Constraint satisfaction neural networks for image segmentation

Lin Wei-Chung Lin*, Chen-Kuo Tsao Eric Chen-Kuo Tsao, Chen Chin-Tu Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


Image segmentation is a process to divide an image into segments with uniform and homogeneous attributes such as graytone or texture. An image segmentation problem can be casted as a Constraint Satisfaction Problem (CSP) by interpreting the process as one of assigning labels to pixels subject to certain spatial constraints. A class of Constraint Satisfaction Neural Networks (CSNNs), different from the conventional algorithms, is proposed for image segmentation. In the network, each neuron represents one possible label of an object in a CSP and the interconnections between the neurons constitutes the constraints. In the context of image segmentation, each pixel in an n × n image can be considered as an object, i.e. there are n2 objects in the CSP. Suppose that each object is to be assigned one of m labels. Then, the CSNN consists of n × n × m neurons which can be conceived as a three-dimensional (3D) array. The connections and the topology of the CSNN are used to represent the constraints in a CSP. The initial condition for this network is set up by Kohonen's self-organizing feature map. The mechanism of the CSNN is to find a solution that satisfies all the constraints in order to achieve a global consistency. The final solution outlines segmented areas and simultaneously satisfies the given constraints. From our extensive experiments, the results show that this CSNN method is a very promising approach for image segmentation. Due to its network structure, it lends itself admirably to parallel implementation and is potentially faster than conventional image segmentation algorithms.

Original languageEnglish (US)
Pages (from-to)679-693
Number of pages15
JournalPattern Recognition
Issue number7
StatePublished - Jul 1992


  • Constraint satisfaction problem
  • Image segmentation
  • Neural networks

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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