Contour Matching for Fish Species Recognition and Migration Monitoring

  • Dah-Jye Lee
  • James K. Archibald
  • Robert B. Schoenberger
  • Aaron W. Dennis
  • Dennis K. Shiozawa
Part of the Studies in Computational Intelligence book series (SCI, volume 122)


A variety of matching and classification techniques have been employed in applications requiring pattern recognition. In this chapter we introduce a simple and accurate real-time contour matching technique specifically for applications involving fish species recognition and migration monitoring. We describe FishID, a prototype vision system that employs a software implementation of our newly developed contour matching algorithms. We discuss the challenges involved in the design of this system, both hardware and software, and we present results from a field test of the system at Prosser Dam in Prosser, Washington. In tests with up to four distinct species, the algorithm correctly determines the species with greater than 90 percent accuracy.


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  1. 1.
    Arkin EM, Chew LP, Huttenlocher DP, Kedem K, Mitchell JSB (1991) An efficient computable metric for comparing polygon shapes. IEEE Trans. On Pattern Analysis and Machine Intelligence 13:209–216CrossRefGoogle Scholar
  2. 2.
    Bendall C, Hiebert SD, Mueller G (1999) Experiments in in situ fish recognition systems using fish spectral and spatial signatures. US Department of the Interior, US Geological SurveyGoogle Scholar
  3. 3.
    Bogert GM, Healy MJ, Tukey JW (1963) The quefrency analysis of time series for echoes: cepstrum and saphe cracking. In: Rosenblatt M (ed) Proc. of a Symposium on Time Series Analysis, pp 209–243. John Wiley, New YorkGoogle Scholar
  4. 4.
    Chambah M, Semani D, Renouf A, Courtellemont P, Rizzi A (2004) Underwater color constancy: enhancement of automatic live fish recognition. Proceedings of the SPIE 5293:157–168CrossRefGoogle Scholar
  5. 5.
    Chan D, Hockaday S, Tillett RD, Ross LG (1999) A trainable n-tuple pattern classifier and its application for monitoring fish underwater. In: Proc. Seventh Int. Conf. on Image Processing And Its Applications, vol 1, pp 255–259Google Scholar
  6. 6.
    Childers DG, Skinner DP, Kemerait RC (1977) The cepstrum: A guide to processing. Proceedings of the IEEE, 65: 1428–1443CrossRefGoogle Scholar
  7. 7.
    Cunningham DJ, Anderson WH, Anthony RM (2006) An image-processing program for automated counting. Wildlife Society Bulletin 24:345–346Google Scholar
  8. 8.
    Dauble DD, Mueller RP (2000) Upstream passage monitoring: difficulties in estimating survival for adult Chinook salmon in the Columbia and Snake Rivers. Fisheries 25:24–34CrossRefGoogle Scholar
  9. 9.
    Dudgeon D (1977) The computation of two-dimensional cepstra. IEEE Trans. Acoustics, Speech, and Signal Processing 25:276–484CrossRefGoogle Scholar
  10. 10.
    Gamage LB, de Silva CW (1990) Use of image processing for the measurement of orientation with application to automated fish processing. In: Proc. 16th Annual Conf. IEEE Industrial Electronics Society, pp 482–487Google Scholar
  11. 11.
    Gonzalez RC, Woods RE (2002) Digital image processing. Prentice-Hall Inc., Upper Saddle River, New JerseyGoogle Scholar
  12. 12.
    Gregory S, Li H, Li J (2002) The conceptual basis for ecological responses to dam removal. BioScience 52:713–723CrossRefGoogle Scholar
  13. 13.
    Hiebert S, Helfrich LA, Weigmann DL, Liston C (2000) Anadromous salmonid passage and video image quality under infrared and visible light at Prosser Dam, Yakima River, Washington. North American Journal of Fisheries Management 20:827–832CrossRefGoogle Scholar
  14. 14.
    Huettmann F (1993) Use of a video camera and digitized video pictures in wildlife biology. Proc. of XXI IUGB (Int. Union of Game Biologists) Congress, pp 187–191Google Scholar
  15. 15.
    Huettmann F (1995) Recognizing animal species with Artificial Intelligence (AI) software on digitized video pictures; an application using roe deer and red fox. Proc. of XXII IUGB (Int. Union of Game Biologists) Congress, pp 129–138Google Scholar
  16. 16.
    Kemerait RC, Childers DG (1972) Signal detection and extraction by cepstrum techniques. IEEE Trans. Information Theory 18:745–759CrossRefGoogle Scholar
  17. 17.
    Laliberte AS, Ripple WJ (2003) Automated wildlife counts from remotely sensed imagery. Wildlife Society Bulletin 31:362–371Google Scholar
  18. 18.
    Latecki LJ, Lakämper R (2001) Shape description and search for similar objects in image databases. In: State-of-the-Art in Content-Based Image and Video Retrieval, pp 69–95, Kluwer, Deventer, The NetherlandsGoogle Scholar
  19. 19.
    Latecki LJ, Lakämper R (2002) Application of planar shape comparison to object retrieval in image databases. Pattern Recognition 35:15–29MATHCrossRefGoogle Scholar
