An aquatic insect imaging system to automate insect classification

Transactions of the ASABE

Volumn 51, Issue 6, 2008, Pages 2217-2225

  Sarpola, Matt J ^a   Paasch, Robert K ^b   Mortensen, Eric N ^b   Dietterich, Thomas G ^b   Lytle, David A ^c   Moldenke, Andrew R ^c   Shapiro, Linda G ^d  

^a Videx Corporation   (United States)

^b Oregon State University   (United States)

^c OREGON STATE UNIVERSITY   (United States)

^d University of Washington   (United States)

Author keywords

Automated; Classification; Digital image; Insects; Water quality

Indexed keywords

AQUATIC INSECTS; AUTOMATED; CLASSIFICATION; DIGITAL IMAGE; INSECTS; MIRROR SYSTEMS; RECTANGULAR TUBES; SIZE CLASSES; SPECIES IDENTIFICATIONS; SYSTEM USABILITIES; SYSTEM USES; TIME CONSUMING;

CAMERAS; DIGITAL IMAGE STORAGE; ENGINEERING RESEARCH; ETHANOL; IMAGE QUALITY; IMAGING SYSTEMS; OPTOELECTRONIC DEVICES; PATTERN RECOGNITION; TUBES (COMPONENTS); WATER POLLUTION;

WATER QUALITY;

AQUATIC ORGANISM; CLASSIFICATION; DIGITAL IMAGE; IMAGING METHOD; RIVER; STONEFLY; STREAM; WATER QUALITY;

HEXAPODA; PLECOPTERA;

EID: 58149252528     PISSN: 21510032     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (24)

1. Bardinet, E., L. D. Cohen, and N. Ayache. 1998. A parametric deformable model to fit unstructured 3D data. Computer Vision and Image Understanding 71(1): 39-54.
2. Blanz, V., and T. Vetter. 1999. A morphable model for the synthesis of 3D faces. In Proc. Computer Graphics (SIGGRAPH), 187-194. Association for Computing Machinery, Special Interest Group on Graphics and Interactive Techniques.
3. Chesmore, E. D., and C. Nellenbach. 2001. Acoustic methods for the automated detection and identification of insects. Acta Hort. 562: 223-231.
4. Deng, H., W. Zhang, E. N. Mortensen, T. G. Dietterich, and L. G. Shapiro. 2007. Principal curvature-based region detector for object recognition. In Proc. Conf. on Computer Vision and Pattern Matching (CVPR '07). Piscataway, N.J.: IEEE.
5. Dorkó, G., and C. Schmid. 2005. Object class recognition using discriminative local features. Technical Report RR-5497. Montbonnot, France: INRIA-Grenoble, Rhone-Alpes Research Center.
6. Fergus, R., P. Perona, and A. Zisserman. 2005. A sparse object category model for efficient learning and exhaustive recognition. In Proc. Conf. on Computer Vision and Pattern Recognition (CVPR '05), I: 380-387. Piscataway, N.J.: IEEE.
7. Hauser, J., and D. Clausing. 1988. The house of quality. Harvard Business Review (May-June): 63-70.
8. Hilsenhoff, W. L. 1988. Rapid field assessment of organic pollution with a family-level biotic index. J. North American Benth. Soc. 7(1): 65-68.
9. Howell, V. D., K. Hoelmer, and P. A. Norman. 1982. Computer-assisted measurement and identification of honey bees. Ann. Entomol. Soc. of America 75(6): 591-594.
10. Kutulakos, K. N., and S. M Seitz. 2000. A theory of shape by space carving. Intl. J. Computer Vision 38(3): 199-218.
11. Larios, N., H. Deng, W. Zhang, M. Sarpola, J. Yuen, R. K. Paasch, A. Moldenke, D. A. Lytle, S. Ruiz Correa, E. N. Mortensen, L. G. Shapiro, and T. G. Dietterich. 2007. Automated insect identification through concatenated histograms of local appearance features. In IEEE Workshop on Applications of Computer Vision (WACV 2007), 23-32. Piscataway, N.J.: IEEE.
12. Lowe, D. G. 2004. Distinctive image features from scale-invariant keypoints. Intl. J. Computer Vision 60(2): 91-110.
13. Moore, A. 1991. Artificial neural network trained to identify mosquitoes in flight. J. Insect Behavior 4(3): 391-396.
14. Moore, A., and R. Miller. 2002. Automated identification of optically sensed aphid wingbeat waveforms. Ann. Entomol. Soc. of America 95(1): 1-8.
15. Mortensen, E. N., H. Deng, and L. Shapiro. 2005. A SIFT descriptor with global context. In Proc. Conf. on Computer Vision and Pattern Recognition (CVPR '05), I: 184-190. Piscataway, N.J.: IEEE.
16. Mortensen, E. N., E. L. Delgado, H. Deng, D. Lytle, A. Moldenke, R. Paasch, L. Shapiro, P. Wu, W. Zhang, and T. G. Dietterich. 2007. Pattern recognition for ecological science and environmental monitoring: An initial report. In Automated Taxon Identification in Systematics, 189-206. N. MacLeod and M. O'Neill, eds. Boca Raton, Fla.: CRC Press.
17. Opelt, A., A. Pinz, and A. Zisserman. 2006. Incremental learning of object detectors using a visual shape alphabet. In Proc. Conf. on Computer Vision and Pattern Recognition (CVPR '06), I: 3-10. Piscataway, N.J.: IEEE.
18. Resh, V. H., M. J. Myers, and M. J. Hannaford. 1996. Macroinvertebrates as biotic indicators of environmental quality. In Methods in Stream Ecology, 647-667. F. R. Hauer and G. A. Lamberti, eds. San Diego, Cal.: Academic Press.
19. Roth, V., V. Steinhage, S. Schroder, A. B. Cremers, and D. Wittmann. 1999. Pattern recognition combining de-noising and linear discriminate analysis within a real-world application. Lecture Notes in Computer Sci. 1689: 251-258.
20. Seitz, S. M., and C. R. Dyer. 1999. Photorealistic scene reconstruction by voxel coloring. Intl. J. Computer Vision 35(2): 151-173.
21. Weeks, P. J. D., I. D. Gauld, K. J. Gaston, and M. A. O'Neill. 1997. Automating the identification of insects: A new solution to an old problem. Bulletin Entomol. Res. 87(2): 203-211.
22. Weeks, P. J. D., M. A. O'Neill, K. J. Gaston, and I. D. Gauld. 1999. Automating insect identification: Exploring the limitations of a prototype system. J. Applied Entomol. 123(1): 1-8.
23. Yu, D. S., E. G. Kokko, J. R. Barron, G. B. Schaalje, and B. E. Gowen Dicky. 1992. Identification of ichneumonid wasps using image analysis of wings. Systematic Entomol. 17(4): 389-395.
24. Zhang, W., H. Deng, T. G. Dietterich, and E. N. Mortensen. 2006. A hierarchical object recognition system based on multi-scale principal curvature regions. In Proc. Intl. Conf. on Pattern Recognition (ICPR), I: 778-782. Piscataway, N.J.: IEEE.