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Proceedings Paper

Image screening using spatial filters based on linear transformation for image recognition
Author(s): Koichi Arimura; Norihiro Hagita
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Paper Abstract

Statistical image recognition methods based on linear transformation require a lot of calculation of correlation between subimages and reference patterns of the specified objects to be detected. Image screening provides an effective preprocessing method for lowering the calculation load and improving recognition accuracy. It selects candidate subimages that are similar to the detecting objects in images and rejects the remainders using spatial filters which are based on linear transformation. We have already investigated the spatial filters that are based on 2D projection pursuit (PP). PP requires more heavy calculation load than the principal components analysis (PCA). We, therefore, compare spatial filters based on two kinds of linear transformation algorithms, the PP and PCA, in terms of recognition accuracy and efficiency. Experiments are made for two object detection tasks: eye- and mouth-area detection in face images and text-area detection in document images. The results show that PCA-based image screening is superior to PP-based one for the eye- and mouth-area detection. PCA also achieves higher recognition rate (75%) than PP for the eye- and mouth-area detection, while PP offers equal performance in text-area detection. The results suggest that PCA is totally superior to PP in image screening.

Paper Details

Date Published: 27 March 1995
PDF: 9 pages
Proc. SPIE 2423, Machine Vision Applications in Industrial Inspection III, (27 March 1995); doi: 10.1117/12.205509
Show Author Affiliations
Koichi Arimura, NTT Basic Research Labs. (Japan)
Norihiro Hagita, NTT Basic Research Labs. (Japan)


Published in SPIE Proceedings Vol. 2423:
Machine Vision Applications in Industrial Inspection III
Frederick Y. Wu; Stephen S. Wilson, Editor(s)

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