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         Geoplanning 
Vol 5, No. 1, 2018, 63-74                                                                                                                                                            Journal of Geomatics and Planning 

                                                                                                 E-ISSN: 2355-6544 
http://ejournal.undip.ac.id/index.php/geoplanning 

doi: 10.14710/geoplanning.5.1.63-74 
 

PRELIMINARY INVESTIGATION OF THE ROBUSTNESS OF MAXIMALLY STABLE 
EXTREMAL REGIONS (MSER) MODEL FOR THE AUTOMATIC REGISTRATION OF 
OVERLAPPING IMAGES 

O. G. Ajayi a, I. J. Nwadialor b, I. C. Onuigbo b, O. A. Kemiki b 
a Department of Surveying and Geoinformatics, Federal University of Technology, Minna, PMB 65, Minna, Nigeria, Nigeria  
b Federal University of Technology, Minna, Nigeria  
 

Abstract: Various researchers in Digital Image Processing have developedkeen 

interest in the automation of object detection, description and extraction process used 
for various applications, and this has led to the development of series of feature 
detection and extraction models, one of which is the Maximally Stable Extremal 
Regions Features Algorithm (MSER). This paper investigates the robustness of MSER 
algorithm (a blob-like and affine-invariant feature detector) for the detection and 
extraction of corresponding features used for the automatic registration of series of 
overlapping images. The robustness investigation was carried out in three different 
registration campaigns using overlapping images extracted from google earth online 
image data repository and image pairs acquired from an Unmanned Aerial Vehicle 
(UAV) flight mission. Sum of Square Difference (SSD) and Bilinear interpolation models 
were used to establish the similarity measure between the registered images, 
resampling of the pixel-values and computation of non-integer coordinates 
respectively while Random Sampling Consensus (RANSAC) algorithm was used to 
exclude the outliers and to compute the transformation matrix using affine 
transformation function. The results obtained from this preliminary investigation 
shows that the processing speed of MSER is quite high for Automatic Image 
Registration with a relatively high accuracy. While an accuracy of 61.54% was 
obtained from the first campaign with a processing time of 11.92 seconds, the second 
campaign gave an accuracy of 52.02% with a processing time of 11.20 seconds and the 
third campaign produced an accuracy of 55.62% with a processing time of 3.27 
seconds. The obtained speed and accuracy shows that MSER is a very robust model 
and as such, can be deployed as a feature detection and extraction model in the 
development of an automatic image registration scheme.  

 Copyright © 2018 GJGP-UNDIP  
This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license. 

Ajayi, O.G et al. (2018). Preliminary investigation of the robustness of maximally stable extremal regions (mser) model for the automatic registration 
of overlapping images. Geoplanning: Journal of Geomatics and Planning, 5(1), 63-74. doi: 10.14710/geoplanning.5.1.63-74 

1. INTRODUCTION 

Recently, obtaining descriptors of features by analyzing the linear scale space of an image have proved to 
be robust and reliable in the recognition, extraction and matching of objects (Sivic & Zisserman, 2003) 
which is a very crucial stage in mosaic generation from overlapping images. One of the essential image 
processing operations in remote sensing is image registration. Diverse applications such as change 
detection, image fusion, etc. are made possible by mosaicking overlapping image pairs differently acquired 
under different imaging conditions and circumstances, at different time epochs, covering the same imaging 
area (Kumar, Manjunath, & Rao, 2003). The basic aim of image registration is to ensure that two 
overlapping image pairs are aligned and matched spatially such that analogous pixels in the overlapping 
image pairs will correspond to the exact imaged scene of interest (physical region). This alignment and 
matching is achieved by estimating scale, rotation and translation using a defined or selected appropriate 
transformation function. It is an unavoidable issue in various fields of application of digital image 

OPEN ACCESS 

Article Info: 
Received: 21 November 2017 
in revised form: 5 March 2018 
Accepted: 30 March 2018 
Available Online:  30 April 2018 
 

Keywords:  
MSER, Image Registration, 
Overlapping Images, RANSAC, 
UAV 
 

Corresponding Author: 
Department of Surveying and 

Geoinformatics, Federal 

University of Technology, Minna, 

PMB 65, Minna, Nigeria, Nigeria 

Email: ogbajayi@gmail.com  

  

https://doi.org/10.14710/geoplanning.5.1.63-74
https://doi.org/10.14710/geoplanning.5.1.63-74
mailto:ogbajayi@gmail.com


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processing, especially, those that involves pixel by pixel comparison of two or more images of the same 
scene (Dai & Khorram, 1999).  

