Preserving objects in Markov Random Fields region growing image segmentation

This paper proposes an algorithm that preserves objects in Markov Random Fields (MRF) region growing based image segmentation. This is achieved by modifying the MRF energy minimization process so that it would penalize merging regions that have real edges in the boundary between them. Experimental results show that the integration of edge information increases the precision of the segmentation by ensuring the conservation of the objects contours during the region-growing process.

[1]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[2]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[3]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[4]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[5]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Anthony J. Yezzi,et al.  A Fully Global Approach to Image Segmentation via Coupled Curve Evolution Equations , 2002, J. Vis. Commun. Image Represent..

[7]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Stan Z. Li,et al.  Markov Random Field Modeling in Image Analysis , 2001, Computer Science Workbench.

[9]  Fernand Meyer,et al.  Topographic distance and watershed lines , 1994, Signal Process..

[10]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[11]  Haluk Derin,et al.  Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Martial Hebert,et al.  Discriminative Random Fields , 2006, International Journal of Computer Vision.

[13]  Aggelos K. Katsaggelos,et al.  Hybrid image segmentation using watersheds and fast region merging , 1998, IEEE Trans. Image Process..

[14]  Chee Sun Won,et al.  Unsupervised segmentation of noisy and textured images using Markov random fields , 1992, CVGIP Graph. Model. Image Process..

[15]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[16]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Marcel Worring,et al.  Watersnakes: Energy-Driven Watershed Segmentation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Jun Zhang.  The mean field theory in EM procedures for Markov random fields , 1992, IEEE Trans. Signal Process..

[20]  B. S. Manjunath,et al.  EdgeFlow: a technique for boundary detection and image segmentation , 2000, IEEE Trans. Image Process..

[21]  David A. Clausi,et al.  IRGS: Image Segmentation Using Edge Penalties and Region Growing , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Philippe Andrey,et al.  Unsupervised Segmentation of Markov Random Field Modeled Textured Images Using Selectionist Relaxation , 1998, IEEE Trans. Pattern Anal. Mach. Intell..