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Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Roy P., Goswami S., Chakraborty S, Azar AT, Dey N (2014) Image Segmentation Using Rough Set Theory: A Review. International Journal of Rough Sets and Data Analysis, 1(2): 62-74.
Authors: Not Available
Year: 2014
Keywords: Not Available
Journal: Not Available
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link:
Full paper Ahmad Taher Azar_image-segmentation-using-rough-set-theory--a-review.pdf
Supplementary materials Not Available
Abstract:

In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

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