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<meta content="Horton, Michael" name="eprints.creators_name" />
<meta content="Cameron-Jones, Mike" name="eprints.creators_name" />
<meta content="Williams, Raymond" name="eprints.creators_name" />
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<meta content="2007-12-14 03:47:03" name="eprints.datestamp" />
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<meta content="After this paper was submitted for publication, errors were found in the positive sample generation and in the OpenCV training algorithms. These errors made the results in section 4.2 invalid. This version of the paper contains the correct results, which differ from those in the conference proceedings.
The original publication is available at www.springerlink.com
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<meta content="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." name="eprints.abstract" />
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<meta content="1. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. Proc. IEEE International Conference on Computer Vision and Pattern Recognition 1 (2001) 511–518
2. Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. Proc. International Conference on Machine Learning 148 (1996) 156
3. Freund, Y., Schapire, R.: A short introduction to boosting. Journal of the Japanese Society for Artificial Intelligence 14(5) (1999) 771–780
4. Jones, M., Viola, P.: Fast multi-view face detection. Mitsubishi Electric Research Lab TR-20003-96 (2003)
5. K¨olsch, M., Turk, M.: Analysis of Rotational Robustness of Hand Detection with a Viola-Jones Detector. Proc. IAPR International Conference on Pattern Recognition (2004)
6. Brubaker, S., Wu, J., Sun, J., Mullin, M., Rehg, J.: On the Design of Cascades of Boosted Ensembles for Face Detection. Georgia Institute of Technology Technical Report GIT-GVU-05-28 (2005)
7. Viola, P., Jones, M., Snow, D.: Detecting Pedestrians Using Patterns of Motion and Appearance. International Journal of Computer Vision 63(2) (2005) 153–161
8. Li, S., Zhang, Z.: FloatBoost learning and statistical face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9) (2004) 1112–1123
9. Xiao, R., Zhu, L., Zhang, H.: Boosting chain learning for object detection. Proc. IEEE International Conference on Computer Vision (2003) 709–715
10. Williams, R., Lambert, T., Kelsall, A., Pauly, T.: Detecting Marine Animals in Underwater Video: Let’s Start with Salmon. Proc. Americas Conference on Information Systems (2006)
11. Lin, Y., Liu, T., Fuh, C.: Fast Object Detection with Occlusions. Proc. European Conference on Computer Vision 3021 (2004) 402–413
12. Lines, J., Tillett, R., Ross, L., Chan, D., Hockaday, S., McFarlane, N.: An automatic image-based system for estimating the mass of free-swimming fish. Computers and Electronics in Agriculture 31(2) (2001) 151–168
13. Mueller, R., Brown, R., Hop, H., Moulton, L.: Video and acoustic camera techniques for studying fish under ice: a review and comparison. Reviews in Fish Biology and Fisheries 16(2) (2006) 213–226
14. Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. Proc. International Conference on Image Processing 1 (2002)
15. Rowley, H., Baluja, S., Kanade, T., Sung, K., Poggio, T.: Frontal Face Images. http://vasc.ri.cmu.edu/idb/html/face/frontal images/ (2003)
16. Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. DAGM 25th Pattern Recognition Symposium (2003)" name="eprints.referencetext" />
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<h1 class="ep_tm_pagetitle">Multiple Classifier Object Detection with Confidence Measures</h1>
<p style="margin-bottom: 1em" class="not_ep_block"><span class="person_name">Horton, Michael</span> and <span class="person_name">Cameron-Jones, Mike</span> and <span class="person_name">Williams, Raymond</span> (2007) <xhtml:em>Multiple Classifier Object Detection with Confidence Measures.</xhtml:em> AI 2007: Advances in Artificial Intelligence, 1 . pp. 559-568. ISSN 1611-3349</p><div style="margin-bottom: 1em" class="not_ep_block"><p><em>This is the latest version of this item.</em></p></div><p style="margin-bottom: 1em" class="not_ep_block"></p><table style="margin-bottom: 1em" class="not_ep_block"><tr><td valign="top" style="text-align:center"><a href="http://eprints.utas.edu.au/2694/1/Horton-MultipleClassifierObjectDetectionWithConfidenceMeasures.pdf"><img alt="[img]" src="http://eprints.utas.edu.au/style/images/fileicons/application_pdf.png" class="ep_doc_icon" border="0" /></a></td><td valign="top"><a href="http://eprints.utas.edu.au/2694/1/Horton-MultipleClassifierObjectDetectionWithConfidenceMeasures.pdf"><span class="ep_document_citation">PDF</span></a> - Full text restricted - Requires a PDF viewer<br />1656Kb</td></tr></table><p style="margin-bottom: 1em" class="not_ep_block">Official URL: <a href="http://dx.doi.org/10.1007/978-3-540-76928-6">http://dx.doi.org/10.1007/978-3-540-76928-6</a></p><div class="not_ep_block"><h2>Abstract</h2><p style="padding-bottom: 16px; text-align: left; margin: 1em auto 0em auto">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.</p></div><table style="margin-bottom: 1em" cellpadding="3" class="not_ep_block" border="0"><tr><th valign="top" class="ep_row">Item Type:</th><td valign="top" class="ep_row">Article</td></tr><tr><th valign="top" class="ep_row">Additional Information:</th><td valign="top" class="ep_row">After this paper was submitted for publication, errors were found in the positive sample generation and in the OpenCV training algorithms. These errors made the results in section 4.2 invalid. This version of the paper contains the correct results, which differ from those in the conference proceedings.
The original publication is available at www.springerlink.com
</td></tr><tr><th valign="top" class="ep_row">Keywords:</th><td valign="top" class="ep_row">Object detection,Fish detection,Haar Classifier Cascades</td></tr><tr><th valign="top" class="ep_row">Subjects:</th><td valign="top" class="ep_row"><a href="http://eprints.utas.edu.au/view/subjects/280203.html">280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280203 Image Processing</a><br /><a href="http://eprints.utas.edu.au/view/subjects/280208.html">280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280208 Computer Vision</a><br /><a href="http://eprints.utas.edu.au/view/subjects/280207.html">280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280207 Pattern Recognition</a></td></tr><tr><th valign="top" class="ep_row">ID Code:</th><td valign="top" class="ep_row">2694</td></tr><tr><th valign="top" class="ep_row">Deposited By:</th><td valign="top" class="ep_row"><span class="ep_name_citation"><span class="person_name">Mr Michael Horton</span></span></td></tr><tr><th valign="top" class="ep_row">Deposited On:</th><td valign="top" class="ep_row">14 Dec 2007 14:47</td></tr><tr><th valign="top" class="ep_row">Last Modified:</th><td valign="top" class="ep_row">09 Jan 2008 02:30</td></tr><tr><th valign="top" class="ep_row">ePrint Statistics:</th><td valign="top" class="ep_row"><a target="ePrintStats" href="/es/index.php?action=show_detail_eprint;id=2694;">View statistics for this ePrint</a></td></tr></table><div style="margin-bottom: 1em" class="not_ep_block"><h3>Available Versions of this Item</h3><ul><li><a href="http://eprints.utas.edu.au/2693/"><span class="citation">Multiple Classifier Object Detection with Confidence Measures. (deposited 21 Dec 2007 10:40)</span></a><ul><li><span class="citation">Multiple Classifier Object Detection with Confidence Measures. (deposited 14 Dec 2007 14:47)</span> <strong>[Currently Displayed]</strong></li></ul></li></ul></div><p align="right">Repository Staff Only: <a href="http://eprints.utas.edu.au/cgi/users/home?screen=EPrint::View&eprintid=2694">item control page</a></p>
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