Performance Analysis of Efficient Framework of Image Segmentation using Energy Minimization Function
Pranoti P. Mahakalkar1, Aarti J. Vyavahare2
1Pranoti P. Mahakalkar, P G Student, Department of Electronics & Tele-Communication, Modern College of Engineering, Pune University, Pune (Maharashtra). India.
2Dr. Aarti J. Vyavahare, Associate Professor, Department of Electronics & Tele-Communication, Modern College of Engineering, Pune University, Pune (Maharashtra). India.
Manuscript received on 13 June 2016 | Revised Manuscript received on 20 June 2016 | Manuscript Published on 30 June 2016 | PP: 30-35 | Volume-5 Issue-5, June 2016 | Retrieval Number: E4581065516/16©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Image segmentation plays very vital role in many image processing applications and domains. Efficient image segmentation leads to accurate results to end users. There are number of image segmentation techniques presented so far with different objectives. The existing segmentation techniques are based on various features of image. Target objects segmentation from the input image which may from different application areas such as medical, security systems etc. The segmentation of images those are having many complex areas, mixed pixel intensities or noise corrupted data. The existing level set based image segmentation methods needs the prior information about the total number of image segments which is practically impossible for each image. Therefore to overcome such limitations and research challenges of image segmentation, in this paper we proposed the new image segmentation energy function with two distribution descriptors in order to distinguish automatically background and target region from input image. This paper presents the extensive analysis of this proposal method against the existing method in terms of execution time and JD error rates. In this propose scheme, first single background descriptor models the heterogeneous background with multiple regions. Then, the target descriptor takes into account the intensity distribution and incorporates local spatial constraint. The proposed descriptors, which have more complete distribution information, construct the unique energy function to differentiate the target from the background and are more tolerant of image noise. The simulation and evaluation of this proposed method is done by using well known image processing tool MATLAB.
Keywords: Image Segmentation, Image Processing, Energy Minimization, Level Set Methods, Region Based, Edge Based, Minimizer
Scope of the Article: Image Processing