  20. 20.
    Lee DJ, Bates D, Dromey C, Xu X (2003) A vision system performing lip shape analysis for speech pathology research. In: Proc. 29th Annual Conf. IEEE Industrial Electronics Society, pp 1086–1091Google Scholar
  21. 21.
    Lee DJ, Bates D, Dromey C, Xu X, Antani S (2003) An imaging system correlating lip shapes with tongue contact patterns for speech pathology research. In: Proc. 16th IEEE Symposium on Computer-Based Medical Systems, pp 307–313Google Scholar
  22. 22.
    Lee DJ, Krile TF, Mitra S (1988) Power spectrum and cepstrum techniques applied to image registration. Applied Optics 27:1099–1106CrossRefGoogle Scholar
  23. 23.
    Lee DJ, Mitra S, Krile TF (1988) Noise tolerance of power cepstra and phase correlation in image registration. Optical Society of America Meeting, Santa Clara, CaliforniaGoogle Scholar
  24. 24.
    Lee DJ, Mitra S, Krile TF (1989) Analysis of sequential complex images using feature extraction and 2-D cepstrum techniques. Journal of Optical Society of America 6:863–871CrossRefGoogle Scholar
  25. 25.
    Lee DJ, Mitra S, Krile TF (1990) Accuracy of depth information from cepstrumdisparities of a sequence of 2-D projections. Proceedings of the SPIE 1192:778–788Google Scholar
  26. 26.
    Lee DJ, Redd S, Schoenberger R, Xu X, Zhan P (2003) An automated fish species classification and migration monitoring system. In: Proc. 29th Annual Conf. IEEE Industrial Electronics Society, pp 1080–1085Google Scholar
  27. 27.
    Lee DJ, Schoenberger RB, Shiozawa DK, Xu XQ, Zhan P (2004) Contour matching for a fish recognition and migration monitoring system. Proceedings of the SPIE 5606:37–48CrossRefGoogle Scholar
  28. 28.
    Lee DJ, Zhan P, Shiozawa DK, Schoenberger R (2004) An automated fish recognition and migration monitoring system for biology research. Annual Meeting of the Western Division of the American Fisheries Society, Salt Lake City, UT, MarchGoogle Scholar
  29. 29.
    Lichatowich JA (2001) Salmon without rivers: a history of the pacific salmon crisis. Island Press, Washington D.C.Google Scholar
  30. 30.
    Menard M, Loonis P, Shahin A (1997) A priori minimization in pattern recognition: Application to industrial fish sorting and face recognition by computer vision. In: Proc. Sixth IEEE Int. Conf. on Fuzzy Systems, vol 2, pp 1045–1050Google Scholar
  31. 31.
    Mitra S, Lee DJ, Krile TF (1990) 3-D representation from time-sequenced biomedical images using 2-D cepstrum. In: Proc. IEEE Conference on Visualization in Biomedical Computing, pp 401–408Google Scholar
  32. 32.
    Naiberg A, Little JJ (1994) A unified recognition and stereo vision system for size assessment of fish. In: Proc. Second IEEE Workshop on Applications of Computer Vision, pp 2–9Google Scholar
  33. 33.
    Nogita S, Baba K, Yahagi H, Watanabe S, Mori S (1988) Acute toxicant warning system based on a fish movement analysis by use of AI concept. In: Proc. Int. Workshop on Artificial Intelligence for Industrial Applications, pp 273–276Google Scholar
  34. 34.
    Semani D, Bouwmans T, Frélicot C, Courtellemont P (2002) Automatic fish recognition in interactive live video. In: Proc. Int. Workshop on IVRCIA, The 6th World Multi-Conference on Systemics, Cybernetics and Informatics, pp 14–18Google Scholar
  35. 35.
    Semani D, Saint-Jean C, Frélicot C, Bouwmans T, Courtellemont P (2002) Alive fishes species characterization from video sequences. In: Proc. Joint IAPR Int. Workshop on Structural, and Statistical Pattern Recognition, pp 689–698Google Scholar
  36. 36.
    Sonka M, Hlavac V, Boyle R (1999) Image processing, analysis, and machine vision. PWS Publishing, Pacific Grove, CaliforniaGoogle Scholar
  37. 37.
    Strachan NJC (1993) Recognition of fish species by colour and shape. Image and Vision Computing 11:2–10CrossRefGoogle Scholar
  38. 38.
    Strachan NJC, Nesvadba P, Allen AR (1990) Fish species recognition by shape analysis of images. Pattern Recognition 23:539–544CrossRefGoogle Scholar
  39. 39.
    Strout C, Shiozawa DK, Lee DJ (2004) Computerized fish imaging and population count analysis. Annual Meeting of the Western Division of the American Fisheries Society, Salt Lake City, UT, MarchGoogle Scholar
  40. 40.
    Zahn CT, Roskie RZ (1972) Fourier descriptors for plane closed curves. IEEE Trans. on Computers 21:269–281MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Dah-Jye Lee
    • 1
  • James K. Archibald
    • 1
  • Robert B. Schoenberger
    • 2
  • Aaron W. Dennis
    • 1
  • Dennis K. Shiozawa
    • 3
  1. 1.Department of Electrical and Computer EngineeringBrigham Young UniversityProvoUSA
  2. 2.Symmetron, LLC a div. of ManTech International Corp.FairfaxUSA
  3. 3.Department of BiologyBrigham Young UniversityProvoUSA

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