Color image segmentation with genetic algorithm in a raisin sorting system based on machine vision in variable conditions

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.