The determination of a transformation function which best aligns features in the base image with the 
conjugate features in the observed image is the major task of image registration. According Zitová & Flusser 
(2003), automatic image registration can be classified into both area based method which directly aligns the 
intensity or pixels of the image pair holistically, and feature based method which extracts higher-level 
structures from the image pair, and find similar features to execute the registration task which makes this 
method more useful when the detection and extraction of conjugate features are reliably possible. 
Nevertheless, low spatial resolution and the presence of blur and noise in the base and moving image pairs 
will negatively affect the accurate detection and extraction of corresponding or conjugate feature points 
(Zitová & Flusser, 2003). Brown (1992) presented the four major components of Image Registration as 
Feature Space, Search Space, Search Strategy and Similarity Measure. The selection of each of these model 
components is determined by the nature and properties of the images (Rao, Rao, Manjunath, & Srinivas, 
2004). 

Various feature detection and extraction models have been implemented for accurate generation of mosaic 
from overlapping image pairs. Some of these include Speeded Up Robust Feature (SURF) algorithm, Scale 
Invariant Feature Transform (SIFT) algorithm, Harris and Stephen Corner Detector, etc (Olaleye et al., 2015) 
(Ajayi et al., 2014). Though these models have proved to be relatively robust, current research focus still 
seeks to investigate the possibility of obtaining a more robust feature detection and extraction model, 
which necessitated the need for this study. The Maximally Stable Extremal Regions (MSER) model is a 
robust feature extraction algorithm. MSER extracts a number of co-variant regions from an image, and it is 
a blob-like, local and affine invariant feature detector which is invariant to illumination changes and image 
resolution. It scales well for both small and large objects (Varah & Grujić, 2013). An MSER is a stable 
connected component of some gray-level sets of the image and it is based on the idea of extracting regions 
which stay nearly the same through a wide range of thresholds. While MSER has been widely and 
successfully applied in different image processing applications (Mikolajczyk et al., 2005) (Fraundorfer & 
Bischof, 2005) some of which include tracking and 3D segmentation (Donoser & Bischof, 2006), retrieval or 
restoration of images (Nister & Stewenius, 2006), matching of wide baselines (Matas et al., 2004) and 
curvilinear structures (Lemaitre et al., 2011), object recognition (Obdrzalek & Matas, 2002), real-time visual 
surveillance (Salahat et al., 2015), Field Programmable Gate Array- FPGA (Kristensen & MacLean, 2007), cell 
detection and analysis (Kaakinen et al., 2013), etc, research efforts aimed at implementing it for mosaic 
generation or automatic image registration is relatively unknown. This paper presents some preliminary 
findings of the investigation of the robustness of MSER for the automatic registration of overlapping image 
pairs using images acquired from Trimble Ux-5 Unmanned Aerial Vehicles (UAV) and google earth online 
image data repository. 

 

2. DATA AND METHODS 

2.1. Mathematical Formulation of The MSER Model 

Obdrzalek & Matas (2002) presented the formal definition of MSER and mathematical annotation of its 
terms such as image region, region boundary, extremal region and Maximally Stable Extremal Regions. 
According to  (Kimmel, Zhang, Bronstein, & Bronstein, 2011), the following is the mathematical formulation 
of MSER model: 

Let tR
 be the family of connected components representing an edge in the component tree. Such regions 

are referred to as extremal by Obdrzalek & Matas (2002) since 

either: int int( )  or ( )  t t t tI R I R I R I R  which implies that the value of all the pixels within the 
regions are either completely darker or completely brighter than the pixel values along the boundary where 

the intensity is exactly equal to t . 

