Algorithms for clustering data
Algorithms for clustering data
Neurocomputing
An edge-based description of color images
CVGIP: Graphical Models and Image Processing
C4.5: programs for machine learning
C4.5: programs for machine learning
Floating search methods in feature selection
Pattern Recognition Letters
A region growing and merging algorithm to color segmentation
Pattern Recognition
Automatic watershed segmentation of randomly textured color images
IEEE Transactions on Image Processing
Comparing Combination Rules of Pairwise Neural Networks Classifiers
Neural Processing Letters
The feature extraction and analysis of flaw detection and classification in BGA gold-plating areas
Expert Systems with Applications: An International Journal
Graph-based tools for microscopic cellular image segmentation
Pattern Recognition
Two-Tier genetic programming: towards raw pixel-based image classification
Expert Systems with Applications: An International Journal
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This paper presents a color object recognition scheme which proceeds in three sequential steps: segmentation, features extraction and classification. We mainly focus on the first and the third steps here. A color watershed using global and local criteria is first described. A color contrast value is defined to select the best color space for segmenting color objects. Then, an architecture of binary neural networks is described. Its properties relies on the simplification of the recognition problem, leading to a noticeable increase in the classification rate. We conclude with the abilities of such a recognition scheme and present an automated cell sorting system.