16 NUM ajst-12328 82 12 ( ( PUNCT ajst-12328 82 13 3 3 NUM ajst-12328 82 14 ) ) PUNCT ajst-12328 82 15 : : PUNCT ajst-12328 82 16 286－290 286－290 X ajst-12328 82 17 . . PUNCT ajst-12328 83 1 [ [ X ajst-12328 83 2 2 2 NUM ajst-12328 83 3 ] ] PUNCT ajst-12328 83 4 xu xu PROPN ajst-12328 83 5 y y PROPN ajst-12328 83 6 f.image f.image NOUN ajst-12328 83 7 segmentation segmentation NOUN ajst-12328 83 8 based base VERB ajst-12328 83 9 on on ADP ajst-12328 83 10 the the DET ajst-12328 83 11 genetic genetic ADJ ajst-12328 83 12 fuzzy fuzzy ADJ ajst-12328 83 13 c－ c－ NOUN ajst-12328 83 14 mean mean NOUN ajst-12328 83 15 algorithm algorithm NOUN ajst-12328 84 1 [ [ X ajst-12328 84 2 j].journal j].journal NOUN ajst-12328 84 3 of of ADP ajst-12328 84 4 northwestern northwestern PROPN ajst-12328 84 5 polytechnical polytechnical PROPN ajst-12328 84 6 university university NOUN ajst-12328 84 7 , , PUNCT ajst-12328 84 8 2002 2002 NUM ajst-12328 84 9 , , PUNCT ajst-12328 84 10 20 20 NUM ajst-12328 84 11 ( ( PUNCT ajst-12328 84 12 4 4 NUM ajst-12328 84 13 ) ) PUNCT ajst-12328 84 14 : : PUNCT ajst-12328 85 1 549－553 549－553 X ajst-12328 85 2 . . PUNCT ajst-12328 86 1 [ [ X ajst-12328 86 2 3 3 X ajst-12328 86 3 ] ] SYM ajst-12328 86 4 li li PROPN ajst-12328 86 5 y y PROPN ajst-12328 86 6 j j PROPN ajst-12328 86 7 , , PUNCT ajst-12328 86 8 zuo zuo PROPN ajst-12328 86 9 h h PROPN ajst-12328 86 10 f f PROPN ajst-12328 86 11 , , PUNCT ajst-12328 86 12 chen chen PROPN ajst-12328 86 13 g g PROPN ajst-12328 86 14 , , PUNCT ajst-12328 86 15 et et PROPN ajst-12328 86 16 al.genetic al.genetic ADV ajst-12328 86 17 fcm fcm VERB ajst-12328 86 18 algorithm algorithm NOUN ajst-12328 86 19 for for SCONJ ajst-12328 86 20 wear wear VERB ajst-12328 86 21 particle particle NOUN ajst-12328 86 22 image image NOUN ajst-12328 86 23 objective objective NOUN ajst-12328 86 24 extracting extract VERB ajst-12328 86 25 [ [ PRON ajst-12328 86 26 j].pattern j].pattern ADJ ajst-12328 86 27 ο½’ ο½’ NOUN ajst-12328 86 28 ecognition ecognition NOUN ajst-12328 86 29 and and CCONJ ajst-12328 86 30 artificial artificial ADJ ajst-12328 86 31 intelligence intelligence NOUN ajst-12328 86 32 , , PUNCT ajst-12328 86 33 2003 2003 NUM ajst-12328 86 34 , , PUNCT ajst-12328 86 35 16 16 NUM ajst-12328 86 36 ( ( PUNCT ajst-12328 86 37 3 3 NUM ajst-12328 86 38 ) ) PUNCT ajst-12328 86 39 : : PUNCT ajst-12328 87 1 379－383 379－383 X ajst-12328 87 2 . . PUNCT ajst-12328 88 1 [ [ X ajst-12328 88 2 4 4 NUM ajst-12328 88 3 ] ] X ajst-12328 88 4 guo guo PROPN ajst-12328 88 5 h h NOUN ajst-12328 88 6 g g PROPN ajst-12328 88 7 , , PUNCT ajst-12328 88 8 qu qu PROPN ajst-12328 88 9 j j PROPN ajst-12328 88 10 , , PUNCT ajst-12328 88 11 wang wang PROPN ajst-12328 88 12 x x PROPN ajst-12328 88 13 h. h. NOUN ajst-12328 88 14 wear wear VERB ajst-12328 88 15 particle particle NOUN ajst-12328 88 16 color color NOUN ajst-12328 88 