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Bangladesh Journal of Multidisciplinary Scientific Research; Vol. 1, No. 2 
ISSN 2687-850X      E-ISSN 2687-8518  

July-September; 2019 
Published by Centre for Research on Islamic Banking & Finance and Business  

 

        19 
 

A Comparisonal Study on Circle Detection for Real-World Images 
 
 

Md. Omar Faruq 
Lecturer 

Department of Computer Science and Engineering 

Bangladesh Army University of Engineering & Technology, Natore, Bangladesh 
E-mail: omarfarukcse10@gmail.com 

 
Md. Almash Alam 

Lecturer 
Department of Computer Science and Engineering 

Bangladesh Army University of Engineering & Technology, Natore, Bangladesh 
E-mail: almash.anik.61@gmail.com 

 
Md. Muktar Hossain 

Lecturer 
Department of Computer Science and Engineering 

Bangladesh Army University of Engineering & Technology, Natore, Bangladesh 
E-mail: muktar.hossain@bauet.ac.bd 

 
 
Abstract 
Real-life objects have different characteristics such as form characteristics, texture characteristics, and color characteristics and so 
on. The circular objects are the most common shape in our day to day lives and industrial production. So circle detection 
algorithm is ever ending research today. The most common algorithm is Circular Hough Transform which is used to detect a 
circle in an image. It is not very robust to noise so a simple approach to modified Circular Hough Transform algorithm is 
applied to detect the circle from an image. The image is pre-processed by edge detection. A comparison between Circular Hough 
Transform and modified Circular Hough Transform algorithm is presented in this research. 
 
Keywords: Circle Detection, Hough Transform, Modified Hough Transform.  
 
1. Introduction 
The circle is one of the most common shapes in our daily life, and indeed the universe. Planets, the movement of the planets, 
natural cycles, and natural shapes there are circles everywhere. The circle is one of the most complex shapes, and indeed the most 
difficult for a man to create, yet nature manages to do it perfectly. The centers of flowers, eyes, and many more things are 
circular and we see them in our everyday life. Detection of circles is very important for us. Usually, detect the object by detecting 
the object characteristics in the machine vision field. For these reasons, circle detection is an ever ending research application in 
the real world.Each circle detection should be accurate. The object including circle characteristics exist widely in our daily lives 
and industrial production, such as iris of eye automatic detection in face recognition, the inhibition halos of antibacterial activity 
detection (Silveira, 2004) in food industry, the camera calibration in optical study (Jiang & Quan, 2005) concentric circles 
precise identify of printed circuit board (PCB) round hole photoelectric image i. e. reflector in industrial production (Qiao et al., 
2010) concentric circles ring recognition of targeting system (Wang et al., 2008) and PET bottle of online inspection in 
blowing machines and beverage packaging industry and so on, are all need to use concentric circles detection technology. The 
circular shape when detected perfectly then recognition for iris of the eye in identifies the purpose, people counting, industrial 
production, etc. The goal of this thesis to find the circle or a set of circles includes its radius and center from an image. 

 
2. Literature Review 
Digital image processing is the use of the computer algorithm to perform image processing on digital images. Image processing 
operations can be roughly divided into three major categories: 

Image Compression, Image Enhancement, and Restoration, Image Segmentation (Gonzalez et al., 2005). 



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Input Image Edge Detected 

Image Compression: Image compression means reducing the amount of data required to represent an image. This 
technique is used for converting an image to a discrete signal for computer processing and compressing it to economize on 
storage capacity or communication bandwidth for transmitting purpose. 

Image Enhancement and Restoration: Whenever an image is converted from one form to another, such as digitizing, 
transmitting, scanning, etc, some form of degradation occurs at the output. Improvement in the quality of these degraded images 
can be achieved by the application of restoration and/or enhancement technique. Image enhancement improves the quality 
(clarity) of images for human viewing. Removing blurring and noise, increasing contrast and revealing details are examples of 
enhancement operations. The image signals are sometimes degraded by noise, low contrast or blurring. To obtain the original 
image or to improve it for analysis purposes, image enhancement, restoration or reconstruction techniques are used depending on 
the objective. 

Image Segmentation: Segmentation subdivides an image into its constituent regions and objects. When images taken by 
different sensors or at different times are to be compared, we use matching or registration techniques. This analysis includes 
segmentation of an image, measuring the properties of different parts and obtaining a relationship between the parts and 
comparing. The resulting descriptions are examined using certain models. The ultimate goal of the above techniques is to help an 
observer translate the contents of an image into useful information. 

Edge detection is part of image segmentation. Edge detection is very useful in some contexts. Edge characterizes object 
boundaries and is, therefore, useful for segmentation, registration, and identification of objects in scenes. The output of edge 
detection should be an edge image, in which the value of each pixel reflects how strong the corresponding pixel in the original 
image meets the requirements of being an edge pixel. Many edge detectors have been proposed, such as Sobel, Robert, and 
Prewitt. 
 
3. Research Methodology 
3.1 Circular Hough Transform 
The Circle Hough Transform (CHT) is a feature extraction technique for detecting circle. Detecting circles in an image are one 
of the problems that are discussed in this paper. Many algorithms, such as Linear Square Method (Hsiao et al., 2006), Hough 
Transform, and Canny Edge detection Algorithms have been proposed to detect circles. These algorithms detect circles from the 
edge detected images. Among these algorithms, Early Circular Hough Transform has been widely successful in meeting the real-
time requirement of being able to detect the circles in noisy environments. Modified Circular Hough Transform discussed in the 
next section. And also discussed Modified CHT is the best algorithm to detect circle as compared to Circular Hough 
Transform. Hough Transform was introduced by Paul Hough in 1962 and patented by IBM. In 1972 (Shapiro & George, 
2002) modified Hough Transform, which is used universally today. 

