勢是人類自然的溝通方式，因此辨識手勢一直是人機介面研究領域努力的目標。手語基本上由一組具特定意義的手勢建構而成，這個特性讓手語成為手勢辨識的一個特別應用。手語辨識的目的即在於透過手勢辨識作為聽語障者和正常人之間溝通的橋樑。台灣手語中，手語的辨識不只牽涉到手型的識別，還牽涉到手語背景的判定。由於手處在顏色近似的複雜背景，當遮蔽時，前後物體特徵相似造成視覺上的混淆，無法擷取完整的手型輪廓，也會影響到手型的判斷。

在此提出使用馬可夫隨機場核函數特徵擷取增進複雜背景下階層法追蹤目標的可分離度。基於核函數能夠導入環境資訊增加特徵擷取的效能。同時提出改良階層法的先驗資訊解決遮蔽問題。在我們實驗結果中，對於手型在複雜背景中的變是顯示出良好的改善。

A sign language can be completely defined by a finite set of specific gestures. This characteristic has made sign recognition an appealing application of gesture recognition. In Sign Language, a hand gesture occlusion with complex background may result a fault hand shape. To recognize a sign, a major difficulty arises because of the similar chromatic feature between the hand and background. In the presence of occlusion, it is difficult to extract the precise contour of the hand shape.

In this work, we use a MRF-based kernel feature extraction and improved level set model to track contours of the hand shape in feature space. The kernel-based feature extraction incorporates contextual and class information into the RBF kernel function to improve the performance of feature extraction. Along with the extracted features, we proposed an improved level set method with fitting and prior terms to solve the occlusion false effect.

In our experiment, the results show the applicability of the proposed method for recognizing the sign words in daily life. Table of Contents

