Visual information from anisotropic transformations

Loss of information in images undergoing fine-to-coarse image transformations is analyzed by using an approach based on the theory of irreversible transformations. It is shown that entropy variation along scales can be used to characterize basic, low-level information and to gauge essential perceptual components of the image. The method is extended to the case of anisotropic diffusion and an algorithm based on entropy variation is presented that extracts relevant features of the image, showing in particular how to discriminate between smooth, textured and edge-type regions.

[1]  Brendan McCane,et al.  Multi-scale adaptive segmentation using edge and region based attributes , 1997, Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES '97.

[2]  A. Rosenfeld,et al.  Edge and Curve Detection for Visual Scene Analysis , 1971, IEEE Transactions on Computers.

[3]  Michael C. Mackey,et al.  The dynamic origin of increasing entropy , 1989 .

[4]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[6]  Rama Chellappa,et al.  A unified approach to boundary perception: edges, textures, and illusory contours , 1993, IEEE Trans. Neural Networks.

[7]  Nariman Farvardin,et al.  A perceptually motivated three-component image model-Part I: description of the model , 1995, IEEE Trans. Image Process..

[8]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[9]  Giuseppe Boccignone,et al.  Information properties in fine-to-coarse image transformations , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).