Segmmentation of Different Skin Colors by Combining Graph Cuts with Probability Neural Network, and its Application to Classification of Hand Gesture

• Main Authors: Kai-Di Lu, 呂愷迪
• Other Authors: Chih-Lyang Hwang
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/75086725440389799948

碩士 === 淡江大學 === 電機工程學系碩士班 === 99 === It is known that fixed thresholds mostly fail in two situations as they only search for a certain skin color range: (i) any skin-like object may be classified as skin if skin-like colors belong to fixed threshold range. (ii) any true skin for different races may be mistakenly classified as non-skin if that skin colors do not belong to fixed threshold range. In this paper, a dynamic threshold of different skin colors based on the input image is determined by the combination of graph cuts (GC) and probability neural network (PNN). The compared results among GC, PNN and GC+PNN are presented not only to verify the accurate segmentation of different skin colors but also to reduce the computation time as compared with only using the neural network for the classification of different skin colors and non-skin color. The experimental results for different lighting conditions also verify the usefulness of our method. Finally, the application to the classification of hand gestures in complex environment is presented to evaluate the effectiveness and efficiency of the proposed method.