An Efficient Boundary Detection and Image Segmentation Method Based on Perceptual Organization
 | International Journal of ComputerTrends and Technology (IJCTT) |  |
| © 2014 by IJCTT Journal | ||
| Volume-7 Number-1 | ||
| Year of Publication : 2014 | ||
| Authors : Ch.Sambasivarao , V. Naganjaneyulu. | ||
| DOI : 10.14445/22312803/IJCTT-V7P122 |
Ch.Sambasivarao , V. Naganjaneyulu. Article: An Efficient Boundary Detection and Image Segmentation Method Based on Perceptual Organization. International Journal of Computer Trends and Technology (IJCTT) 7(1):40-46, January 2014. Published by Seventh Sense Research Group.
Abstract
In this paper, we presents a novel method for detecting the boundaries of the object in outdoor images by using most common properties of the images such as perceptual organization laws. Here the proposed segmentation scheme is based on perceptual organization and background recognition. This paper mainly concentrates to recognize the structurally challenging objects, which is generally combination of several constituent parts. Our new proposed method based on perceptual organization model can efficiently recognize the non-accidental relationships, which are perfectly structured from the constituent parts of the strictly structured objects. The simulation results of this paper show that the efficient and accurate image segmentation by using perceptual organization models.
References
[1] Rafael C. Gonzalez, Richard E. Woods, “Digital Image Processing”, 2nd ed., Beijing: Publishing House of Electronics Industry, 2007.
[2] S.Aksoy, “Image Segmentation”, Department of Computer Engineering, Bilkent Univ.
[3] K. G. Gunturk, “EE 7730- Image Analysis I”, Louisiana state university.
[4] W. X. Kang, Q. Q. Yang, R. R. Liang,“The Comparative Research on Image Segmentation Algorithms”, IEEE Conference on ETCS, pp. 703-707, 2009.
[5] N. R. Pal, S. K. Pal, “A Review on Image Segmentation Techniques”, Pattern Recognition, Vol. 26, No. 9, pp. 1277- 1294, 1993.
[6] H. Zhang, J. E. Fritts, S. A. Goldman, “Image Segmentation Evaluation: A Survey of unsupervised methods”, computer vision and image understanding, pp. 260-280, 2008.
[7] K. K. Singh, A. Singh,“A Study of Image Segmentation Algorithms for Different Types of Images”, International Journal of Computer Science Issues, Vol. 7, Issue 5, 2010.
[8] S. S. Varshney, N. Rajpal, R. Purwar,“Comparative Study of Image Segmentation Techniques and Object Matching using Segmentation”, Proceeding of International Conference on Methods and Models in Computer Science, pp. 1-6, 2009.
[9] P. Karch, I. Zolotova, “An Experimental Comparison of Modern Methods of Segmentation”, IEEE 8th International Symposium on SAMI, pp. 247-252, 2010.
[10] H. G. Kaganami, Z. Beij,“Region Based Detection versus Edge Detection”, IEEE Transactions on Intelligent information hiding and multimedia signal processing, pp. 1217-1221, 2009.
[11] L.Aurdal, “Image Segmentation beyond thresholding”, Norsk Regnescentral, 2006.
[12] Y. Zhang, H. Qu, Y. Wang,“Adaptive Image Segmentation Based on Fast Thresholding and Image Merging”, Artificial reality and Telexistence-Workshops, pp. 308-311, 1994.
[13] Y. Chang, X. Li, “Adaptive Image Region Growing”, IEEE Trans. On Image Processing, Vol. 3, No. 6, 1994.
[14] T. Gevers, V. K. Kajcovski,“Image Segmentation by direct region subdivision”, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 1.
[15] X. Jiang, R. Zhang, S. Nie, “Image Segmentation Based on PDEs Model: a Survey”, IEEE conference, pp. 1-4, 2009.
[16] C. Zhu, J. Ni, Y. Li, G. Gu,“General Tendencies in Segmentation of Medical Ultrasound Images”, International Conference on ICICSE, pp. 113-117, 2009.
[17] V. K. Dehariya, S. K. Shrivastava, R. C. Jain,“Clustering of Image Data Set Using K-Means and Fuzzy K-Means Algorithms”, International conference on CICN, pp. 386- 391, 2010.
[18] F .Z. Kettaf, D. BI, J. P.,“A Comparison Study of Image Segmentation by Clustering Technique”, Proceedings of ICSP, pp. 1280-1282, 1996.
[19] S. Naz, H. Majeed, H. Irshad, “Image Segmentation using Fuzzy Clustering: A Survey”, International Conference on ICET, pp.181-186, 2010.
[20] S.Tatiraju, A. Mehta,“Image Segmentation using k-means clustering, EM and Normalized Cuts”, Department of EECS, pp. 1-7.
[21] B. C. Wei, R. Mandava,“Multi–objective Nature-inspired Clustering Techniques for Image Segmentation”, IEEE Conference on CIS, pp.150-155, 2010.
Keywords Energy function, image segmentation, perceptual organization.