Texture segmentation via a diffusion-segmentation scheme in the Gabor feature space

We address the problem of texture segmentation in the context of the Gabor feature space of images. Gabor filters which are tuned to different orientations, scales and frequencies are applied to textured images to create the Gabor feature space. We regard the scale, orientation and frequency for which maximum response of the Gabor filters was obtained. A two-dimensional Riemannian manifold of local features is extracted via the Beltrami framework. The metric of this surface is a good indicator of texture changes and is used, therefore, in a Beltrami based diffusion mechanism and in a geodesic active contours algorithm for texture segmentation. The innovation of this work lies in using the metric of the feature manifold which integrates information from all the features, in both the diffusion and segmentation

[1]  Ron Kimmel,et al.  A general framework for low level vision , 1998, IEEE Trans. Image Process..

[2]  Jayant Shah.  Riemannian Drums, Anisotropic Curve Evolution and Segmentation , 1999, Scale-Space.

[3]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

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

[5]  Yehoshua Y. Zeevi,et al.  Gabor-Space Geodesic Active Contours , 2000, AFPAC.

[6]  Gerald Sommer,et al.  Algebraic Frames for the Perception-Action Cycle , 2000, Lecture Notes in Computer Science.

[7]  Guillermo Sapiro,et al.  Vector-valued active contours , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[9]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[10]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[11]  B. Julesz,et al.  Texton gradients: The texton theory revisited , 2004, Biological Cybernetics.

[12]  Ron Kimmel,et al.  Orientation Diffusion or How to Comb a Porcupine , 2002, J. Vis. Commun. Image Represent..

[13]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Yehoshua Y. Zeevi,et al.  Gabor Feature Space Diffusion via the Minimal Weighted Area Method , 2001, EMMCVPR.

[15]  Roman Goldenberg,et al.  Fast Geodesic Active Contours , 1999, Scale-Space.

[16]  Olivier D. Faugeras,et al.  Segmentation of Bone in Clinical Knee MRI Using Texture-Based Geodesic Active Contours , 1998, MICCAI.

[17]  Rachid Deriche,et al.  Geodesic active regions for supervised texture segmentation , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[18]  Song-Chun Zhu,et al.  Equivalence of Julesz Ensembles and FRAME Models , 2000, International Journal of Computer Vision.

[19]  Dennis Gabor,et al.  Theory of communication , 1946 .

[20]  R. Kimmel,et al.  On the Geometry of Texture , 1996 .

[21]  Yehoshua Y. Zeevi,et al.  The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Anthony J. Yezzi,et al.  Gradient flows and geometric active contour models , 1995, Proceedings of IEEE International Conference on Computer Vision.

[23]  S Marcelja,et al.  Mathematical description of the responses of simple cortical cells. , 1980, Journal of the Optical Society of America.

[24]  Jayant Shah.  Riemannian Drums, Anisotropic Curve Evolution, and Segmentation , 2000, J. Vis. Commun. Image Represent..

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

[26]  Anil K. Jain,et al.  Markov Random Field Texture Models , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.