DocumentCode :
1624847
Title :
Object boundary detection using Rough Set Theory
Author :
Phophalia, Ashish ; Mitra, Sanjit ; Rajwade, Ajit
Author_Institution :
DA-IICT, Gandhinagar, India
fYear :
2013
Firstpage :
1
Lastpage :
4
Abstract :
A Rough Set Theory based closed form object boundary detection method has been suggested in this paper. Most of the edge detection methods fail in getting closed boundary of objects of any shape present in the image. Active contour based methods are available to get such object boundaries. The Multiphase Chan-Vese Active Contour Method is one of the most popular of such techniques. However, it is constrained with number of objects present in the image. The granular processing using Rough Set method overcomes this constraint and provides a closed curve around the boundary of the objects. This information can further be utilized in selection of similar patches for various image processing problems such as Image Denoising, Image Super-resolution, Image Segmentation etc. The proposed boundary detection method has been tested in presence of noise also. The experimental results have shown on synthetic image as well as on MRI of human brain. The performance of proposed method is found to be encouraging.
Keywords :
object detection; rough set theory; MRI; active contour based methods; closed form object boundary detection method; granular processing; human brain; image denoising; image processing problems; image segmentation; image super-resolution; multiphase Chan-Vese active contour method; rough set theory; synthetic image; Active contours; Approximation methods; Detectors; Image edge detection; Image segmentation; Noise; Set theory; Active Contour Method; Object Boundary Detection Problem; Rough Set Theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2013 Fourth National Conference on
Conference_Location :
Jodhpur
Print_ISBN :
978-1-4799-1586-6
Type :
conf
DOI :
10.1109/NCVPRIPG.2013.6776259
Filename :
6776259
Link To Document :
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