Abstract
This paper presents a method to segment the hand over complex backgrounds, such as the face. The similar colors and texture of the hand and face make the problem particularly challenging. Our method is based on the concept of an image force field. In this representation each individual image location consists of a vector value which is a nonlinear combination of the remaining pixels in the image. We introduce and develop a novel physics based feature that is able to measure regional structure in the image thus avoiding the problem of local pixel based analysis, which break down under our conditions. The regional image structure changes in the occluded region during occlusion. Elsewhere the regional structure remains relatively constant. We model the regional image structure at all image locations over time using a Mixture of Gaussians (MoG) to detect the occluded region in the image. We have tested the method on a number of sequences demonstrating the versatility of the proposed approach.
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References
Ahmad, T., Taylor, C., Lanitis, A., Cootes, T.: Tracking and recognising hand gestures, using statistical shape models. Image and Vision Computing (1997)
Bretzner, L., Laptev, I., Lindeberg, T.: Hand gesture recognition using multi-scale colour features, hierarchical models and particle filtering. Automatic Face and Gesture Recognition (2002)
Brèthes, L., Menezes, P., Lerasle, F., Hayet, J.: Face tracking and hand gesture recognition for human-robot interaction. In: International Conference on Robotics and Automation (2004)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis.TPAMI (2002)
Cui, Y., Weng, J.: Learning-based hand sign recognition. Automatic Face and Gesture Recognition (1995)
Cui, Y., Weng, J.: Hand sign recognition from intensity image sequences with complex backgrounds. Automatic Face and Gesture Recognition (1996)
Davis, J., Shah, M.: Recognizing hand gestures. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 800, Springer, Heidelberg (1994)
Fei, H., Reid, I.D.: Joint bayes filter: A hybrid tracker for non-rigid hand motion recognition. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3023, pp. 497–508. Springer, Heidelberg (2004)
Hamada, Y., Shimada, N., Shirai, Y.: Hand shape estimation under complex backgrounds for sign language recognition. Automatic Face and Gesture Recognition (2004)
Hurley, D.J., Nixon, M.S., Carter, J.N.: Force field energy functionals for image feature extraction. IVC (2002)
Jeong, M.H., Kuno, Y., Shimada, N., Shirai, Y.: Recognition of shape-changing hand gestures. IEICE Transactions Division D E85-D (10), 1678–1687 (2002)
Sherrah, J., Gong, S.: Resolving visual uncertainty and occlusion through probabilistic reasoning. In: BMVC (2000)
Stauffer, C., Grimson, E.: Learning patterns of activity using real-time tracking. PAMI (2000)
Stenger, B., Thayananthan, A., Torr, P., Cipolla, R.: Hand pose estimation using hierarchical detection. In: Intl. Workshop on Human-Computer Interaction (2004)
Triesch, J., von der Malsburg, C.: A system for person-independent hand posture recognition against complex backgrounds. TPAMI (2001)
Triesch, J., von der Malsburg, C.: Classification of hand postures against complex backgrounds using elastic graph matching. Image and Vision Computing (2002)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR (2001)
Zhou, H., Huang, T.S.: Tracking articulated hand motion with eigen dynamics analysis. In: ICCV (2003)
Zhu, X., Yang, J., Waibel, A.: Segmenting hands of arbitrary color. Automatic Face and Gesture Recognition (2000)
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Smith, P., da Vitoria Lobo, N., Shah, M. (2005). Resolving Hand over Face Occlusion. In: Sebe, N., Lew, M., Huang, T.S. (eds) Computer Vision in Human-Computer Interaction. HCI 2005. Lecture Notes in Computer Science, vol 3766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573425_16
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DOI: https://doi.org/10.1007/11573425_16
Publisher Name: Springer, Berlin, Heidelberg
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