Equation (1) gives the stability of a Region tR
 : 

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1

( )
( )

( )

t
t

t

A R
R

d
A R

dt

 

, where 
( )tA R

= Area tR
 ...................................................................................... (1) 

A region whose area remains slightly thesame with the change of the threshold t  is considered stable while 

a region tR
 is termed maximally stable if 1( )tR

 (an affine – invariant property) has a local maximum at t  

since area ratio is retained under affine transformation. This suggests that for an affine transformation T  

of the domain X , the corresponding regions 
' and R R detected in images I d and

1( )I T 

, respectively will 

be related to 
'TR R . These are the kind of regions that the MSER model detects. 

Briefly highlighted in Figure 1 are the stages involved, techniques and the mathematical models used in the 
automatic registration of overlapping images. Figures 1 and 2 present a flowchart of the methodology. 

It extracts the feature points by various filters or descriptors, 
Maximally Stable Extremal Regions (MSERs) Feature detector 
algorithm was used for the feature detection and as the Descriptor 
extraction method. 

                                                                                                                                 
Builds the relationship between the feature point sets from the 
reference and floating images. Sum of Square Difference (SSD) metric 
was used at a Match Threshold of 1.0 Scalar vector. SSD Establishes 
Similarity measure between the images to be registered. 

 

The Model Estimation decides the types and Parameters that are 
needed for the mapping function. The parameters are computed from 
the feature pairs of the correspondence built in feature matching 

 

The Bilinear interpolation was used for the resampling of the pixel-
values and computation of non-integer coordinates. RANSAC was used 
to exclude the outliers and to compute the Transformation Matrix 
using Affine transformation function 

Figure 1: Stages and techniques used for the Automatic Image Registration 

Detailed discussion of the Mathematical models of SSD, RANSAC, Bilinear Interpolation and Affine 
Transformation functions used can be found in (Ajayi et al., 2014). All the necessary computations and 
image registration were carried out using code scripts written in MATLAB R2014a environment. 

2.2. Data Used for Experimentation 

The model experimentation is subdivided into three (3) different image registration campaigns with 
overlapping image pairs showing different geographical areas used for the experimentation. Pairs of 
overlapping images with image size of 700x1028 pixels each, showing part of the University of Lagos, Akoka-
Campus, Lagos Nigeria, extracted from google earth online image data repository was used for the first 
image registration campaign while overlapping image pairs showing part of the Federal University of 
Technology, Main Campus, Minna Nigeria, also extracted from google earth was used for the second image 
registration campaign. The images are of the size 2745x4800 each. Overlapping image pairs of part of The 
Federal Polytechnic Ado-Ekiti, acquired with the aid of Trimble UX5 UAV, was used for the model 
experimentation in the third image registration campaign. The image pairs for the first image registration 
campaign is presented as Figures 3a and 3b while the image pairs for the second image registration 
campaign is presented as Figures 4a and 4b. The image pairs used for the third registration campaign is 
presented as Figures 5a and 5b and its size is 800x532x3 pixels each. 

Feature 

Detection 

Feature 

Matching 

Model Estimation 

Resampling and 

Transformation 

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Figure 3a: An Image of part of UNILAG Campus (The Base Image). 

 

 

 

 

 

 

 

 

 

 

Figure 3b: An Image of part of UNILAG Campus (The Moving Image). 

 

 

 

 

 

 

 

 

 

 

Figure 4a: Image part of FUTMinna, Main Campus (Base image). 

 

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Figure 4b: Image part of FUTMinna, Main Campus (Moving image). 

 

 

 

 

 

 

 

 

                          

 

 

 

 

 

 

Figure 5a: Image part of Federal Polytechnic Ado,        Figure 5b: Image part Federal Polytechnic, Ado 

Ekiti (Base image)          Ekiti (Moving Image) 

Affine transformation function was adopted for the computation of the model’s transformation matrix 
(equation 2). It has six unknown parameters and requires a minimum of three reference points. 

 


































f

e

v

u

dc

ba

y

x
…………………………………………………………………………………………………………… (2) 

Where: 








dc

ba
 are scalar Quantities,   









f

e
 are Translation Parameters. 

