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 images. A new technique suitable for segmenting both gray-level and color images 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 multithresholding algorithm and reduce the memory requirements, a sub-sampling technique can be used. Several experimental and comparative results exhibiting the performance of the proposed technique are presented. 2000 Elsevier Science B.V. All rights reserved.

• Type: Journal
• Year: 2000
• Where: IVC
• Authors: Nikos Papamarkos, Charalambos Strouthopoulos, Ioannis Andreadis
• Keywords: Entire Multithresholding Algorithm, Images, IVC 2000, Principal Component Analyzer