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Proceedings Paper

Comparison of supervised learning techniques applied to color segmentation of fruit images
Author(s): P. Wayne Power; Roger S. Clist
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Paper 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.

Paper Details

Date Published: 29 October 1996
PDF: 12 pages
Proc. SPIE 2904, Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling, (29 October 1996); doi: 10.1117/12.256294
Show Author Affiliations
P. Wayne Power, Industrial Research Ltd. (New Zealand)
Roger S. Clist, Industrial Research Ltd. (New Zealand)


Published in SPIE Proceedings Vol. 2904:
Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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