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Abstract

This paper describes an extension to the Haar Classifier Cascade technique for object detection. Existing Haar Classifier Cascades are binary; the extension adds confidence measurement. This confidence measure was implemented and found to improve accuracy on two object detection problems: face detection and fish detection. For fish detection, the problem of selecting positive training-sample angle-ranges was also considered; results showed that large random variations that result in cascades covering overlapping ranges increases their accuracy.

Item Type:	Book Section
Authors/Creators:	Horton, M and Cameron-Jones, RM and Williams, RN
Journal or Publication Title:	AI 2007: Advances in Artificial Intelligence. Proceedings of the 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2-6, 2007
Publisher:	Springer Berlin
DOI / ID Number:	https://doi.org/10.1007/978-3-540-76928-6
Additional Information:	The original publication is available at www.springerlink.com
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