## Abstract

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 n^{2} 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 language | English (US) |
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Pages (from-to) | 679-693 |

Number of pages | 15 |

Journal | Pattern Recognition |

Volume | 25 |

Issue number | 7 |

DOIs | |

State | Published - Jan 1 1992 |

## Keywords

- Constraint satisfaction problem
- Image segmentation
- Neural networks

## ASJC Scopus subject areas

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