Fish monitoring and sizing using computer vision

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9108, Issue , 2015, Pages 419-428

  Rodriguez, Alvaro ^a   Rico Diaz, Angel J ^a   Rabuñal, Juan R ^a   Puertas, Jeronimo ^a   Pena, Luis ^a  

^a UNIVERSITY OF A CORUÑA   (Spain)

Author keywords

Computer vision; Segmentation

Indexed keywords

ALGORITHMS; COMPUTER VISION; FISHERIES; IMAGE PROCESSING; IMAGE SEGMENTATION; OPTICAL DATA PROCESSING; STEREO IMAGE PROCESSING; STEREO VISION;

BIOLOGICAL VARIABLES; COMPUTER VISION TECHNIQUES; DIRECT OBSERVATIONS; FISH BEHAVIOR; IMAGE PROCESSING ALGORITHM; STEREO SYSTEM; TRADITIONAL TECHNIQUES; UNDERWATER ENVIRONMENTS;

FISH;

EID: 84937682467     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-18833-1_44     Document Type: Conference Paper

References (32)

1. Leon-Santana, M., Hernandez, J.M.: Optimum management and environmental protection in the aquaculture industry. Ecological Economics 64, 849–857 (2008)
2. Cappo, M., Harvey, E., Malcolm, H., Speare, P.: Potential of video techniques to monitor diversity, abundance and size of fish in studies of marine protected areas. In: Aquatic Protected Areas-What Works Best and How do We Know, pp. 455–464 (2003)
3. Brosnan, T., Sun, D.-W.: Inspection and grading of agricultural and food products by computer vision systems a review. Computers and Electronics in Agriculture 36, 193–213 (2002)
4. Costa, C., Antonucci, F., Pallottino, F., Aguzzi, J., Sun, D.-W., Menesatti, P.: Shape Analysis of Agricultural Products: A Review of Recent Research Advances and Potential Application to Computer Vision. Food and Bioprocess Technology 4, 673–692 (2011)
5. Armstrong, J.D., Bagley, P.M., Priede, I.G.: Photographic and acoustic tracking observations of the behavior of the grenadier Coryphaenoides (Nematonorus) armatus, the eel Synaphobranchus bathybius, and other abyssal demersal fish in the North Atlantic Ocean. Marine Biology 112, 1432–1793 (1992)
6. Steig, T.W., Iverson, T.K.: Acoustic monitoring of salmonid density, target strength, and trajectories at two dams on the Columbia River, using a split-beam scaning system. Fisheries Research 35, 43–53 (1998)
7. Rodriguez, A., Bermudez, M., Rabuñal, J., Puertas, J.: Fish tracking in vertical slot fishways using computer vision techniques. Journal of Hydroinformatics (2014)
8. Zion, B., Shklyar, A., Karplus, I.: Sorting fish by computer vision. Computers and Electronics in Agriculture 23, 175–187 (1999)
9. Zion, B., Shklyar, A., Karplus, I.: In-vivo fish sorting by computer vision. Aquacultural Engineering 22, 165–179 (2000)
10. Petrell, R.J., Shi, X., Ward, R.K., Naiberg, A., Savage, C.R.: Determining fish size and swimming speed in cages and tanks using simple video techniques. Aquacultural Engineering 16, 63–84 (1997)
11. Israeli, D., Kimmel, E.: Monitoring the behavior of hypoxia-stressed Carassius auratus using computer vision. Aquacultural Engineering 15, 423–440 (1996)
12. Ruff, B.P., Marchant, J.A., Frost, A.R.: Fish sizing and monitoring using a stereo image analysis system applied to fish farming. Aquacultural Engineering 14, 155–173 (1995)
13. Duarte, S., Reig, L., Oca, J., Flos, R.: Computerized imaging techniques for fish tracking in behavioral studies. European Aquaculture Society (2004)
14. Chambah, M., Semani, D., Renouf, A., Courtellemont, P., Rizzi, A.: Underwater color constancy enhancement of automatic live fish recognition. In: IS&T Electronic Imaging (SPIE) (2004)
15. Morais, E.F., Campos, M.F.M., Padua, F.L.C., Carceroni, R.L.: Particle filterbased predictive tracking for robust fish count. In: Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI) (2005)
16. Clausen, S., Greiner, K., Andersen, O., Lie, K.-A., Schulerud, H., Kavli, T.: Automatic segmentation of overlapping fish using shape priors. In: Ersbøll, B.K., Pedersen, K.S. (eds.) SCIA 2007. LNCS, vol. 4522, pp. 11–20. Springer, Heidelberg (2007)
17. Chuang, M.-C., Hwang, J.-N., Williams, K., Towler, R.: Automatic fish segmentation via double local thresholding for trawl-based underwater camera systems. In: IEEE International Conference on Image Processing (ICIP) (2011)
18. Spampinato, C., Chen-Burger, Y.-H., Nadarajan, G., Fisher, R.: Detecting, Tracking and Counting Fish in Low Quality Unconstrained Underwater Videos. In: Int. Conf. on Computer Vision Theory and Applications (VISAPP) (2008)
19. Lines, J.A., Tillett, R.D., Ross, L.G., Chan, D., Hockaday, S., McFarlane, N.J.B.: An automatic image-based system for estimating the mass of free-swimming fish. Computers and Electronics in Agriculture 31, 151–168 (2001)
20. Frenkel, V., Kindschi, G., Zohar, Y.: Noninvasive, mass marking of fish by immersion in calcein: evaluation of fish size and ultrasound exposure on mark endurance. Aquaculture 214, 169–183 (2002)
21. Martinez-de Dios, J., Serna, C., Ollero, A.: Computer vision and robotics techniques in fish farms. Robotica 21, 233–243 (2003)
22. White, D.J., Svellingen, C., Strachan, N.J.C.: Automated measurement of species and length of fish by computer vision. Fisheries Research 80, 203–210 (2006)
23. Hartley, R.I., Zisserman, A.: Multiple View Geometry. Cambridge University Press (2004)
24. Abad, F.H., Abad, V.H., Andreu, J.F., Vives, M.O.: Application of Projective Geometry to Synthetic Cameras. In: XIV International Conference of Graphic Engineering (2002)
25. Martin, N., Perez, B.A., Aguilera, D.G., Lahoz, J.G.: Applied Analysis of Camera Calibration Methods for Photometric Uses. In: VII National Conference of Topography and Cartography (2004)
26. Zhang, Z.: Flexible Camera Calibration By Viewing a Plane From Unknown Orientations. In: International Conference on Computer Vision (ICCV) (1999)
27. OPENCV: Open Source Computer Vision, http://opencv.org (Visited: February 2015)
28. Coifman, B., Beymer, D., McLauchlan, P., Malik, J.: A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research Part C: Emerging Technologies 6, 271–288 (1998)
29. Horprasert, T., Harwood, D., Davis, L.S.: A statistical approach for real-time robust background subtraction and shadow detection. In: IEEE ICCV, pp. 1–19 (1999)
30. KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Video-Based Surveillance Systems, pp. 135–144. Springer (2002)
31. Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: International Conference on Patern Recognition (ICPR 2004), pp. 28–31 (2004)
32. Godbehere, A.B., Matsukawa, A., Goldberg, K.: Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation. In: American Control Conference (ACC), pp. 4305–4312 (2012)