Comparison of supervised learning techniques applied to color segmentation of fruit images

P. Wayne Power; Roger S. Clist

doi:10.1117/12.256294

29 October 1996 Comparison of supervised learning techniques applied to color segmentation of fruit images

P. Wayne Power, Roger S. Clist

Proceedings Volume 2904, Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling; (1996) https://doi.org/10.1117/12.256294
Event: Photonics East '96, 1996, Boston, MA, United States

Abstract

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.

Citation Download Citation

P. Wayne Power and 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); https://doi.org/10.1117/12.256294

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