Accurate recognition of large number of hand gestures

A hierarchical gesture recognition algorithm is introduced to recognise a large number of gestures. Three stages of the proposed algorithm are based on a new hand tracking technique to recognise the actual beginning of a gesture using a Kalman filtering process, hidden Markov models and graph matching. Processing time is important in working with large databases. Therefore, special cares are taken to deal with the large number of gestures, which are partially similar

A Study on Hand Gesture Recognition Technique

Hand gesture recognition system can be used for interfacing between computer and human using hand gesture. This work presents a technique for a human computer interface through hand gesture recognition that is able to recognize 25 static gestures from the American Sign Language hand alphabet. The objective of this thesis is to develop an algorithm for recognition of hand gestures with reasonable accuracy. The segmentation of gray scale image of a hand gesture is performed using Otsu thresholding algorithm. Otsu algorithm treats any segmentation problem as classification problem. Total image level is divided into two classes one is hand and other is background. The optimal threshold value is determined by computing the ratio between class variance and total class variance. A morphological filtering method is used to effectively remove background and object noise in the segmented image. Morphological method consists of dilation, erosion, opening, and closing operation. Canny edge detection technique is used to find the boundary of hand gesture in image. A contour tracking algorithm is applied to track the contour in clockwise direction. Contour of a gesture is represented by a Localized Contour Sequence (L.C.S) whose samples are the perpendicular distances between the contour pixels and the chord connecting the end-points of a window centered on the contour pixels. These extracted features are applied as input to classifier. Linear classifier discriminates the images based on dissimilarity between two images. Multi Class Support Vector Machine (MCSVM) and Least Square Support Vector Machine (LSSVM) is also implemented for the classification purpose. Experimental result shows that 94.2% recognition accuracy is achieved by using linear classifier and 98.6% recognition accuracy is achieved using Multiclass Support Vector machine classifier. Least Square Support Vector Machine (LSSVM) classifier is also used for classification purpose and shows 99.2% recognition accuracy

Building Temporal Models for Gesture Recognition

This work presents a piecewise linear approximation to non-linear Point Distribution Models for modelling the human hand. The work utilises the natural segmentation of shape space, inherent to the technique, to apply temporal constraints which can be used with CONDENSATION to support multiple hypotheses and quantum leaps through shape space. This paper presents a novel method by which the one-state transitions of the English Language are projected into shape space for tracking and model prediction using a HMM like approach. 1 Introduction Previous work by the author and other researchers have investigated statistical models of deformation [1-8]. These deformable models have been used to learn a priori shape and deformation from a training set of examples which, represent the shape and deformation of an object or a class of objects. Models are typically constructed that know what is valid deformation but not when deformation is valid. This important temporal constraint is benef..