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Multiple classifier object detection with confidence measures.

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Horton, M, Cameron-Jones, RM and Williams, RN 2007 , 'Multiple classifier object detection with confidence measures.', in JG Carbonell and J Siekmann (eds.), AI 2007: Advances in Artificial Intelligence. Proceedings of the 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2-6, 2007 , Lecture Notes in Computer Science, vol. 4830/2 (4830/2) , Springer Berlin, Heidelberg, pp. 559-568.

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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: 10.1007/978-3-540-76928-6
Additional Information:

The original publication is available at www.springerlink.com

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