Multithresholding of color and gray-level images through a neural network technique

One of the most frequently used methods in image processing is thresholding . This can be a highly efficient means of aiding the interpretation of imag es; A new technique suitable for segmenting both gray-level and color image s is presented in this paper. The proposed approach is a multithresholding technique implemented by a Principal Component Analyzer (PCA) and a Kohonen Self-Organized Feature Map (SOFM) neural network. To speedup the entire mu ltithresholding algorithm and reduce the memory requirements, a sub-samplin g technique can be used. Several experimental and comparative results exhib iting the performance of the proposed technique are presented. (C) 2000 Els evier Science B.V. All rights reserved.