Hand detections based on two proposed pixel-based skin-color models are proposed. Different from the conventional methods, the proposed models are built on the invariant surface obtained by supervised learning on nonlinear color space -which effectively preserves vital properties of skin colors in low illumination. By using a high-dimensional feature space, which incorporating probability of fitting the invariant surface with a nonlinear mapping value of chrominance in linear space, first skin-color model is proposed as a thresholding method according to feature values of pixels. Subsequently, a second skin-color model is constructed based on a parametric region which maps the learning data on an invariant surface. This mode achieves more favorable skin-color segmentation results than the conventional methods in experiments.
關聯:
The 4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing，起迄日：2008/8/15~2008/8/17，地點：Harbin