17 image image NOUN ajst-12328 88 18 segmentation segmentation NOUN ajst-12328 88 19 based base VERB ajst-12328 88 20 on on ADP ajst-12328 88 21 color color NOUN ajst-12328 88 22 feature feature NOUN ajst-12328 88 23 and and CCONJ ajst-12328 88 24 texture texture ADJ ajst-12328 88 25 feature feature NOUN ajst-12328 88 26 [ [ X ajst-12328 88 27 j].lubrication j].lubrication NOUN ajst-12328 88 28 engineering engineering NOUN ajst-12328 88 29 , , PUNCT ajst-12328 88 30 2013 2013 NUM ajst-12328 88 31 , , PUNCT ajst-12328 88 32 38 38 NUM ajst-12328 88 33 ( ( PUNCT ajst-12328 88 34 6 6 NUM ajst-12328 88 35 ) ) PUNCT ajst-12328 88 36 : : PUNCT ajst-12328 89 1 94－97 94－97 X ajst-12328 89 2 . . PUNCT ajst-12328 90 1 [ [ X ajst-12328 90 2 5 5 NUM ajst-12328 90 3 ] ] PUNCT ajst-12328 90 4 zhang zhang PROPN ajst-12328 90 5 l.application l.application PROPN ajst-12328 90 6 research research NOUN ajst-12328 90 7 of of ADP ajst-12328 90 8 ant ant ADJ ajst-12328 90 9 colony colony NOUN ajst-12328 90 10 algorithm algorithm NOUN ajst-12328 90 11 in in ADP ajst-12328 90 12 ferrography ferrography NOUN ajst-12328 90 13 image image NOUN ajst-12328 90 14 processing processing NOUN ajst-12328 90 15 [ [ X ajst-12328 90 16 d].nanjing d].nanje VERB ajst-12328 90 17 : : PUNCT ajst-12328 90 18 nanjing nanjing PROPN ajst-12328 90 19 university university PROPN ajst-12328 90 20 of of ADP ajst-12328 90 21 aeronautics aeronautics PROPN ajst-12328 90 22 and and CCONJ ajst-12328 90 23 astronautics astronautic NOUN ajst-12328 90 24 , , PUNCT ajst-12328 90 25 2013 2013 NUM ajst-12328 90 26 . . PUNCT ajst-12328 91 1 [ [ X ajst-12328 91 2 6 6 NUM ajst-12328 91 3 ] ] PUNCT ajst-12328 91 4 qiu qiu PROPN ajst-12328 91 5 l l PROPN ajst-12328 91 6 j j PROPN ajst-12328 91 7 , , PUNCT ajst-12328 91 8 xuan xuan PROPN ajst-12328 91 9 z z PROPN ajst-12328 91 10 n n PROPN ajst-12328 91 11 , , PUNCT ajst-12328 91 12 zhang zhang PROPN ajst-12328 91 13 x x PUNCT ajst-12328 91 14 f.debris f.debris NOUN ajst-12328 91 15 color color NOUN ajst-12328 91 16 image image NOUN ajst-12328 91 17 segmentaion segmentaion NOUN ajst-12328 91 18 by by ADP ajst-12328 91 19 k k PROPN ajst-12328 91 20 － － PROPN ajst-12328 91 21 means mean VERB ajst-12328 91 22 clustering cluster VERB ajst-12328 91 23 and and CCONJ ajst-12328 91 24 ostu ostu NOUN ajst-12328 91 25 method method NOUN ajst-12328 91 26 [ [ X ajst-12328 91 27 j].lubrication j].lubrication NOUN ajst-12328 91 28 engineering engineering NOUN ajst-12328 91 29 , , PUNCT ajst-12328 91 30 2014 2014 NUM ajst-12328 91 31 , , PUNCT ajst-12328 91 32 39 39 NUM ajst-12328 91 33 ( ( PUNCT ajst-12328 91 34 12 12 NUM ajst-12328 91 35 ) ) PUNCT ajst-12328 91 36 : : PUNCT ajst-12328 92 1 101－104 101－104 X ajst-12328 92 2 . . PUNCT ajst-12328 93 1 [ [ X ajst-12328 93 2 7 7 X ajst-12328 93 3 ] ] X ajst-12328 93 4 maiti maiti NOUN ajst-12328 93 5 abhik abhik PROPN ajst-12328 