EDGE DETECTION TECHNIQUES FOR IMAGE SEGMENTATION

Interpretation of image contents is one of the objectives in computer vision specifically in image processing. In this era it has received much awareness of researchers. In image interpretation the partition of the image into object and background is a severe step. Segmentation separates an image into its component regions or objects. Image segmentation t needs to segment the object from the background to read the image properly and identify the content of the image carefully. In this context, edge detection is a fundamental tool for image segmentation. In this paper an attempt is made to study the performance of most commonly used edge detection techniques for image segmentation and also the comparison of these techniques is carried out with an experiment by using MATLAB software.

[1]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[2]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[3]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[4]  Azriel Rosenfeld,et al.  Robust regression methods for computer vision: A review , 1991, International Journal of Computer Vision.

[5]  B. Poornima,et al.  SEGMENTATION AND OBJECT RECOGNITION USING EDGE DETECTION TECHNIQUES , 2010 .

[6]  Neil A. Thacker,et al.  Performance characterisation in computer vision: statistics in testing and design , 2001 .

[7]  Rui Seara,et al.  Image segmentation by histogram thresholding using fuzzy sets , 2002, IEEE Trans. Image Process..

[8]  N. Senthilkumaran,et al.  Image Segmentation - A Survey of Soft Computing Approaches , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

[9]  J. Canny Finding Edges and Lines in Images , 1983 .

[10]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[11]  S. Lakshmi,et al.  IJCA Special Issue on “Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications” CASCT, 2010. A study of Edge Detection Techniques for Segmentation Computing Approaches , 2022 .

[12]  Xueyin Lin,et al.  Color image segmentation using modified HSI system for road following , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[13]  Bernard Gosselin,et al.  A Study of Image Segmentation and Edge Detection Techniques , 2011 .

[14]  G. S. Roinson Edge Detection by Compass Gradient Masks , 1989 .

[15]  Adam Krzyzak,et al.  Robust Estimation for Range Image Segmentation and Reconstruction , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  William V. Stoecker,et al.  Unsupervised color image segmentation: with application to skin tumor borders , 1996 .

[17]  Rae-Hong Park,et al.  Robust Adaptive Segmentation of Range Images , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Jacques Blanc-Talon,et al.  Imaging and vision systems: theory, assessment and applications , 2001 .

[20]  R A Kirsch,et al.  Computer determination of the constituent structure of biological images. , 1971, Computers and biomedical research, an international journal.

[21]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[22]  Wenhui Yang,et al.  A Modified Fuzzy C-Means Algorithm for Segmentation of Magnetic Resonance Images , 2003, DICTA.

[23]  V. K. Banga,et al.  Color Image Segmentation Using Soft Computing , .