Texture segmentation using a diffusion region growing technique

Abstract In this paper we consider the segmentation of textured images using a diffusion region growing method. This method is able to grow several separate (but similar) regions simultaneously, and is very well suited to parallel implementation. Recent research, both in computer and human vision, suggests the use of spatial/spatial-frequency (s/sf) image representations as the basis for such tasks. The representation we have used is an approximation to the Wigner distribution, an s/sf representation that has recently been suggested as corresponding to the early and preattentive stage of visual processing. Experimental results on both synthesized and natural textures are shown.

[1]  Harry Wechsler,et al.  Segmentation of Textured Images and Gestalt Organization Using Spatial/Spatial-Frequency Representations , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  H. Wechsler,et al.  Tracking of nonstationarities for texture fields , 1988 .

[3]  Phil Brodatz,et al.  Textures: A Photographic Album for Artists and Designers , 1966 .

[4]  Luc Van Gool,et al.  Texture analysis Anno 1983 , 1985, Comput. Vis. Graph. Image Process..

[5]  Harry Wechsler,et al.  Texture analysis — a survey , 1980 .

[6]  Andrew B. Watson,et al.  The cortex transform: rapid computation of simulated neural images , 1987 .

[7]  Azriel Rosenfeld,et al.  Frequency Domain Analysis and Synthesis of Image Pyramid Generating Kernels , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  E. Wigner.  On the quantum correction for thermodynamic equilibrium , 1932 .

[9]  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.

[10]  Harry Wechsler,et al.  A Theory for Invariant Object Recognition in the Frontoparallel Plane , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  D. Marr,et al.  An Information Processing Approach to Understanding the Visual Cortex , 1980 .

[12]  H. Wechsler,et al.  Joint spatial/spatial-frequency representation , 1988 .

[13]  Eitan M. Gurari,et al.  On the Difficulties Involved in the Segmentation of Pictures , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  T. Claasen,et al.  THE WIGNER DISTRIBUTION - A TOOL FOR TIME-FREQUENCY SIGNAL ANALYSIS , 1980 .

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

[16]  Jacob Beck,et al.  Spatial frequency channels and perceptual grouping in texture segregation , 1987, Comput. Vis. Graph. Image Process..

[17]  Harry Wechsler,et al.  A paradigm for invariant object recognition of brightness, optical flow and binocular disparity images , 1982, Pattern Recognit. Lett..

[18]  Harry Wechsler,et al.  Derivation of optical flow using a spatiotemporal-Frequency approach , 1987, Comput. Vis. Graph. Image Process..