Image segmentation based on multi-scan constraint satisfaction neural network

Abstract A novel image segmentation method based on a constraint satisfaction neural network (CSNN) is presented. The new method uses CSNN-based relaxation but with a modified scanning scheme of the image. The pixels are visited with more distant intervals and wider neighborhoods in the first level of the algorithm. The intervals between pixels and their neighborhoods are reduced in the following stages of the algorithm. This method contributes to the formation of more regular segments rapidly and consistently. A cluster validity index to determine the number of segments is also added to complete the proposed method into a fully automatic unsupervised segmentation scheme. The results are compared quantitatively by means of a novel segmentation evaluation criterion. The results are promising.

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

[2]  Sankar K. Pal,et al.  Self-organization for object extraction using a multilayer neural network and fuzziness measures , 1993, IEEE Trans. Fuzzy Syst..

[3]  Sankar K. Pal,et al.  Fuzzy models for pattern recognition , 1992 .

[4]  Fatih Kurugollu,et al.  Image Segmentation Based on Boundary Constraint Neural Network , 1996 .

[5]  James C. Bezdek,et al.  Fuzzy Kohonen clustering networks , 1994, Pattern Recognit..

[6]  Antanas Verikas,et al.  Colour image segmentation by modular neural network , 1997, Pattern Recognit. Lett..

[7]  Azriel Rosenfeld,et al.  Digital Picture Processing , 1976 .

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

[9]  Santanu Chaudhury,et al.  A connectionist approach for gray level image segmentation , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

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

[11]  Chin-Tu Chen,et al.  Constraint satisfaction neural networks for image recognition , 1993, Pattern Recognit..

[12]  Michael A. Arbib,et al.  Color Image Segmentation using Competitive Learning , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Shyang Chang,et al.  A new criterion for automatic multilevel thresholding , 1995, IEEE Trans. Image Process..

[14]  Jun Zhang,et al.  A Model-Fitting Approach to Cluster Validation with Application to Stochastic Model-Based Image Segmentation , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Bayya Yegnanarayana,et al.  Unsupervised texture classification using vector quantization and deterministic relaxation neural network , 1997, IEEE Trans. Image Process..

[16]  Esther M. Arkin,et al.  An efficiently computable metric for comparing polygonal shapes , 1991, SODA '90.

[17]  William A. Yasnoff,et al.  Error measures for scene segmentation , 1977, Pattern Recognit..

[18]  Berthold K. P. Horn.  Robot vision , 1986, MIT electrical engineering and computer science series.

[19]  James C. Bezdek,et al.  Partially supervised clustering for image segmentation , 1996, Pattern Recognit..

[20]  Thomas S. Huang,et al.  Image processing , 1971 .

[21]  Ling-Hwei Chen,et al.  Random-sampling thresholding: A new approach to multilevel thresholding , 1993, Signal Process..

[22]  Steven M. LaValle,et al.  A Framework for Constructing Probability Distributions on the Space of Image Segmentations , 1995, Comput. Vis. Image Underst..

[23]  Bülent Sankur,et al.  Color Cell Image Segmentation Using Pyramidal Constraint Satisfaction Neural Network , 1998, MVA.