Abstract
Hand posture recognition is one of the most challenging problems in the computer vision field, especially in the scenes with complex background and illumination variance. This paper presents a real time hand posture recognition method in color-depth image. To accurately locate hands in the images with complex background, a depth histogram based adaptive thresholding method is adopted for the depth image and a Bayesian skin-color detection is performed for the corresponding color image. Then two processed results are fused and refined with a region-growing method. Finally, the histogram of gradients feature of the hand posture is computed for Extreme Learning Machine classifier to recognize different postures. Experiments show that the proposed hand posture recognition method runs in real-time and achieves high recognition accuracy.
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Zhou, Z., Li, S., Sun, B. (2014). Extreme Learning Machine Based Hand Posture Recognition in Color-Depth Image. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_29
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DOI: https://doi.org/10.1007/978-3-662-45643-9_29
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