Gesture input is a natural and effective interactive model. The tracking of deformable hand gesture is a very important task in gesture-based interaction. A novel real-time tracking approach is proposed to capture hand motion with single camera. It combines the characters of model-based and appearance-based method. The presented approach achieves auto-initialization by posture recognition and matching with image features. It solves the problem of interference among fingers successfully by the integration of K-Means clustering and Particle Filters. Moreover, tracking detection realizes resumption from tracking failure and automatic update of hand model. Experiments show that, the proposed method can achieve continuous real-time tracking of deformable hand gesture, and meets the requirements for gesture-based Human-Computer Interaction.

Recognition of complex dynamic gesture is a key issue for visual gesture-based human-computer interaction. In this paper, an HMM-FNN model is proposed for gesture recognition, which combines ability of HMM model for temporal data modeling with that of fuzzy neural network for fuzzy rule modeling and fuzzy inference. Complex dynamic gesture has two important properties: Its motion can be decomposed and usually being defined in a fuzzy way. By HMM-FNN, complex gesture is firstly decomposed into three components: Posture changing, movement in 2D plane and movement in Z-axis direction, each of which is modeled by HMM. The likelihood of each HMM to observation sequence is considered as membership value of FNN, and gesture is classified through fuzzy inference of FNN. In this proposed method, high-dimensional gesture feature is transformed into several low-dimensional features, as a result, computational complexity is reduced. Furthermore, human's experience or prior knowledge can be used to build and optimize model structure. Experimental results show that the proposed method is an effective method for recognition of complex dynamic gesture, and is superior to conventional HMM method.

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, http://iel.iscas.ac.cn/ Wang XY, Zhang XW, Dai GZ. An approach to tracking deformable hand gesture for real-time interaction.

In order to grasp the water quality change trend and predict the future water quality characteristics of the bicarbonate mineral water in WUDALIANCHI, using the measured data from 2008 to 2016 of north drink spring in WUDALIANCHI as the predicted sample, carbon dioxide, total soluble solids, strontium and metasilicic acid which can divide mineral water type as analysis factor, the BP neural network combination forecast model was contructed. The results showed that the BP neural network combination forecast model was obviously more precise and better than grey system model, its average relative error was controlled within 5%. The results indicated that the BP neural network combination forecast model can effectively predict the change trend of water quality of bicarbonate mineral water in WUDALIANCHI.