Abstract
Contrary to Genetics Snakes, the current methods of mouth modeling are very sensitive to initialization (position of a snake or a deformable contour before convergence) and fall easily into local minima. We propose in this article to make converge two snakes in parallel via a genetic algorithm. The coding of the chromosome takes into account at the same time gradients and region type information contained in the image. In addition we introduce the use of STM (Sparse Template Matching) into the field of leapreading. Thanks to a temporal filter, word signatures (stored in Sparse Templates) make it possible to recognize various words pronounced several times at one week interval.
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References
C. Bounsaythip and J.T. Alander. Genetic algorithms applied to image processing - a review. In Proc. of the 3rd Nordic Workshop on Genetic Algorithms (3NWGA), 1997.
K. Sakaue, A. Amano, and N Yokoya. Optimization approaches in computer vision and image processing. IEICE Trans. Inf. and Syst, 1999.
A. Cagnoni, A. Dobrzeniecki, R. Poli, and J. Yanch. Genetic algorithm-based interactive segmentation of 3d medical images. Image and Vision Computing, 17(12):881–895.
L. Ballerini. Genetic snakes for color images segmentation. Lecture Notes in computer sciences 2037, 2001.
N. Covavisaruch and T. Tanatipanond. Deformable contour for brain mr images by genetic algorithm: From rigid totraining approaches. In Proceedings, Image and Vision Computing New Zealand (IVCNZ ’99), 1999.
Michael T. Chan, You Zhang, and Thomas S. Huang. Real-time lip tracking and bimodal continuous speech recognition. In Workshop on Multimedia Signal Processing, 1998.
P. Delmas, P.Y. Coulon, and V. Fristot. Automatic snakes for robust lip boudaries extraction. In International Conference on Acoustics Speech and Signal Processing (ICASSP), 1999.
Y. Tian, T. Kanade, and J. F. Cohn. Recognizing action units for facial expression analysis. IEEE Trans, on Pattern Analysis and Machine Intelligence, 2001.
R. Séguier, N. Cladel, C. Foucher, and D. Mercier. Lipreading with spiking neurons: One pass learning. In International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, Feb 2002.
G. Potamianos, J. Luettin, and C. Neti. Hierarchical discriminant features for audio-visual lvcsr. In International Conference on Acoustics Speech and Signal Processing (ICASSP), May 2001.
G. Sullivan, K. Baker, A. Worrall, C. Attwood, and P. Remagnino. Model-based vehicle detection and classification using orthographic approximations. In Proc of 7th British Machine Vision Conference, 1996.
R. Séguier, A. Le Glaunec, and B. Loriferne. Human faces detection and tracking in video sequence. In Proc. 7th Portuguese Conf. on Pattern Recognition, 1995.
R. Séguier and D. Mercier. Audio-visual speech recognition: one pass learning with spiking neurons. In International Conference on Artificial Neural Networks (ICANN), 2002.
Michael T. Chan. Hmm-based audio-visual speech recognition integrating geometric and appearance-based visual features. In Workshop on Multimedia Signal Processing, 2001.
Per-Erik Forssén. Window matching using sparse templates. In http://www.isy.liu.se/cvl/ScOut/TechRep/, 2001.
S. Pigeon. M2vts. In www.tele.ucl.ac.be/PROJECTS/M2VTS/m2fdb.html, 1996.
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Séguier, R., Cladel, N. (2003). Genetic Snakes: Application on Lipreading. In: Pearson, D.W., Steele, N.C., Albrecht, R.F. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0646-4_41
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DOI: https://doi.org/10.1007/978-3-7091-0646-4_41
Publisher Name: Springer, Vienna
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