93 6 , , PUNCT ajst-12328 93 7 choudhary choudhary PROPN ajst-12328 93 8 amrit amrit PROPN ajst-12328 93 9 , , PUNCT ajst-12328 93 10 chakravarty chakravarty NOUN ajst-12328 93 11 debashish debashish PROPN ajst-12328 93 12 . . PUNCT ajst-12328 94 1 a a DET ajst-12328 94 2 kmeans kmean NOUN ajst-12328 94 3 clustering cluster VERB ajst-12328 94 4 – – PUNCT ajst-12328 94 5 based base VERB ajst-12328 94 6 approach approach NOUN ajst-12328 94 7 for for ADP ajst-12328 94 8 3d 3d PROPN ajst-12328 94 9 mapping mapping NOUN ajst-12328 94 10 and and CCONJ ajst-12328 94 11 characterization characterization NOUN ajst-12328 94 12 of of ADP ajst-12328 94 13 rock rock NOUN ajst-12328 94 14 faces face NOUN ajst-12328 94 15 using use VERB ajst-12328 94 16 digital digital PROPN ajst-12328 94 17 images[j images[j PROPN ajst-12328 94 18 ] ] PUNCT ajst-12328 94 19 . . PUNCT ajst-12328 95 1 arabian arabian ADJ ajst-12328 95 2 journal journal PROPN ajst-12328 95 3 of of ADP ajst-12328 95 4 geosciences,2021,14(10 geosciences,2021,14(10 PROPN ajst-12328 95 5 ) ) PUNCT ajst-12328 95 6 . . PUNCT ajst-12328 96 1 [ [ X ajst-12328 96 2 8 8 NUM ajst-12328 96 3 ] ] X ajst-12328 96 4 wang wang PROPN ajst-12328 96 5 xinsai xinsai PROPN ajst-12328 96 6 , , PUNCT ajst-12328 96 7 feng feng PROPN ajst-12328 96 8 weiqiang weiqiang PROPN ajst-12328 96 9 , , PUNCT ajst-12328 96 10 feng feng PROPN ajst-12328 96 11 xiaoer xiaoer PROPN ajst-12328 96 12 . . PUNCT ajst-12328 97 1 research research NOUN ajst-12328 97 2 on on ADP ajst-12328 97 3 image image NOUN ajst-12328 97 4 fusion fusion NOUN ajst-12328 97 5 algorithm algorithm NOUN ajst-12328 97 6 based base VERB ajst-12328 97 7 on on ADP ajst-12328 97 8 k k PROPN ajst-12328 97 9 - - PUNCT ajst-12328 97 10 means means NOUN ajst-12328 97 11 clustering clustering NOUN ajst-12328 97 12 and and CCONJ ajst-12328 97 13 nsct[j nsct[j PRON ajst-12328 97 14 ] ] PUNCT ajst-12328 97 15 . . PUNCT ajst-12328 98 1 seventh seventh ADJ ajst-12328 98 2 symposium symposium NOUN ajst-12328 98 3 on on ADP ajst-12328 98 4 novel novel ADJ ajst-12328 98 5 photoelectronic photoelectronic ADJ ajst-12328 98 6 detection detection NOUN ajst-12328 98 7 technology technology NOUN ajst-12328 98 8 and and CCONJ ajst-12328 98 9 applications,2021,11763 applications,2021,11763 NOUN ajst-12328 98 10 . . PUNCT ajst-12328 99 1 [ [ X ajst-12328 99 2 9 9 NUM ajst-12328 99 3 ] ] PUNCT ajst-12328 99 4 yuchechen yuchechen NOUN ajst-12328 99 5 adriΓ‘n adriΓ‘n PROPN ajst-12328 99 6 e. e. PROPN ajst-12328 99 7 , , PUNCT ajst-12328 99 8 lakkis lakkis PROPN ajst-12328 99 9 s. s. PROPN ajst-12328 99 10 gabriela gabriela PROPN ajst-12328 99 11 , , PUNCT ajst-12328 99 12 caferri caferri NOUN ajst-12328 99 13 agustΓ­n agustΓ­n NOUN ajst-12328 99 14 , , PUNCT ajst-12328 99 15 canziani canziani PROPN ajst-12328 99 16 pablo pablo PROPN ajst-12328 99 17 o. o. PROPN ajst-12328 99 18 , , PUNCT ajst-12328 99 19 muszkats muszkat NOUN ajst-12328 99 20 juan juan PROPN ajst-12328 99 21 pablo pablo PROPN ajst-12328 99 22 . . PUNCT ajst-12328 100 1 a a DET ajst-12328 100 2 cluster cluster NOUN ajst-12328 100 3 approach approach NOUN ajst-12328 100 4 to to ADP ajst-12328 100 5 cloud cloud NOUN ajst-12328 100 6 cover cover NOUN ajst-12328 100 7 classification classification NOUN ajst-12328 100 8 over over ADP ajst-12328 100 9 south south PROPN ajst-12328 100 10 america america PROPN ajst-12328 100 11 and and CCONJ ajst-12328 100 12 adjacent adjacent ADJ ajst-12328 100 13 oceans ocean NOUN ajst-12328 100 14 using use VERB ajst-12328 100 15 a a DET ajst-12328 100 16 k k NOUN ajst-12328 100 17 - - PUNCT ajst-12328 100 18 means mean NOUN ajst-12328 100 19 / / SYM ajst-12328 100 20 k k ADJ ajst-12328 100 21 - - ADJ ajst-12328 100 22 means++ means++ ADV ajst-12328 100 23 unsupervised unsupervised ADJ ajst-12328 100 24 algorithm algorithm NOUN ajst-12328 100 25 on on ADP ajst-12328 100 26 goes go VERB ajst-12328 100 27 ir ir PROPN ajst-12328 100 28 imagery[j imagery[j PROPN ajst-12328 100 29 ] ] PUNCT ajst-12328 100 30 . . PUNCT ajst-12328 101 1 remote remote ADJ ajst-12328 101 2 sensing sensing NOUN ajst-12328 101 3 , , PUNCT ajst-12328 101 4 2020 2020 NUM ajst-12328 101 5 , , PUNCT ajst-12328 101 6 12(18 12(18 NUM ajst-12328 101 7 ) ) PUNCT ajst-12328 101 8 . . PUNCT ajst-12328 102 1 [ [ X ajst-12328 102 2 10 10 NUM ajst-12328 102 3 ] ] X ajst-12328 102 4 sensor sensor NOUN ajst-12328 102 5 research research NOUN ajst-12328 102 6 ; ; PUNCT ajst-12328 102 7 studies study NOUN ajst-12328 102 8 from from ADP ajst-12328 102 9 beijing beijing PROPN ajst-12328 102 10 university university PROPN ajst-12328 102 11 of of ADP ajst-12328 102 12 chemical chemical PROPN ajst-12328 102 13 technology technology NOUN ajst-12328 102 14 in in ADP ajst-12328 102 15 the the DET ajst-12328 102 16 area area NOUN ajst-12328 102 17 of of ADP ajst-12328 102 18 sensor sensor NOUN ajst-12328 102 19 research research NOUN ajst-12328 102 20 described describe VERB ajst-12328 102 21 ( ( PUNCT ajst-12328 102 22 research research NOUN ajst-12328 102 23 on on ADP ajst-12328 102 24 tire tire NOUN ajst-12328 102 25 marking mark VERB ajst-12328 102 26 point point NOUN ajst-12328 102 27 completeness completeness NOUN ajst-12328 102 28 evaluation evaluation NOUN ajst-12328 102 29 based base VERB ajst-12328 102 30 on on ADP ajst-12328 102 31 k k PROPN ajst-12328 102 32 - - PUNCT ajst-12328 102 33 means mean VERB ajst-12328 102 34 clustering cluster VERB ajst-12328 102 35 image image NOUN ajst-12328 102 36 segmentation)[j segmentation)[j PROPN ajst-12328 102 37 ] ] PUNCT ajst-12328 102 38 . . PUNCT ajst-12328 103 1 journal journal PROPN ajst-12328 103 2 of of ADP ajst-12328 103 3 technology,2020 technology,2020 PROPN ajst-12328 103 4 . . PUNCT ajst-12328 104 1 [ [ X ajst-12328 