An Adaptive Self-Organizing Color Segmentation Algorithm with Application to Robust Real-time Human Hand Localization

In Proc. Asian Conf. on Computer Vision, Taiwan, 2000 This paper describes an adaptive self-organizing color segmentation algorithm and a transductive learning algorithm used to localize human hand in video sequences. The color distribution at each time frame is approximated by the proposed 1-D self-organizing map (SOM), in which schemes of growing, pruning and merging are facilitated to find an appropriate number of color cluster automatically. Due to the dynamic backgrounds and changing lighting conditions, the distribution of color over time may not be stationary. An algorithm of SOM transduction is proposed to learn the nonstationary color distribution in HSI color space by combining supervised and unsupervised learning paradigms. Color cue and motion cue are integrated in the localization system, in which motion cue is employed to focus the attention of the system. This approach is also applied to other tasks such as human face tracking and color indexing. Our localization system implemented on a SGI O2 R10000 workstation is reliable and efficient at 20-30Hz.

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