Processing of Underwater Colour Images Applied to Live Aquarium Videos

D. Semani, M. Chambah, and P. Courtellemont


  1. [1] D. Semani, T. Bouwmans, C. Frélicot, & P. Courtellemont,Automatic fish recognition in interactive live video, 6th WorldMulti-Conf. on Systemics, Cybernetics and Informatics SCI’02,(XIV), Orlando, USA, 2002, 94–99.
  2. [2] D. Semani, C. Saint-Jean, C. Frélicot, T. Bouwmans, & P. Courtellemont, Alive fishes species characterization fromvideo sequences, 9th Int. workshop on Structural and Syntactic Pattern Recognition, 4th Int. workshop on Statistical Techniques in Pattern Recognition, SSPR2002 & SPR2002, Windsor, Canada, 2002, 689–698.
  3. [3] D. Semani, C. Frélicot, & P. Courtellemont, Feature selection using an ambiguity measure based on fuzzy OR-2 operators, Int. Fuzzy Systems Association IFSA’03, Istanbul, 2003, 265–268.
  4. [4] M. Chambah, B. Besserer, & P. Courtellemont, Recent progressin automatic digital restoration of color motion pictures, Proc.SPIE Electronic Imaging, San Jose, CA, Vol. 4663, 2001, 98–109. doi:10.1117/12.452979
  5. [5] A. Rizzi, C. Gatta, & D. Marini, From retinex to ACE: issuesin digital images unsupervised color equalization, Journal ofElectronic Imaging, 13(1), 2004. doi:10.1117/1.1635366
  6. [6] M. Chambah, A. Rizzi, C. Gatta, B. Besserer, & D. Marini,Perceptual approach for unsupervised digital color restorationof cinematographic archives, Proc. SPIE Electronic Imaging,S. Clara, California, Vol. 5008, 2003, 138–149. doi:10.1117/12.472019
  7. [7] A. Rizzi, C. Gatta, & D. Marini, A new algorithm for unsupervised global and local color correction, Pattern Recognition Letters, 24(11), 2003, 1663–1677. doi:10.1016/S0167-8655(02)00323-9
  8. [8] F. Gasparini, R. Schettini, & P. Gallina, Tunable cast removerfor digital photographs, Proc. SPIE Electronic Imaging, San Jose, CA, Vol. 5008, 2003, 92–100. doi:10.1117/12.472719
  9. [9] F. Gasparini, R. Schettini, & P. Gallina, An innovative algorithm for cast detection, Proc. SPIE Electronic Imaging, San Jose, CA, Vol. 4672, 2002, 280–286. doi:10.1117/12.452683
  10. [10] A. Rizzi, M. Chambah, D. Lenza, B. Besserer, & D. Marini,Tuning of perceptual technique for digital movie color restoration, Proc. SPIE Electronic Imaging, San Jose, CA, Vol. 5308, 2004, 1286–1294. doi:10.1117/12.525789
  11. [11] C. Stauffer & W.E.L. Grimson, Adaptive background mixturemodels for real-time tracking, Proc. of Computer Vision andPattern Recognition, Vol. 2, 1999, 246–252. doi:10.1109/CVPR.1999.784637
  12. [12] A. Elgammal, R. Duraiswami, D. Harwood, & L.S. Davis,Background and foreground modeling using nonparametrickernel density for visual surveillance, Proc. of the IEEE, 9(7),2002, 1151–1163. doi:10.1109/JPROC.2002.801448
  13. [13] M. Chambah, D. Semani, A. Renouf, P. Courtellemont, & A.Rizzi, Underwater color constancy: enhancement of automaticlive fish recognition, Proc. SPIE Electronic Imaging, San Jose,CA, USA, Vol. 5293, 2003, 157–168. doi:10.1117/12.524540
  14. [14] Y.J. Zhang, A survey on evaluation methods for image segmentation, Pattern Recognition, 29(8), 1996, 1335–1346. doi:10.1016/0031-3203(95)00169-7
  15. [15] M.D. Levine & A.M. Nazif, Dynamic measurement of computergenerated image segmentation, IEEE Trans. on PAMI, 7(2), 1985, 155–164.
  16. [16] J. Liu & Y.-H. Yang, Multiresolution color image segmentation,IEEE Trans. on PAMI, 16(7), 1994, 689–700. doi:10.1109/34.297949
  17. [17] J. Huang, S.R. Kumar, M. Mitra, W.-J. Zhu, & R. Zabih, Image indexing using colour correlograms, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1997, 762-768. doi:10.1109/CVPR.1997.609412

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