Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Elements of information theory
Elements of information theory
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Adaptive image contrast enhancement using generalizations of histogram equalization
IEEE Transactions on Image Processing
Computationally efficient algorithm for fuzzy rule-based enhancement on JPEG compressed color images
WSEAS Transactions on Signal Processing
Evaluating retina image fusion based on quantitative approaches
WSEAS Transactions on Computers
Maximum entropy method and underdetermined systems applied to computer network topology and routing
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
An algorithm for the network design problem based on the maximum entropy method
AMERICAN-MATH'10 Proceedings of the 2010 American conference on Applied mathematics
WSEAS Transactions on Signal Processing
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Image enhancement and image clustering are two practical implementation approaches for pattern recognition with a variety of engineering applications. In most cases, the actual outcomes of some advanced image processing approaches will directly affect the decision making, such as in target detection and medical diagnosis. Among these approaches, image adaptive contrast stretching is a typical enhancement approach under conditions of improper illumination and unpleasant disturbances, which adapts to the intensity distribution of an image. K-means clustering is a typical segmentation approach to minimize the medium dispersing impact, which produces the distinctive clusters or layers for representing different components of the information being detected. In trimulus color systems, each of three color components takes an independent role along with image processing procedures. To evaluate actual effects of image enhancement and image segmentation, quantitative measures should be taken into account rather than qualitative evaluations exclusively. In this article, quantitative measures for trimulus color systems are proposed instead of the existing gray level ones. Considering the gray level image measures, the corresponding true color RGB component energy, discrete entropy, relative entropy and mutual information are proposed to measure the effectiveness of color image enhancement and segmentation techniques.