In this paper, we suggest a method that recognizes hand gesture based on moment features in hand shape. First of all, hand regions are segmented from input streams based on skin color detection. Hand detection can be achieved more easily if smart devices such as Kinect are used but we used web camera as an input device. Because hand and also face can be segmented from a frame, we try to remove face from the segmented result. From segmented hand regions palm region is extracted by removing wrist and then moment invariants are calculated from the palm region. Finally we use artificial neural network to classify the classes of the hand gestures. We perform recognition test for input patters with trained DB of 7 classes that contains hand gesture of rock-paper-scissors game and 3 different kinds of hand shape concerned with robot control.
In this paper, we combine two moments of Hu and Zernike to try improve the recognition accuracy. And we apply PCA on the Zernike moments to speedup of recognition without degrading the recognition accuracy. We compare the recognition performance between Hu and Zernike moments. Also we show the recognition accuracy of new methods with two modified features. We evaluate the recognition accuracy with two datasets of hands and digits. For hand recognition, we localize hands from hand candidates with improved method against our preliminary study [2].
In this paper, we suggest new technology that can draw characters at a long distance by tracking a hand and analysing the trajectories of hand positions. It's difficult to recognize the shape of a character without discriminating effective strokes from all drawing strokes. We detect end points from input trajectories of a syllable with camera system and localize strokes by using detected end points. Then we classify the patterns of the extracted strokes into eight classes and finally into two categories of stroke that is part of syllable and not. We only draw the strokes that are parts of syllable and can display a character. We can get 88.3% in classification accuracy of stroke patterns and 91.1% in stroke type classification.
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