Multithresholding of color and gray-level images through a neural network technique
| Autor: | Nikos Papamarkos, C. Strouthopoulos, Ioannis Andreadis |
|---|---|
| Rok vydání: | 2000 |
| Předmět: |
Self-organizing map
Speedup Artificial neural network Computer science business.industry Pattern recognition Image processing Thresholding Gray level Feature (computer vision) Signal Processing Principal component analysis Computer vision Computer Vision and Pattern Recognition Artificial intelligence business |
| Zdroj: | Image and Vision Computing. 18:213-222 |
| ISSN: | 0262-8856 |
| DOI: | 10.1016/s0262-8856(99)00015-3 |
| Popis: | 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. q 2000 Elsevier Science B.V. All rights reserved. |
| Databáze: | OpenAIRE |
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