A review on image segmentation techniques

Pal, Nikhil R. ; Pal, Sankar K. (1993) A review on image segmentation techniques Pattern Recognition, 26 (9). pp. 1277-1294. ISSN 0031-3203

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Official URL: http://linkinghub.elsevier.com/retrieve/pii/003132...

Related URL: http://dx.doi.org/10.1016/0031-3203(93)90135-J


Many image segmentation techniques are available in the literature. Some of these techniques use only the gray level histogram, some use spatial details while others use fuzzy set theoretic approaches. Most of these techniques are not suitable for noisy environments. Some works have been done using the Markov Random Field (MRF) model which is robust to noise, but is computationally involved. Neural network architectures which help to get the output in real time because of their parallel processing ability, have also been used for segmentation and they work fine even when the noise level is very high. The literature on color image segmentation is not that rich as it is for gray tone images. This paper critically reviews and summarizes some of these techniques. Attempts have been made to cover both fuzzy and non-fuzzy techniques including color image segmentation and neural network based approaches. Adequate attention is paid to segmentation of range images and magnetic resonance images. It also addresses the issue of quantitative evaluation of segmentation results.

Item Type:Article
Source:Copyright of this article belongs to Elsevier Science.
Keywords:Image Segmentation; Fuzzy Sets; Thresholding; Edge Detection; Clustering; Relaxation; Markov Random Field
ID Code:26051
Deposited On:06 Dec 2010 13:10
Last Modified:13 Jun 2011 05:56

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