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Austin, Texas
Feb. 21, 2007 to Feb. 22, 2007
ISBN: 0-7695-2794-9
pp: 26
N. Larios , University of Washington
H. Deng , Oregon State University
W. Zhang , Oregon State University
M. Sarpola , Oregon State University
J. Yuen , Massachusetts Institute of Technology
R. Paasch , Oregon State University
A. Moldenke , Oregon State University
D. A. Lytle , Oregon State University
Ruiz Correa , Children?s National Medical Center
E. Mortensen , Oregon State University
L. G. Shapiro , University of Washington
T. G. Dietterich , Oregon State University
ABSTRACT
This paper describes a fully automated stonefly-larvae classification system using a local features approach. It compares the three region detectors employed by the sys- tem: the Hessian-affine detector, the Kadir entropy detector and a new detector we have developed called the princi- pal curvature based region detector (PCBR). It introduces a concatenated feature histogram (CFH) methodology that uses histograms of local region descriptors as feature vec- tors for classification and compares the results using this methodology to that of Opelt [11] on three stonefly identifi- cation tasks. Our results indicate that the PCBR detector outperforms the other two detectors on the most difficult discrimination task and that the use of all three detectors outperforms any other configuration. The CFH methodol- ogy also outperforms the Opelt methodology in these tasks.
INDEX TERMS
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CITATION
N. Larios, H. Deng, W. Zhang, M. Sarpola, J. Yuen, R. Paasch, A. Moldenke, D. A. Lytle, Ruiz Correa, E. Mortensen, L. G. Shapiro, T. G. Dietterich, "Automated Insect Identification through Concatenated Histograms of Local Appearance Features", WACV, 2007, Applications of Computer Vision, IEEE Workshop on, Applications of Computer Vision, IEEE Workshop on 2007, pp. 26, doi:10.1109/WACV.2007.13
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