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Proceedings Paper

Object detection using independent local feature extractor
Author(s): Ryouta Nakano; Kazuhiro Hotta; Haruhisa Takahashi
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Paper Abstract

This paper presents an object detection method using independent local feature extractor. In general, it can be considered that objects are the combination of characteristic parts. Therefore, if local parts specialized for recognition target are obtained automatically from training samples, it is expected that good object detector is developed. For this purpose, we use Independent Component Analysis (ICA) which decomposes a signal into independent elementary signals. The basis vectors obtained by ICA are used as independent local feature extractors specified for detection target. The feature extractors are applied to candidate region, and their outputs are used in classification. However, the extracted features are independent local features. Therefore the relative information between neighboring positions of independent features may be more effective for object detection than simple independent features. To extract the relative information, higher order local autocorrelation features are used. To classify detection target and non-target, we use Support Vector Machine which is known as binary classifier. The proposed method is applied to car detection problem. Superior results are obtained by comparison with Principal Component Analysis.

Paper Details

Date Published: 6 December 2005
PDF: 8 pages
Proc. SPIE 6051, Optomechatronic Machine Vision, 605106 (6 December 2005); doi: 10.1117/12.647967
Show Author Affiliations
Ryouta Nakano, Univ. of Electro-Communications (Japan)
Kazuhiro Hotta, Univ. of Electro-Communications (Japan)
Haruhisa Takahashi, Univ. of Electro-Communications (Japan)

Published in SPIE Proceedings Vol. 6051:
Optomechatronic Machine Vision
Kazuhiko Sumi, Editor(s)

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