α = rotation angle, Tx = Translation in x axis and Ty = Transformation on y axis  

Where:  ɑ = cos α, b = sin α (α is the rotation angle), c = Tx  and d = Ty (Translation in x and y axis 
respectively)  

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Feature Extraction 

Initial Feature Matching 

Initial Transformation (Affine 

transform) and Consistency Check in 

Image Space 

Estimation of Transformation 

Parameters (Affine transform) 

Image Resampling 

Registered Image 

Reference or Base Image Moving or Floating 

Image 

Feature Search and Detection 

INPUT THE IMAGES 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 2: Flowchart of the step by step procedure of executing  
feature based auto-registration (Adapted from Ajayi et al., 2014) 

 
2.3. Accuracy Assessment 

In order to ascertain the robustness of the registration model, an accuracy assessment method developed 
by (Olaleye et al., 2015) was adopted. This method (Equation 3) makes use of the total percentage of 
matched inliers, out of the total matched conjugate points (inliers and outliers) to determine how robust 
the feature extraction process is and in turn, define the degree of accuracy or accuracy measure of the 
entire image registration process since the success of the process depends largely on the robustness of the 
feature detection and extraction model and the model used for the exclusion of outliers.  

(100%)
a

x
b

 ............................................................................................................................... (3)  

Where x  the overall accuracy (%), a  total matched inliers (excluding outliers), and b  total matched 
points (both inliers and outliers). 

 

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3. RESULTS AND DISCUSSION 

The results obtained from the first automatic image registration campaign using MSER as the feature 
detection and extraction model are as given in Figures 6a, 6b and 6c. Figure 6a shows the identified and 
extracted corresponding features, figure 6b presents the extracted corresponding points/features having 
excluded the outliers using RANSAC while Figure 6c presents the Mosaic generated by registering the two 
overlapping images. All units are measured in pixels. 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 6a: All Matched and extracted corresponding points (Inliers and Outliers) for the first campaign (In 
pixels). 

 

 

 

 

 

 

 

 

 

 

 

Figure 6b: Extracted Corresponding points used for the computation of Transformation Matrix (outliers 
excluded) for the first campaign (in pixels). 

 

 

 

 

 

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Figure 6c: The registered Image or Mosaic generated from the overlapping image pairs for Campaign 1 (in 
pixels) using the developed model. 

With a registration accuracy of 61.44%, the computed transformation matrix of the first registration 
campaign is: 

1

1.0171 0.0756 502.0217

0.0009 1.0169 49.8708

0 0 1.0000

T

 
 


 
  

 

The results obtained from the second image registration campaign are presented in Figures 7a-7c. Figure 7a 
presents all the matched corresponding features (inliers and outliers inclusive) while figure 7b presents the 
extracted corresponding points/features, having excluded the outliers using RANSAC. Figure 7c presents the 
Mosaic generated by registering the two overlapping images. All units are measured in pixels. 

 

 

 

 

 

 

 

 

Figure 7a: All matched corresponding points (inliers and outliers inclusive) for campaign 2 (in pixels). 

 

 

 

 

 

 

 

 

Figure 7b: Matched inliers used for the image registration for campaign 2 (in pixels). 

 

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Figure 7c: Mosaic generated for campaign 2 (in pixels) using the developed model. 

With a registration accuracy of 52.02%, the computed transformation matrix of the second registration 
campaign is: 

2

0.0010 0.0000 2.0287

0.0000 0.0010 0.0504

0 0 0.0010

T

 
 

  
 
  

 

The results obtained from the third registration campaign are presented in Figures 8a-8c. While figure 8a 
presents all the matched corresponding features (inliers and outliers inclusive), figure 8b presents the 
extracted corresponding points/features, having excluded the outliers using RANSAC and the Mosaic 
generated by registering the two overlapping images is presented in figure 8c. All units are measured in 
pixels. The accuracy obtained from the third registration campaign is 55.62% while computed 
transformation matrix of the registration for the third registration campaign is: 

3

1.0051 0.0041 115.8039

-0.0014 1.0197 15.3436

0 0 1.0000

T

 
 


 
  

 

  

 

 

 

 

 

 

 

 

 

 

            

Figure 8a: All matched corresponding points (Camp. 3)    Figure 8b: Matched Inliers (Campaign 3) in pixels 

 

 

