Machine vision and digital image processing fundamentals
Machine vision and digital image processing fundamentals
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
Computer Vision and Image Understanding
Computers and Electronics in Agriculture
Comparing data mining classifiers for grading raisins based on visual features
Computers and Electronics in Agriculture
Expert Systems with Applications: An International Journal
Crop segmentation from images by morphology modeling in the CIE L*a*b* color space
Computers and Electronics in Agriculture
A sequential machine vision procedure for assessing paper impurities
Computers in Industry
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This study was undertaken to develop machine vision-based raisin detection technology for various lighting conditions. Supervised color image segmentation using a permutation-coded genetic algorithm (GA) identifying regions in hue-saturation-intensity (HSI) color space (GAHSI) for desired and undesired raisin detection in various conditions was successfully implemented. Images from two extreme intensity lighting and dense conditions: under weak lighting and high-density product and under suitable lighting and low-density product, were mosaicked to explore the possibility of using GAHSI to locate desired raisin and undesired raisin regions in color space when these two extremes were presented simultaneously. The GAHSI results provided evidence for the existence and separability of such regions. In the experiment, GAHSI performance was measured by comparing the GAHSI-segmented image with a corresponding hand-segmented reference image. When compared with cluster analysis-based segmentation results, the GAHSI method showed no significant difference.