Research on Image Segmentation Methods of Tomato in Natural Conditions
It is difficult for image segmentation of harvesting objects in natural conditions because of the large range of illuminance variation. In order to improve the performance of adaptation to illuminace variation of image segmentation algorithm, three image segmentation algorithms are researched and comparatively analyzed: segmentation algorithm based on R-G, segmentation algorithm based on normalized RG, and segmentation algorithm based on band ratio. Experiments showed that segmentation method based on R-G could not meet the need of image segmentation of tomato in natural lighting conditions, but segmentation method based on normalized R-G and band ratio could. Light spot of tomato region couldnt be recognized in front lighting conditions using the second method. In the third method, light spots recognition could be realized, although error segmentation would happen in some conditions. The segmentation efficiency of method based on normalization chromatic aberration was better than others in weak lighting conditions, the method based on multiband ratio was better than others in front lighting conditions.
natural condtions tomato image segmentation normalized chromatic aberration band ratio
Rong Xiang Yibin Ying Huanyu Jiang Rong Xiang
College of Biosystems Engineering and Food Science Zhejiang University Hangzhou, China College of Quality and Safety Engineering China Jiliang University Hangzhou, China
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
1289-1293
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）