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Figure 8c: Mosaic generated from the UAV acquired image pair (Campaign 3) in pixels 

A total of 191, 662 and 27 corresponding points were automatically extracted from the overlapping images 
of the first, second and third image registration campaigns respectively. The number of matched 
corresponding points of the second image registration campaign was more than the matched 
corresponding features of the first and third campaigns because the image pairs used for the second 
registration campaign have a larger total area coverage or image size (approximately 2745x4800 pixels) 
compared to the image pairs used for the first and third registration campaigns which have image sizes of 
700x1028 and 800x532 pixels respectively. Also, the image pairs used for the second registration campaign 
have a higher overlapping percentage, and as such, the model has sufficient latitudes for the extraction of 
corresponding features. It was observed that the number of matched points using MSER are very few which 
makes it an excellent model for applications where only small matches are needed such as the computation 
of epipolar geometry (Mikolajczyk et al., 2005). This suggest that integrating MSER with epipolar correlation 
in the development of an automatic image registration scheme will result into a highly robust model with 
respect to time and accuracy.  

Approximately 61.44%, 52.02% and 55.62% of the matched corresponding points for the first, second and 
third registration campaigns respectively were used for the automatic image registration. These 
percentages were obtained after the exclusion of outliers (mis-matches) from all the matched points using 
Random Sampling Consensus (RANSAC). This shows that 38.56%, 47.98% and 44.38% of all the matched 
points were outliers for the first, second and third image registration campaigns respectively. This imply 
that MSER automatically extracted more outliers during the second image registration campaign which can 
be attributed to the quality of the spatial resolution of the used overlapping image pairs. They also depict 
the accuracy level or measure of reliability of the automatic registration model (Olaleye et al., 2015).  

The time taken for the complete execution of the automatic image registration was approximately 11.92 
seconds (speed of the developed registration scheme) for the first registration campaign, about 11.20 
seconds was expended on the second campaign and 3.27 seconds was used for the third campaign. The 
processing time of each of the registration campaigns was determined automatically using the system run 
time generated by the MATLAB report. This is in tandem with the results obtained by (Mikolajczyk et al., 
2005) which attests to the speed of MSER when compared to 5 other detectors. An improved 
computational time was also recorded by Nistér & Stewénius (2008) and (Kaakinen et al., 2013) and this is 
as a results of MSER’s negligible need for pre-optimization procedures which dramatically reduces its computation 

time (Kaakinen et al., 2013). 

Though MSER is regarded as one of the best region detectors due to its robustness against view point, scale 
and lightning changes, this fair results obtained especially in registration campaign 2 can be attributed to 

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the sensitivity of MSER algorithm to image blurs (Śluzek, 2016) and as such, the resolution of the image may 
be influential to the process of determining the efficiency of the successfully matched inliers and 
consequently, the registration result. This is evident in the fact that better accuracies were recorded in the 
first and third registration campaigns compared to the accuracy obtained from the second registration 
campaign because the image pairs used for the second registration campaign are quite blurry. 

4. CONCLUSION 

The result of the preliminary investigation of the robustness of MSERs feature identification, detection and 
extraction algorithm for the extraction of corresponding features used for the automatic registration of 
overlapping images is herein presented. All computation was done with code scripts written in MATLAB 
R2014a environment. Though the percentage matched inliers used for the registration of the images were 
quite good (61.44%, 52.02% and 55.62% for the first, second and third registration campaigns respectively), 
registration campaign 2 produced the least satisfactory results of the three campaigns which is an evidence 
that MSER is highly sensitive to image blurs (Lemaitre et al., 2011), since the spatial resolution of the image 
pairs used for the second campaign is not as high as the resolution of the image pairs used for the first and 
third campaigns. It is however noted that MSER is highly robust and can be a choice model for feature 
detection and extraction in an automatic image registration scheme. 

For further studies, attempt shall be made to investigate the possibility of pre-defining a minimum 
threshold (radius) as the acceptable region for smaller MSERs so as to avoid unnecessarily small regions. 
The robustness of edge-enhanced Maximally Stable Extremal Regions which should help the sensitivity of 
this algorithm to image blurs and noise will also be investigated. 
 

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