Abstract
In this paper we introduce a probabilistic approach to support visual supervision and gesture recognition. Task knowledge is both of geometric and visual nature and it is encoded in parametric eigenspaces. Learning processes for compute modal subspaces (eigenspaces) are the core of tracking and recognition of gestures and tasks. We describe the overall architecture of the system and detail learning processes and gesture design. Finally we show experimental results of tracking and recognition in block-world like assembling tasks and in general human gestures.
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References
Baumberg, A., Hogg, D.: Learning Flexible Models in Image Sequences. European Conference on Computer Vision. (1994).
Black, M.J., Jepson, A.D.: EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation. In Proc. ECCV-96, Cambridge 329–342.(1996).
Cédras, C., Shah, M.:Motion Based Recognition: A Survey. Tech-Rep. Department of Computer Science. Univ. of Central Florida. (1995).
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham J.: Trainable Method of Parametric Shape. Image and Vision Computing. 10 289–294. (1992).
Cootes, T.F., Taylor, C.J.: Active Shape Models. Smart Snakes. Proc. British Machine Vision Conference. 266–275. (1992).
Cootes, T.F., Taylor, C.J., Lanitis, A., Cooper, D.H., Graham, J.: Building and Using Flexible Models Incorporating Grey-Level Information. In Proc. International Conference of Computer Vision. (1993).
Davis, J.W.: Appearance-Based Motion Recognition of Human Action. MIT Media Lab Tech Rep. 387. (1996).
Darrell, T.J., Essa, I.A., Pentland, A.P.: Task Specific Gesture Analysis in Real Time Using Interpolated Views. IEEE Trans. PAMI. 18 1236–1242. (1996).
Escolano, F.: Plantillas Deformables Extendidas: Modelización Local Robusta y Caracterización Basada en Auto-Modelos para el Reconocimiento y Tracking de Estructuras Geométricas Activas. Tesis Doctoral. Universidad de Alicante (1997).
Escolano, F., Cazorla, M., Gallardo, D., Rizo, R.: Deformable Templates for Tracking and Analysis of Intravascular Ultrasound Sequences. EMMCVPR-97: International Workshop on Energy Minimization Methods for Computer Vision and Pattern Recognition. Lecture Notes in Computer Science N.1223. Springer Verlag 521–534.(1997).
Escolano, F., Cazorla, M., Gallardo, D., Llorens, F., Satorre R., Rizo, R.: Spatio-Temporal Deformable Templates for Gesture Tracking and Recognition. VII Conference of the Spanish Association of Artificial Intelligence (1997).
Fukunaga, K.: Introduction to Statistical Pattern Recognition. New York: Academic (1972).
Kirby, M., Weisser, F., Dangelmayr, G.: A model problem in the representation of digital image sequences. Patter Recognition. 26 63–73. (1993).
Lanitis, A., Taylor, C.J., Cootes, T.F., Ahmed, T.: Automatic Interpretation of Human Faces and Hand Gestures Using Flexible Models. Tech-Rep. Department of Medical Biophysics. University of Manchester. (1994).
Moghaddam, B., Pentland, A.: Face Recognition using View-Based and Modular Eigenspaces. M.I.T. Technical Report No. 301. (1994)
Murase, H., Sakai, R.: Moving object recognition in eigenspace representation: gait analysis and lip reading. Patter Recognition Letters. 17. 155–162. (1996).
Nastar, C., Ayache, N.: A New Physically Based Model for Efficient Tracking and Analysis of Deformations. In Proc. Geometric Reasoning for Perception and Action. Springer-Verlag (1993).
Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience. 3 71–89. (1991).
Zhu, S.C., Yuille, A.L.: FORMS: A Flexible Object Recognition and Modeling System. Int. Journal of Computer Vision. (1996)
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Escolano, F., Cazorla, M., Gallardo, D., Llorens, F., Satorre, R., Rizo, R. (1998). A combined probabilistic framework for learning gestures and actions. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_452
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DOI: https://doi.org/10.1007/3-540-64574-8_452
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