Abstract: The accurate classification of hand gestures in the development of novel hand gesture based systems designed for human computer interaction (HCI) and for human alternative and augmentative communication (HAAC) is decisive. A complete vision-based system consisting of hand gesture acquisition, segmentation, filtering, representation, and classification is developed for classification of hand gestures. The algorithms in the subsystems are formulated or selected to optimally classify hand gestures. By using a histogram thresholding algorithm grey scale image of a hand gesture is segmented. For effectively remove background and object noise in the segmented image a morphological filtering approach is designed The contour of a gesture is represented by a localized contour sequence whose samples are the perpendicular distances between the contour pixels and the chord connecting the end-points of a window centred on the contour pixels. Gesture similarity is determined by measuring the similarity between the localized contour sequences of the gestures. To measure the similarity between the localized contour sequences the linear alignment and nonlinear alignment are developed. Experiments and evaluations on a subset of American Sign Language (ASL) hand gestures show that, by using nonlinear alignment, no gestures are misclassified by the system. Additionally, it is also estimated that real-time gesture classification is possible through the use of a high-speed PC, high-speed digital signal processing chips, and code optimization.
Keywords: Gesture, augmentative, Human Computer Interaction, VHGC System.