Image segmentation by relaxation using constraint satisfaction neural network
✍ Scribed by Fatih Kurugollu; Bülent Sankur; A. Emre Harmancı
- Publisher
- Elsevier Science
- Year
- 2002
- Tongue
- English
- Weight
- 814 KB
- Volume
- 20
- Category
- Article
- ISSN
- 0262-8856
No coin nor oath required. For personal study only.
✦ Synopsis
The problem of image segmentation using constraint satisfaction neural networks (CSNN) has been considered. Several variations of the CSNN theme have been advanced to improve its performance or to explore new structures. These new segmentation algorithms are based on interplay of additional constraints, of varying the organization of the network or modifying the relaxation scheme. The proposed schemes are tested comparatively on a bank of test images as well as real world images.
📜 SIMILAR VOLUMES
We address the problem of solving a constraint satisfaction problem (CSP) by treating a constraint logic program (CLP) as a network of constraints. We attempt to show that each computation in a CLP becomes a sequence of linear steps, since the check satisfiability of the system of constraints is app
This article describes the application of a neural network to the segmentation of remote sensing images of multispectral SPOT and fully polarimetric SAR data. The structure of the network is a modified multilayer perceptron and is trained by the Kalman filter theory. The internal activity of the net
Region segmentation of images is a well-known illposed problem, and a specific algorithm-like regularization seems to be available. In this paper, an active-region segmentation algorithm based on a regularization approach using the Hopfield neural network is proposed. Pyramid images are used to avoi
In the paper a new hardware architecture for the implementation of a high-speed, low bit-rate image coding system is outlined. Our proposed algorithm is based on the Cellular Neural/Nonlinear Network (CNN) chip-set. A simple and fast method is introduced to generate basis functions of two-dimensiona