Pattern Recognition for Ecology
Department of Computer Science and Engineering
University of Washington
Pattern Recognition for Ecological Science and Environmental Monitoring*
*This research is sponsored by the National Science Foundation under grant
IIS-0326052, "ITR: Pattern Recognition for Ecological Science
and Environmental Monitoring." Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarly reflect the views of the National Science Foundation.
The goal of this project is to develop a highly-automated approach to the classification of insect specimens. It includes the development of computer vision and machine learning algorithms for automatic classification of insects from images according to family, genus, and species. It also involves designing and building a mechanical device that can transport insects through the field of a microscope and automatically photograph them. Potential applications include measuring soil biodiversity (by counting "mesofauna", small mites and beetles) and monitoring freshwater stream health (by counting stonefly larvae that live in the streams).
- PI: Thomas G. Dietterich, Oregon State University
- Co-PIs: Dave Lytle (OSU), Andy Moldenke (OSU) Linda Shapiro (UW), Bob Paasch (OSU)
- Key Personnel: Eric Mortensen
- Students: WeiZhang, Nadia Payet, Natalia Larios, Asako Yamamuro
- Postdocs: Gonzalo Martinez-Munoz, Sinisa Todorovic
- N. Larios, J. Lin, M. Zhang,
D. Lytle, A. Moldenke, L. Shapiro, T. Dietterich,
"Stacked Spatial-Pyramid Kernel: An Object-Class Recognition Method to
Combine Scores from Random Trees," WACV 2011.
- N. Larios, B. Soran, L. G. Shapiro, G. Martinez-Munoz, J. Lin, T. G. Dietterich, "Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly
Species Identification," International Conference on Pattern
Recognition, 2010. and
- G. Martinez-Munoz, W. Zhang, N. Payet, S. Todorovic, N. Larios, A. Yamamuro, D. Lytle, A. Moldenke, E. Mortensen, R. Paasch, L. Shapiro, and T. Dietterich,
"Dictionary-Free Categorization of Very Similar Objects via Stacked Evidence Trees",
IEEE Conference on Computer Vision and Pattern Recognition, 2009.
- M. J. Sarpola, R. K. Paasch, E. N. Mortensen,
T. G. Dietterich, D. A. Lytle, A. R. Moldenke, L. G. Shapiro,
"An Aquatic Insect Imaging System to Automate Insect Classification,"
Transactions of the ASABE, Vol. 51, No. 6, 2008.
- N. Larios, H. Deng, W. Zhang, M. Sarpola, J. Yuen, R. Paasch, A. Moldenke, D. Lytle, S. Ruiz Correa, E. Mortensen, L. Shapiro, T. Dietterich, "Automated Insect Identification
through Concatenated Histograms of Local Appearance Features,"
Machine Vision and Applications, 2007.
- H. Deng, W. Zhang, E. Mortensen, T. Dietterich, L.
Shapiro, "Principal Curvature-Based Region Detector for Object Recognition,"
IEEE Conference on Computer Vision and Pattern Recognition, 2007.
- E. Mortensen, H. Deng, and L. Shapiro,
"A SIFT Descriptor with Global Context,"
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2005.