The week's pick
Random Articles
Reseach Article
Image Segmentation using Weighted Average Local Histogram
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 64 - Number 19 |
| Year of Publication: 2013 |
| Authors: Imran Hassan, Abrar Hussain |
10.5120/10745-5598
|
Imran Hassan, Abrar Hussain . Image Segmentation using Weighted Average Local Histogram. International Journal of Computer Applications. 64, 19 ( February 2013), 37-41. DOI=10.5120/10745-5598
Abstract
The prime objective of this paper is to implement an efficient improved color image segmentation method using local histogram and region merging technique. The goal of image segmentation is to cluster pixels into salient image regions, i. e. regions corresponding to individual surfaces, objects or natural parts of objects. Segmentation can be used for object recognition, occlusion boundary estimation within motion or stereo systems, image compression, image editing or image database look-up. Usually image segmentation is an initial and vital step in a series of processes aimed at overall image understanding. There are various techniques for image segmentation. In this research paper, a thorough work has done on the average local histogram of three different color spaces RGB, HSV & Lab. After that k-means clustering and labeling have done on the image for final segmentation.
References
- Kanchan Deshmukh, Ganesh Shinde "Adaptive Color Image Segmentation Using Fuzzy Min-Max Clustering"
- James C. Tilton (PI), Giovanni Marchisio (Co-I) and Mihai Datcu (Co-I), "Knowledge Discovery and Data Mining Based on Hierarchical Segmentation of Image Data, " a research proposal submitted October 23, 2000 in response to NRA2-37143 from NASA's Information Systems Program.
- P. Suetens, P. Fua, and A. J. Hanson, "Computational strategies for object recognition", ACM Comput. Surv. , vol. 24, pp. 5–61, Mar. 1992.
- P. Besl and R. Jain, "Three-dimensional object recognition," ACM Comput. Surv. , vol. 17, pp. 75–145, Mar. 1985.
- K. Hohne, H. Fuchs, and S. Pizer, 3D Imaging in Medicine: Algorithms, Systems, Applications. Berlin, Germany: Springer-Verlag, 1990
- M. Bomans, K. Hohne, U. Tiede, and M. Riemer, "3-D segmentation of MR images of the head for 3-D display," IEEE Trans. Med. Imag. , vol. 9, pp. 253–277, June 1990.
- P. Willemin, T. Reed, and M. Kunt, "Image sequence coding by split and merge," IEEE Trans. Commun. , vol. 39, pp. 1845–1855, Dec. 1991.
- F. D. Natale, G. Desoli, D. Giusto, and G. Vernazza, "Polynomial approximation and vector quantization: A region-based integration," IEEE Trans. Commun. , vol. 43, 1995
- K. S. Fu, J. K. Mui,"A Survey on image segmentation". Pattern Recognition.
- Z. Wu and R. Leahy. An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 11, pages 1101-1113, November 1993.
Index Terms
Keywords