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Multiple classivvÖer object detection with convvÖdence measures.

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posted on 2023-05-28, 00:47 authored by Horton, M, Cameron-Jones, RM, Williams, RN
This paper describes an extension to the Haar ClassivvÖer Cascade technique for object detection. Existing Haar ClassivvÖer Cascades are binary; the extension adds convvÖdence measurement. This convvÖdence measure was implemented and found to improve accuracy on two object detection problems: face detection and vvÖsh detection. For vvÖsh 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.

History

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

Series

Lecture Notes in Computer Science

Volume

4830/2

Number

4830/2

Pagination

559-568

ISBN

978-3-540-76926-2

Publisher

Springer Berlin

Publication status

  • Published

Place of publication

Heidelberg

Event title

20th Australasian Joint Conference on Artificial Intelligence

Event Venue

Hobart, Tasmania

Date of Event (Start Date)

2007-12-02

Date of Event (End Date)

2007-12-06

Rights statement

The original publication is available at www.springerlink.com

Repository Status

  • Restricted

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