Increased Accuracy in Image Segmentation Considering the Modular Method Based on Texture Characteristics and Super Pixel
Journal of Electrical and Electronic Engineering
Volume 4, Issue 3, June 2016, Pages: 63-67
Received: May 13, 2016; Accepted: May 23, 2016; Published: Jun. 4, 2016
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Authors
M. Dabbaghha, Department of Telecommunications and Electrical Engineering, Malek Ashtar University of Technology, Tehran, Iran
M. Dashtbayazi, Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
S. Marjani, Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
M. Sabaghi, Laser and Optics Research School, Nuclear Science and Technology Research Institute (NSTRI), Tehran, Iran
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Abstract
To solve the problem of segmenting an image into homogeneous regions with large area, this paper proposes an efficient algorithm that is based optimization base on modularity and super pixel method. Due to the fact that a very small areas of the image before segmentation, the proposed algorithm automatically merged with neighboring small areas and to make larger modules. When the modularity pictures after the merger to reach its maximum stop, leading to the production of segmentation algorithms are final. To keep repetitive patterns in a homogeneous area, a feature based on its histogram modularity with features like color and eventually identified two areas by creating a similarity matrix, it is suggested. So that the problem of segmentation and complexity of the problem to some extent eliminate in a way that due to the combined areas can be achieved for repetitive patterns. Simulation results show that the algorithm has good accuracy.
Keywords
Segmentation, Modularity, Super Pixel
To cite this article
M. Dabbaghha, M. Dashtbayazi, S. Marjani, M. Sabaghi, Increased Accuracy in Image Segmentation Considering the Modular Method Based on Texture Characteristics and Super Pixel, Journal of Electrical and Electronic Engineering. Vol. 4, No. 3, 2016, pp. 63-67. doi: 10.11648/j.jeee.20160403.14
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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