 

            

Figure 1. Circular Hough Transform Algorithm (Nitasha, et al., 2012) 
 

The equation of the circle is : 

r2 = (x-a)2 + (y-b)2 (1) 

As it can be seen the circle to get three-parameter r, a and b, where a & b are the center of the circle in the direction x & y 

respectively and r is the radius. 

 

The parameter representation of the circle is: 

x=a +r*cos(θ) (2) 

y=b + r*sin(θ) (3) 

Thus the parameter space for a circle will belong to R3. As the number of parameters needed to describe the shape 
increase as well as the dimension of the parameter space R increase so do the complexity of the Hough Transform. 

Output Circle Detected Image Accumulator 

Parameter Representation 



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4. Accumulator 
In this way, we sweep over the energy edge point in the input image drawing circle with the desired circle with desired radii and 
incrementing the value in our accumulator. When every edge point and every desired radius is used we can turn our attention to 
accumulator ill now contain numbers corresponding to the number of circles passing through the individual coordinate. Thus the 
highest number corresponds to the circle of the circle in the image. 

Figure 3. Coins image input of CHT algorithm (Virtanen, 2019) 

 

 
 

Figure 4: Circle detected image 



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Table1: Data points of the detected circle 

 
Since the parameter space of CHT is three dimensional, it may require lots of storage and computation. Also, CHT is not very 
robust to noise. Circle detected but redundant and spurious circles frequently occur. 
 
5. Modified CHT Algorithm 
The first stage noise is removed from the image by using a Gaussian filter. After that Edge strength and Edge Direction is found 
out at each pixel by using Sobel operator. Then non-maximum suppression. It is a process for marking all pixels whose intensity 
is not maximal as zero within a certain local neighborhood. 
 

 



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Multiple Circle’s images with Different Radius 

 

Figure 5. Bicycle image 

 

Figure 6. Circle detected image 

 
Table 2. Data of the above result 

 



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Here seen the result with Long Radius  

 

Figure 7. Circle image with long radius 

 

Figure 8. Circle detected image Table 3. 

Represents data of the above result 

 
6. Comparison between CHT and Modified CHT 
The CHT algorithm is implemented in the previous section where circle detected but redundant and spurious circles frequently 
occur and also CHT is not very robust to noise. In modified CHT algorithm, multiple circles with different radius and circle 
with long radius are detected in this section in Figure 4.2  and Figure 4.4 were redundant and spurious circles are not occurring 
and the modified circular hough transform algorithm needs less storage and computation. 

Table 4. Comparison between CHT and Modified CHT Algorithm 
 

Parameters CHT Algorithm Modified CHT Algorithm 

Robustness Not very robust to noise Very robust to noise 

Redundant and Spurious 

Circles 

Frequently occurs Not occur 

Processing Time More time required Ten timeless than CHT 

 
7. Conclusion 
The Hough transform has attracted a lot of research efforts over the decades. The circle is one of the most complex shapes, and 



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indeed the most difficult for a man to create, yet nature manages to do it perfectly. For these reasons, it is often non-trivial to 
group the extracted edge features to an appropriate set of circles. Each circle detection should be accurate. The main motivations 
behind such interest are the noise immunity, the ability to deal with occlusion, and the expandability of the transform. Many 
variations of it have evolved. The comparison between CHT and Modified CHT performed successfully. The modified circular 
hough transform algorithm requires less storage and computation. 
 
8. Acknowledgment 
Authors are sincerely grateful Department of Computer Science & Engineering, Bangladesh Army University of Engineering & 
Technology. 
 
9. References 
Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2005). Digital image processing using MATLAB (Beijing: Publishing House 

of Electronics Industry): p252. 
Hsiao, P. Y., Chen, C. H., Chou, S. S., Li, L. T., & Chen, S. J. (2006, May). A parameterizable digital-approximated 2D 

Gaussian smoothing filter for edge detection in noisy image. In 2006 IEEE International Symposium on Circuits and 
Systems (pp. 4-pp). IEEE. 

Jiang, G., & Quan, L. (2005, October). Detection of concentric circles for camera calibration. In Tenth IEEE International 
Conference on Computer Vision (ICCV'05) Volume 1 (Vol. 1, pp. 333-340). IEEE. 

Nitasha, S. S., Sharma, R., & Sharma, R. (2012). Comparison between circular Hough transform and modified cCanny edge 
detection algorithm for circle detection. Int J Eng Res Technol (IJERT), 1(3), 15. 

Qiao, N. S., Ye, Y. T., Mo, C. H., Wu, Y. F., & Liu, L. (2010). Method for the detection of concentric circles of photoelectric 
image of circular hole in printed circuit board. Acta Optica Sinica, 30(1), 75-78.  

Silveira, M. (2004, May). Antibacterial activity detection and evaluation based on the detection of multiple concentric circles 
with the Hough transform. In First Canadian Conference on Computer and Robot Vision, 2004. Proceedings. (pp. 
329-335). IEEE. 

Shapiro, L., & George, C. (2002). An Introduction of Computer Vision. Prentice-Hall, Inc. 
Virtanen,M.(2019).Myymälän aktiivinen varastosaldojen seuranta. Retrieved from 

https://www.cis.rit.edu/class/simg782.old/talkHough/HoughLecCircles.html 

Wang, H., Niu, J., Liu, S., & Wang, D. (2008). A concentric circles adaptive detection algorithm of measured imagery target 
surface. Acta Photonica Sinica, 37(10), 2094-2098. 

 
 

 

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