104 2 11 11 NUM ajst-12328 104 3 ] ] X ajst-12328 104 4 multi multi ADJ ajst-12328 104 5 - - ADJ ajst-12328 104 6 spectral spectral ADJ ajst-12328 104 7 image image NOUN ajst-12328 104 8 segmentation segmentation NOUN ajst-12328 104 9 based base VERB ajst-12328 104 10 on on ADP ajst-12328 104 11 the the DET ajst-12328 104 12 k k NOUN ajst-12328 104 13 - - PUNCT ajst-12328 104 14 means means NOUN ajst-12328 104 15 clustering[j clustering[j NOUN ajst-12328 104 16 ] ] PUNCT ajst-12328 104 17 . . PUNCT ajst-12328 105 1 international international ADJ ajst-12328 105 2 journal journal PROPN ajst-12328 105 3 of of ADP ajst-12328 105 4 innovative innovative ADJ ajst-12328 105 5 technology technology NOUN ajst-12328 105 6 and and CCONJ ajst-12328 105 7 exploring explore VERB ajst-12328 105 8 engineering,2019,9(2 engineering,2019,9(2 NOUN ajst-12328 105 9 ) ) PUNCT ajst-12328 105 10 . . PUNCT ajst-12328 106 1 [ [ X ajst-12328 106 2 12 12 NUM ajst-12328 106 3 ] ] PUNCT ajst-12328 106 4 yanhong yanhong PROPN ajst-12328 106 5 wang wang PROPN ajst-12328 106 6 , , PUNCT ajst-12328 106 7 denghui denghui PROPN ajst-12328 106 8 li li PROPN ajst-12328 106 9 , , PUNCT ajst-12328 106 10 yuying yuye VERB ajst-12328 106 11 wang wang PROPN ajst-12328 106 12 . . PUNCT ajst-12328 107 1 realization realization NOUN ajst-12328 107 2 of of ADP ajst-12328 107 3 remote remote ADJ ajst-12328 107 4 sensing sense VERB ajst-12328 107 5 image image NOUN ajst-12328 107 6 segmentation segmentation NOUN ajst-12328 107 7 based base VERB ajst-12328 107 8 on on ADP ajst-12328 107 9 k k PROPN ajst-12328 107 10 - - PUNCT ajst-12328 107 11 means means NOUN ajst-12328 107 12 clustering[j clustering[j NOUN ajst-12328 107 13 ] ] PUNCT ajst-12328 107 14 . . PUNCT ajst-12328 108 1 iop iop PROPN ajst-12328 108 2 conference conference PROPN ajst-12328 108 3 series series PROPN ajst-12328 108 4 : : PUNCT ajst-12328 108 5 materials material NOUN ajst-12328 108 6 science science NOUN ajst-12328 108 7 and and CCONJ ajst-12328 108 8 engineering,2019,490(7 engineering,2019,490(7 NOUN ajst-12328 108 9 ) ) PUNCT ajst-12328 108 10 . . PUNCT ajst-12328 109 1 231 231 NUM ajst-12328 110 1 [ [ SYM ajst-12328 110 2 13 13 NUM ajst-12328 110 3 ] ] PUNCT ajst-12328 110 4 angky angky PROPN ajst-12328 110 5 fajriati fajriati PROPN ajst-12328 110 6 ms ms PROPN ajst-12328 110 7 musa musa PROPN ajst-12328 110 8 , , PUNCT ajst-12328 110 9 adiwijaya adiwijaya NOUN ajst-12328 110 10 , , PUNCT ajst-12328 110 11 dody dody NOUN ajst-12328 110 12 qori qori NOUN ajst-12328 110 13 utama utama PROPN ajst-12328 110 14 . . PUNCT ajst-12328 111 1 segmentation segmentation NOUN ajst-12328 111 2 image image NOUN ajst-12328 111 3 re re VERB ajst-12328 111 4 - - NOUN ajst-12328 111 5 coloring color VERB ajst-12328 111 6 based base VERB ajst-12328 111 7 on on ADP ajst-12328 111 8 k k PROPN ajst-12328 111 9 - - PUNCT ajst-12328 111 