[ ] J. Segen, “Controlling Computers With Gloveless Gestures,” Proc. Virtual Reality Systems, Apr. 1993.
[ ] D.J. Sturman and D. Zeltzer, “A Survey of Glove-Based Input,” IEEE Trans, Computer Graphics and Applications, vol. 14, pp. 30-39, Jan. 1994.
[ ] Saeedi, P., Lawrence, P.D., Lowe, D.G., “Vision-based 3-D trajectory tracking for unknown environments,” IEEE Trans. Robotics, vol. 22, pp. 119–136, Feb. 2006.
[ ] Sundaramoorthi, G., Yezzi, A. and Mennucci, A.C., “Coarse-to-Fine Segmentation and Tracking Using Sobolev Active Contours,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 30, pp. 851–864, May 2008.
[ ] Gupta, L. and Suwei Ma, “Gesture-based interaction and communication: automated classification of hand gesture contours,” IEEE Trans. Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 31, pp. 114–120, Feb 2001.
[ ] M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. Journal Computer Vision, vol. 1, no. 4, pp. 321–331, 1987.
[ ] C. Xu and J. L. Prince, “Gradient vector flow: a new external force for snakes,” in Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 66–71, June 1997.
[ ] R. Malladi, J. A. Sethian, and B. C. Vemuri, “Shape modeling with front propagation: a level set approach,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 2, pp. 158–175, Feb. 1995.
[ ] T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Trans. Image Processing, vol. 10, no. 2, pp. 266–277, Feb. 2001.
[ ] D. K. Panjwani and G. Healey, “Markov random field models for unsupervised segmentation of textured color images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 10, pp. 939–954, Oct. 1995.
[ ] T. Yamasaki and D. Gingras, “Image classification using spectral and spatial information based on MRF models,” IEEE Trans. Image Processing, vol. 4, pp. 1333–1339, Sep. 1995.
[ ] G. Rellier, X. Descombes, F. Falzon, and J. Zerubia, ”Texture feature analysis using a Gauss–Markov model in hyperspectral image classification,” IEEE Trans. Geosci. Remote Sensing, vol. 42, pp 1543-1551, July 2004.
[ ] Po-Wen Chou, Pi-Fuei Hsieh, and Chia-Cheng Hsieh, “Kernel-based nonlinear feature extraction for image,” in Proc. IEEE Int. Geoscience and Remote Sensing Symp., Boston, Massachusetts, USA, July 6-11, 2008
[ ] A. Yilmaz, X. Li, and M. Shah, “Contour-based object tracking with occlusion handling in video acquired using mobile cameras,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 26, no. 11, pp. 1531–1536, Nov. 2004.
[ ] Y. Fu, A. T. Erdem, and A. M. Tekalp, “Tracking visible boundary of objects using occlusion adaptive motion snake,” IEEE Trans. Image Processing, vol. 9, no. 12, pp. 2051–2060, Dec. 2000.
[ ] Pei-Jung Lee, “Recovery of Occluded Hand Shapes Using the Level Set Method with Shape Priors,” Department of Computer Science and Information Engineering, National Cheng Kung University, July, 2007.
[ ] G. Bradski, “Computer Vision Face Tracking for Use in a Perceptual User Interface,” Intel Technology J., vol. Q2, 1998.
[ ] D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-Based Object Tracking,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564- 577, May 2003.
[ ] R.T. Collins, “Mean-Shift Blob Tracking through Scale Space,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 234-240, 2003.
[ ] N.S. Peng, J. Yang, and Z. Liu, “Mean Shift Blob Tracking with Kernel Histogram Filtering and Hypothesis Testing,” IEEE, Pattern Recognition Letters, vol. 26, pp. 605-614, 2005.
[ ] M. Bertalmio, G.Sapiro, and G.Randall, “Morphing active contours,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no.7, July 2000.
[ ] A. R. Mansouri, “Region tracking via level set PDEs without motion computation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 947–961, July 2002.
[ ] V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” Int. Journal of Computer Vision, vol. 22, no. 1, pp. 61–79, Feb. 1997.
[ ] S. C. Zhu and A.Yuille, “Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884–900, Sep. 1996.
[ ] C. Xu and J. L. Prince, “Gradient vector flow: a new external force for snakes,” in Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 66–71, June 1997.
[ ] A. A. Amini, S. Tehrani, and T. E. Weymouth, “Using dynamic programming for minimizing the energy of active contours in the presence of hard constraints,” in Proc. IEEE Second Int. Conf. on Computer Vision, pp. 95–99, Dec. 1988.
[ ] A. A. Amini, T. E. Weymouth, and R. C. Jain, “Using dynamic programming for solving variational problems in vision,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 12, no. 9, pp. 855–867, Sept. 1990.
[ ] V. M. Yedid and J. C. Marin, “Active contours for the movement and motility analysis of biological objects,” in Proc. IEEE Int. Conf. on Image Processing, vol. 1, pp.196–199, 2000.
[ ] G. Camps-Valls and L. Bruzzone, “Kernel-based methods for hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1351–1362, Jun. 2005.
[ ] G. Camps-Valls, L. Gomez-Chova, J. Munoz-Mari, J. L. Rojo-Alvarez,and M. Martinez-Ramon, “Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 6, pp. 1822–1835, Mar. 2008.
[ ] V. Roth and V. Steinhage, “Nonlinear discriminant analysis using kernel functions,” Cambridge, MA: MIT Press in Advances in Neural Information Processing Systems., vol. 12, pp. 568–574, 2000.
[ ] T. Hastie, R. Tibshirani, and J. Friedman, “The Elements of Statistical Learning,” New York: Springer-Verlag, 2001.
[ ] M.-F. Balcan, A. Blum, and S. Vempala, “Kernels as features: On kernels, margins, and low-dimensional mappings,” in Proc. 15th Int. Conf. Algorithmic Learn. Theory, pp. 194–205, 2004,.
[ ] J. Ma, J. Theiler, and S. Perkins, “Two realizations of a general feature extraction framework,” Pattern Recognit., vol. 37, no. 5, pp. 875–887, 2004.
[ ] D. K. Panjwani and G. Healey, “Markov random field models for unsupervised segmentation of textured color images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 10, pp. 939–954, Oct. 1995.
[ ] Po-Wen Chou; Pi-Fuei Hsieh; Chia-Cheng Hsieh, "Kernel-Based Nonlinear Feature Extraction for Image Classification," IEEE International Geoscience and Remote Sensing Symposium, Vol.2, 7-11 July, pp.II-931 - II-934, 2008
[ ] S. Osher and J. Sethian, “Front propagation with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations,” J. Comput. Phys., vol. 79, pp. 12–49, 1988.