Open Access Open Access  Restricted Access Subscription or Fee Access

A Survey of Edge Detection and Object Segmentation from Contour based Soft Computing Approaches

Sumant Singh, Siddhartha Choubey


Soft computing approaches such as consisting of fuzzy logic, neural or evolutionary computation are the emerging field with the wide applications in edge detection and image segmentation. Such a process of partitioning the pixels regions of the digital images with boundary between two homogenous regions refers to the detection of edges. There was various approaches that have been implemented to achieve the same but their performance and its scope of operation varies widely. The main aim of the study is to present a survey of the edge detection techniques to facilitate easy object or image segmentation.

Keywords: Image segmentation, edge detection, fuzzy logic, genetic algorithm, neural network



Cite this Article:
Sumant Singh, Siddhartha Choubey, A Survey of Edge Detection and Object Segmentation from Contour based Soft computing Approaches, Recent Trends in Parallel Computing. 2015; 2(1): 16–20p.

Full Text:



Orlando J. Tobias and Rui Seara. Image Segmentation by Histogram Thresholding Using Fuzzy Sets. IEEE Transactions on Image Processing. 2002; 11(12): 1457–1465p.

M. Abdulghafour. Image segmentation using Fuzzy logic and genetic algorithms. Journal of WSCG. 2003; 11(1).

N. Senthil Kumar and R. Rajesh. Edge Detection Techniques for Image Segmentation - A Survey. Proceedings of the International Conference on Managing Next Generation Software Applications

(MNGSA-08). 2008; 749–760p.

Mantas Paulinas and Andrius Usinskas. A Survey of Genetic Algorithms Applicatons for Image Enhancement and Segmentation. Information Technology and Control. 2007; 36(3): 278–284p.

Jianxun Zhang, Quanli Liu and Zhuang Chen. A Medical Image Segmentation Method Based on SOM and Wavelet Transforms. Journal of Communication and Computer. 2005; 2(5): 46–50p.

Dao Qiang Zhanga and Song Can Chena. A novel kernelized fuzzy C-means algorithm with application in medical image segmentation. Artificial Intelligence in Medicine. 2004; 32: 37–50p.

N. Senthil Kumar Anand, R. Rajesh. A Study on Edge Detection Methods for Image Segmentation. Proceedings of the International Conference on Mathematics and Computer Science (ICMCS-2009). 2009;1:255–259p.

Dinesh K. Sharma, Loveleen Gaur and Daniel Okunbor. Image Compression and Feature Extraction with Neural Network. Proceedings of the Academy of Information and Management Sciences. 2007; 11(1) 33–38p.

Wei Sun and Yaonan Wang. Segmentation Method of MRI Using Fuzzy Gaussian Basis Neural Network Neural Information Processing. Letters and Reviews. 2005; 8(2): 19–24p.

Xian Bin Wen, Hua Zhang and Ze Tao Jiang. Multiscale Unsupervised Segmentation of SAR Imagery Using the Genetic Algorithm. Sensors. 2008; 8: 1704–1711p.

A. Borji, and M. Hamidi. Evolving a Fuzzy Rule-Base for Image Segmentation. International Journal of Intelligent Systems and Technologies. 2007; 178–183p.

Metin Kaya. Image Clustering and Compression Using an Annealed Fuzzy Hopfield Neural Network. International Journal of Signal Processing. 2005; 80–88p.

N. Senthil Kumar and R. Rajesh. A Study on Split and Merge for Region based Image Segmentation. Proceedings of UGC Sponsored National Conference Network

Security (NCNS-08). 2008; 57–61p.


  • There are currently no refbacks.