29 October 1996 Comparison of supervised learning techniques applied to color segmentation of fruit images
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This paper describes the use of color segmentation to assist the detection of blemishes and other defects on fruit. It discusses the advantages and disadvantages of different color spaces including RGB and HSI and different supervised learning techniques including maximum likelihood, nearest neighbor and neural networks. It then compares the performance of various combinations of these on the same training and test set. A selection of images segmented by the best combination is presented and conclusions made.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
P. Wayne Power, P. Wayne Power, Roger S. Clist, Roger S. Clist, } "Comparison of supervised learning techniques applied to color segmentation of fruit images", Proc. SPIE 2904, Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling, (29 October 1996); doi: 10.1117/12.256294; https://doi.org/10.1117/12.256294


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