The week's pick
Random Articles
Reseach Article
Improved Stochastic Random Walker Segmentation based on Gaussian Filtering
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 81 - Number 2 |
| Year of Publication: 2013 |
| Authors: Yogendra Kumar Jain, Nitin Kumar Patel |
10.5120/13987-1998
|
Yogendra Kumar Jain, Nitin Kumar Patel . Improved Stochastic Random Walker Segmentation based on Gaussian Filtering. International Journal of Computer Applications. 81, 2 ( November 2013), 32-36. DOI=10.5120/13987-1998
Abstract
Image segmentation is the process to capture the object from the background and it is a difficult task when a vision of the object is in stochastic region. Here introduce in this paper extension of stochastic random walker segmentation method. In stochastic random walker segmentation, a weighted graph is built from the image, where the each pixel considered as a node and edge weights depend on the image gradient between the pixels. For given seed regions, the probability are evaluated for a stochastic random walk on this graph starting at a pixel to end in one of the seed regions. The problem associated with existing method is the number of random variable (gray-level value in random order) in stochastic images. These random variables increase the graph sizes of stochastic images. If the graph size will increase, consequently execution time would also increase. To overcome these problems, the proposed "Improved stochastic random walker segmentation based on Gaussian filtering" for stochastic image segmentation. In proposed method Gaussian filter has been used for the removal of uncertain gray level and which in turn reduce the noise level and the resultant graph size of corresponding stochastic image, then apply stochastic random walker segmentation method which may help to decrease the execution time of the segmentation process.
References
- Sener Ozan, Ugur Kemal an Ayd?n Alatan A. 2012. Error-tolerant Interactive Image Segmentation using Dynamic and Iterated Graph-Cuts. Proceedings of the 2nd ACM international workshop on Interactive multimedia on mobile and portable devices, pp. 9- 16.
- Grady L. 2006. Random walks for image segmentation. IEEE Transaction. Pattern Anal. Mach. Intell. Vol14-No11, pp. 1768–1783.
- Pätz Torben and Preusser Tobias. Segmentation of Stochastic Images with a Stochastic Random Walker Method. 2012. IEEE Transactions on Image Processing. Vol21-No5, pp. 2424 – 2433.
- Li Y. , Sun J. , Tang C. -K. , and Shum H. -Y. 2004. Lazy snapping. ACM Transactions on Graphics (TOG). vol. 23 issue. 3, pp. 303-308.
- Calderero F. and Marques F. 2010. Region merging techniques using information theory statistical measures. IEEE Transactions on Image Processing. vol 19. issue. 6, pp. 1567–86.
- Liu D. , Pulli K. , Shapiro L. G. , and Xiong Y. 2010. Fast interactive image segmentation by discriminative clustering. Proceedings in ACM multimedia workshop on Mobile cloud media computing. pp. 47-52.
- Liu D. , Xiong Y. , Shapiro L. G. , and Pulli K. 2009. Robust interactive image segmentation with automatic boundary refinement. in ICIP, pp. 225–228.
- Xue Ya, Liao Xuejun and Carin Lawrence. 2007. Multi-Task Learning for Classification with Dirichlet Process Priors, Journal of Machine Learning Research vol. 8, pp. 35 – 63.
- Boykov Y. and Jolly M. -P. 2001. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In ICCV, pp. 105–112.
- Eriksson Anders P. , Barr Olof and Astrom Kalle . Image Segmentation Using Minimal Graph Cuts. Proceedings in CiteSeerX International Journal of Image Processing (IJIP).
- Ito K. 2000. Gaussian filters for nonlinear filtering problems Automatic Control. IEEE Transactions on vol. 45, issue. 5 pp. 910 – 927.
- Preusser T. , Scharr H. , Krajsek K. and Kirby R. M. 2008. Building blocks for computer vision with stochastic partial differential equations. Int J. Comput. Vis. , Vol80-No3, pp. 375–405.
- Pätz T. and Preusser T. 2010. Ambrosio–Tortorelli segmentation of stochastic images. Proceeding in ECCV. vol. 6315, pp. 254–267.
- Debusschere B. J. , Najm H. N. , Pébay P. P. , Knio O. M. , Ghanem R. G. , and Le Ma?tre O. P. 2005. Numerical challenges in the use of polynomial chaos representations for stochastic processes. SIAM J. Sci. Comput. , Vol26-No. 2, pp. 698–719.
- Nouy A. 2007. A generalized spectral decomposition technique to solve a class of linear stochastic partial differential equations. Comput. Methods Appl. Mech. Eng. , Vol196-No. 45–48, pp. 4521–4537.
- Canny John. 1986. A computational approach to edge detection. Pattern Analysis and Machine Intelligence. IEEE Transactions on PAMI-8, pp. 679–698.
Index Terms
Keywords