The week's pick
Reseach Article
Color Image Enhancement with Different Image Segmentation Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 8 |
| Year of Publication: 2019 |
| Authors: Rupali B. Nirgude |
10.5120/ijca2019918790
|
Rupali B. Nirgude . Color Image Enhancement with Different Image Segmentation Techniques. International Journal of Computer Applications. 178, 8 ( May 2019), 36-40. DOI=10.5120/ijca2019918790
Abstract
Image can be improved using different image enhancement techniques. It includes different types of operations like image segmentation, clustering, smoothing, etc. Basically in the process of Image segmentation features having homogenous characteristics are identified. The Input for the system is color image. The image gets converted into horizontal and vertical shape histogram. Then cluster formation is done using hill climbing technique and k means clustering. K Means clustering consider colour intensity as criteria. Sequential probability ratio test checks similar characteristics between different regions. Merging of these regions follows dynamic region merging algorithm. Depending on similar properties partitions are merged. The output is enhanced segmented image. Nearest neighbour graph technique is helpful to speed up the above process. This improved image is useful in the field of medical as well as security purpose.
References
- D. A. Forsyth and J. Ponce, Computer Vision: A Modern Approach. Englewood Cliffs, NJ: Prentice-Hall, 2002.
- L. Ladicky, C. Russell, P. Kohli, and P. Torr, “Associative hierarchical CRFs for object class image segmentation,” in Proc. ICCV, 2009, pp.
- J. Canny, “A computational approach to edge detection,” IEEE Trans.Pattern Anal. Mach. Intell., vol. PAMI-8, no. 6, pp. 679–698, Nov.1986739–746.
- R. C. Gonzalez and R. Elwood, Digital Image Processing. Reading, MA: Addison-Wesley, 1992.
- L. Zhang and B. Paul, “Edge detection by scale multiplication in wavelet domain,” Pattern Recognition. Lett., vol. 23, no. 14, pp.1771–1784, Dec. 2002.
- B. Paul, L. Zhang, and X.Wu, “Canny edge detection enhancement byscale multiplication,” IEEE. Trans. Pattern Anal. Mach. Intell., vol. 27, no. 9, pp. 1485–1490, Sept. 2005.
- Dorin Comaniciu, Peter Meer, “Mean Shift Analysis and Applications”.
- S. Thilagamani1 and N. Shanthi, “A Survey on Image Segmentation Through Clustering”, International Journal of Research and Reviews in Information Sciences Vol. 1, No.
- F. Lecumberry, A. Pardo, and G. Sapiro, “Simultaneous object classification and segmentation with high-order Wesley, 1992.multiple shape models”, IEEE Trans. Image Process., vol. 19, no. 3, pp. 625–635, Mar. 2010.
- R. C. Gonzalez and R. E. Woods, Digital Image Processing. Reading, MA: Addison
- National Programs on Technology Enhanced learning http://nptel.iitm.ac.in/courses/106105032/39,
- R. Nock and F. Nielsen, “Statistic region merging,” IEEE Trans. PatternAnal. Mach. Intell., vol. 26, no. 11, pp. 1452–1458, Nov. 2004.
- K. Haris, S. N. Estradiadis, N. Maglaveras, and A. K. Katsaggelos,“Hybrid image segmentation using watersheds and fast regionmerging,” IEEE Trans. Image Process., vol. 7, no. 12, pp. 1684–1699,Dec. 1998.
- P. F. Felzenszwalb and D. P. Huttenlocher, “Efficient graph-basedimage segmentation,” Int. J. Computed. Vis., vol. 59, no. 2, pp. 167–181,Sep. 2004.
- B. Peng, L. Zhang, and J. Yang, “Iterated graph cuts for image segmentation, ”in Proc. Asian Conf. computed. Vis., 2009, pp. 677–686
- D. Comanicu and P. Meer, “Mean shift: A robust approach toward featurespace analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603–619, May 2002.
- D. Eppstein, M. S. Paterson, and F. F. Yao, “On nearest-neighborgraphs,” Discr. Comput. Geom., vol. 17, no. 3, pp. 263–282, Apr.1997.
- A. Wald, Sequential Analysis, 3rd ed. Hoboken, NJ: Wiley, 1947.
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