10 means mean VERB ajst-12328 111 11 clustering cluster VERB ajst-12328 111 12 algorithm algorithm NOUN ajst-12328 111 13 as as ADP ajst-12328 111 14 a a DET ajst-12328 111 15 tool tool NOUN ajst-12328 111 16 for for ADP ajst-12328 111 17 partial partial ADJ ajst-12328 111 18 color color NOUN ajst-12328 111 19 - - PUNCT ajst-12328 111 20 blind blind ADJ ajst-12328 111 21 people[j people[j NOUN ajst-12328 111 22 ] ] PUNCT ajst-12328 111 23 . . PUNCT ajst-12328 112 1 journal journal PROPN ajst-12328 112 2 of of ADP ajst-12328 112 3 physics physics PROPN ajst-12328 112 4 : : PUNCT ajst-12328 112 5 conference conference NOUN ajst-12328 112 6 series,2019,1192(1 series,2019,1192(1 NOUN ajst-12328 112 7 ) ) PUNCT ajst-12328 112 8 . . PUNCT ajst-12328 113 1 [ [ X ajst-12328 113 2 14 14 NUM ajst-12328 113 3 ] ] X ajst-12328 113 4 juli juli PROPN ajst-12328 113 5 rejito rejito PROPN ajst-12328 113 6 , , PUNCT ajst-12328 113 7 atje atje PROPN ajst-12328 113 8 setiawan setiawan PROPN ajst-12328 113 9 abdullahi abdullahi PROPN ajst-12328 113 10 , , PUNCT ajst-12328 113 11 akmal akmal PROPN ajst-12328 113 12 , , PUNCT ajst-12328 113 13 deni deni PROPN ajst-12328 113 14 setiana setiana PROPN ajst-12328 113 15 , , PUNCT ajst-12328 113 16 budi budi PROPN ajst-12328 113 17 nurani nurani PROPN ajst-12328 113 18 ruchjana ruchjana PROPN ajst-12328 113 19 . . PUNCT ajst-12328 114 1 image image NOUN ajst-12328 114 2 indexing indexing NOUN ajst-12328 114 3 using use VERB ajst-12328 114 4 color color NOUN ajst-12328 114 5 histogram histogram NOUN ajst-12328 114 6 and and CCONJ ajst-12328 114 7 kmeans kmean NOUN ajst-12328 114 8 clustering cluster VERB ajst-12328 114 9 for for ADP ajst-12328 114 10 optimization optimization NOUN ajst-12328 114 11 cbir cbir NOUN ajst-12328 114 12 in in ADP ajst-12328 114 13 image image NOUN ajst-12328 114 14 database[j database[j NOUN ajst-12328 114 15 ] ] PUNCT ajst-12328 114 16 . . PUNCT ajst-12328 115 1 journal journal PROPN ajst-12328 115 2 of of ADP ajst-12328 115 3 physics physics PROPN ajst-12328 115 4 : : PUNCT ajst-12328 115 5 conference conference NOUN ajst-12328 115 6 series,2017,893(1 series,2017,893(1 NOUN ajst-12328 115 7 ) ) PUNCT ajst-12328 115 8 . . PUNCT ajst-12328 116 1 [ [ X ajst-12328 116 2 15 15 NUM ajst-12328 116 3 ] ] PUNCT ajst-12328 116 4 senthan senthan NOUN ajst-12328 116 5 mathavan mathavan NOUN ajst-12328 116 6 , , PUNCT ajst-12328 116 7 akash akash PROPN ajst-12328 116 8 kumar kumar PROPN ajst-12328 116 9 , , PUNCT ajst-12328 116 10 khurram khurram PROPN ajst-12328 116 11 kamal kamal PROPN ajst-12328 116 12 , , PUNCT ajst-12328 116 13 michael michael PROPN ajst-12328 116 14 nieminen nieminen PROPN ajst-12328 116 15 , , PUNCT ajst-12328 116 16 hitesh hitesh PROPN ajst-12328 116 17 shah shah NOUN ajst-12328 116 18 , , PUNCT ajst-12328 116 19 mujib mujib PROPN ajst-12328 116 20 rahman rahman PROPN ajst-12328 