Segmentation into fuzzy regions using topographic distance

This paper exposes an algorithm which leads to a fuzzy segmentation. This algorithm performs, as in the watershed method, a progressive flood of the gradient image from pixels of lowest gradients. It uses a new distance, called topographic distance. Any local minimum of the gradient norm image constitutes a seed for the region growing, avoiding the use of a marker image. These seeds constitute the cores of the initial fuzzy regions. Then the sites are gradually agglomerated to the region, while their membership degrees to the region decrease, according to the distance to the core and to the gradient norms, by the way of the topographic distance. The numerous fuzzy regions are then merged and the membership degrees of pixels to final regions are computed. Applications concern crisp segmentation of colour or gray scale images and pattern recognition from fuzzy regions.

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

[2]  Manfred Glesner,et al.  Fuzzy segmented image coding using orthonormal bases and derivative chain coding , 1999, Pattern Recognit..

[3]  Enrique H. Ruspini,et al.  A New Approach to Clustering , 1969, Inf. Control..

[4]  Heng-Da Cheng,et al.  Fuzzy partition of two-dimensional histogram and its application to thresholding , 1999, Pattern Recognit..

[5]  Nozha Boujemaa,et al.  Fuzzy iterative image segmentation with recursive merging , 1992, Other Conferences.

[6]  Nikolaos G. Bourbakis,et al.  A fuzzy region growing approach for segmentation of color images , 1997, Pattern Recognit..

[7]  Gilles Bertrand,et al.  Segmentation of microscopic images by flooding simulation: a catchment-basins merging algorithm , 1997, Electronic Imaging.

[8]  Azriel Rosenfeld,et al.  The fuzzy geometry of image subsets , 1984, Pattern Recognit. Lett..

[9]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

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

[11]  James M. Keller,et al.  Fuzzy Models and Algorithms for Pattern Recognition and Image Processing , 1999 .

[12]  P. Bolon,et al.  Analyse d'images: filtrage et segmentation , 1995 .

[13]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[14]  Jun Shen,et al.  An optimal linear operator for step edge detection , 1992, CVGIP Graph. Model. Image Process..

[15]  Silvano Di Zenzo,et al.  A note on the gradient of a multi-image , 1986, Comput. Vis. Graph. Image Process..