Model Based Approach for Effective Segmentation of Images Based On Background Subtraction
Pavan Kumar Tadiparthi1, Srinivas Yerramalle2
1Pavan Kumar Tadiparthi, Associate Professor, Department of Information Technology, MVGR College of Engineering, Vizianagaram (Andhra Pradesh), India.
2Srinivas Yerramalle, Professor, Department of Information Technology, GIT, GITAM University, Visakhapatnam (Andhra Pradesh), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 160-164 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6413048419/19©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 is considered as a vital part of image analysis which better understanding of these images is possible. Among the algorithms available to analyze images under motion, background subtraction is considered to be the most imperative. In this article an attempt is made to propose a methodology of image segmentation based on background subtraction by a proposing and developing a model based on truncated Gaussian distribution. The experimentation is carried on CDnet 2014 data set and results are analyzed using the metrics.
Keywords: Image Segmentation; Background Subtraction; Truncated Gaussian Distribution; Performance Metric; Bench Mark Images.
Scope of the Article: Image Processing