116 21 . . PUNCT ajst-12328 117 1 fast fast ADJ ajst-12328 117 2 segmentation segmentation NOUN ajst-12328 117 3 of of ADP ajst-12328 117 4 industrial industrial ADJ ajst-12328 117 5 quality quality NOUN ajst-12328 117 6 pavement pavement NOUN ajst-12328 117 7 images image NOUN ajst-12328 117 8 using use VERB ajst-12328 117 9 laws law NOUN ajst-12328 117 10 texture texture ADJ ajst-12328 117 11 energy energy NOUN ajst-12328 117 12 measures measure NOUN ajst-12328 117 13 and and CCONJ ajst-12328 117 14 k k PROPN ajst-12328 117 15 -means -mean NOUN ajst-12328 117 16 clustering[j clustering[j NOUN ajst-12328 117 17 ] ] PUNCT ajst-12328 117 18 . . PUNCT ajst-12328 118 1 journal journal PROPN ajst-12328 118 2 of of ADP ajst-12328 118 3 electronic electronic ADJ ajst-12328 118 4 imaging,2016,25(5 imaging,2016,25(5 NOUN ajst-12328 118 5 ) ) PUNCT ajst-12328 118 6 . . PUNCT ajst-12328 119 1 [ [ X ajst-12328 119 2 16 16 NUM ajst-12328 119 3 ] ] X ajst-12328 119 4 somasundaram somasundaram PROPN ajst-12328 119 5 k. k. PROPN ajst-12328 119 6 ,mary ,mary PUNCT ajst-12328 119 7 shanthi shanthi PROPN ajst-12328 119 8 rani rani PROPN ajst-12328 119 9 m m PROPN ajst-12328 119 10 .. .. PUNCT ajst-12328 119 11 eigen eigen PROPN ajst-12328 119 12 value value NOUN ajst-12328 119 13 based base VERB ajst-12328 119 14 k k NOUN ajst-12328 119 15 - - PUNCT ajst-12328 119 16 means means NOUN ajst-12328 119 17 clustering cluster VERB ajst-12328 119 18 for for ADP ajst-12328 119 19 image image NOUN ajst-12328 119 20 compression[j compression[j NOUN ajst-12328 119 21 ] ] PUNCT ajst-12328 119 22 . . PUNCT ajst-12328 120 1 international international ADJ ajst-12328 120 2 journal journal PROPN ajst-12328 120 3 of of ADP ajst-12328 120 4 applied apply VERB ajst-12328 120 5 information information NOUN ajst-12328 120 6 systems,2012,3(7 systems,2012,3(7 NOUN ajst-12328 120 7 ) ) PUNCT ajst-12328 120 8 . . PUNCT ajst-12328 121 1 [ [ X ajst-12328 121 2 17 17 NUM ajst-12328 121 3 ] ] X ajst-12328 121 4 guo guo PROPN ajst-12328 121 5 yu yu PROPN ajst-12328 121 6 , , PUNCT ajst-12328 121 7 weng weng PROPN ajst-12328 121 8 guirong guirong PROPN ajst-12328 121 9 . . PUNCT ajst-12328 122 1 k k ADJ ajst-12328 122 2 - - ADJ ajst-12328 122 3 means++ means++ ADV ajst-12328 122 4 clustering clustering NOUN ajst-12328 122 5 - - PUNCT ajst-12328 122 6 based base VERB ajst-12328 122 7 active active ADJ ajst-12328 122 8 contour contour NOUN ajst-12328 122 9 model model NOUN ajst-12328 122 10 for for ADP ajst-12328 122 11 fast fast ADJ ajst-12328 122 12 image image NOUN ajst-12328 122 13 segmentation[j segmentation[j PROPN ajst-12328 122 14 ] ] PUNCT ajst-12328 122 15 . . PUNCT ajst-12328 123 1 journal journal PROPN ajst-12328 123 2 of of ADP ajst-12328 123 3 electronic electronic ADJ ajst-12328 123 4 imaging,2018,27(6 imaging,2018,27(6 NUM ajst-12328 123 5 ) ) PUNCT ajst-12328 123 6 . . PUNCT