International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 9, No. 2, 8 Feb. 2017
Cover page and Table of Contents: PDF (size: 1142KB)
Author(s)
Shiv Gehlot
1,*
John Deva Kumar
2
Index Terms
Image segmentation, normalized self correlation function, relative error function, piecewise image segmentation, Laplace filter, Otsu's method
Abstract
Image segmentation has a crucial role in image processing. Classical segmentation techniques based on thresholding have been extensively used but they fail drastically for noisy or non-uniformly illuminated images. Several alternatives presented over the time have filled this void but with increased complexity. In this paper we present an algorithm to address the above issues with minimum complexity. We propose normalized self correlation function (NSCF) which forms a basis for the progress of the algorithm. We also introduce relative error function (REF) which is used for qualitative assessment of the algorithm and its comparison with other algorithms. We also propose a second algorithm named piecewise image segmentation (PIS) which is a generalized edge-based method able to generate any desired edge map. The results show that the proposed algorithms are able to perform well for different scenarios and at the same time better than traditional algorithms.
Cite This Paper
Shiv Gehlot, John Deva Kumar,"The Image Segmentation Techniques", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.2, pp.9-18, 2017. DOI: 10.5815/ijigsp.2017.02.02
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