NDLI: A comparison of PCA and ICA for object recognition under varying illumination

Content Provider	IEEE Xplore Digital Library
Author	Fortuna, J. Schuurman, D. Capson, D.
Copyright Year	2002
Description	Author affiliation: Dept. of Electr. & Comput. Eng., McMaster Univ., Hamilton, Ont., Canada (Fortuna, J.; Schuurman, D.; Capson, D.)
Abstract	An experiment is performed to evaluate the ability of two different subspace methods to recognize objects under different illumination conditions. The principal component analysis (PCA) and independent component analysis (ICA) are compared for classifying 25 different objects with varying degrees of specularity under different illumination. Each object was sampled under three widely different lighting conditions to form a set of training images used to create subspaces with dimensions ranging from 10 to 30 basis vectors. The efficacy of ICA and PCA to correctly classify the objects was tested using two test images for each object under unique lighting conditions not included in the training set. The results were also determined when the images were pre-filtered with a Laplacian of Gaussian filter. Results show that ICA techniques show promise for object recognition under varying illumination conditions.
Starting Page	11
Ending Page	15
File Size	317673
Page Count	5
File Format	PDF
ISBN	076951695X
ISSN	10514651
DOI	10.1109/ICPR.2002.1047783
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-08-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Independent component analysis Object recognition Lighting Decorrelation Testing Face recognition Computer vision Performance evaluation Gaussian processes
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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