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Classification of Hand Gesture: (a) Three basic classes of hand gesture; (b) colour extraction of each class of hand gesture; (c) shape extraction of each class of hand gesture; (d) orientation histogram of each class of hand gesture This equation can be transformed to another by Bayesian rule:
Source publication
This paper presents a new method for gesture recognition of Human beingspsila hand. This method integrates the features of shape, color and orientation histograms, which are extracted from images, and estimate the comparability with all the different types of gestures by a proposed Expectation-Maximization algorithm in Gaussian mixture model. The c...
Contexts in source publication
Context 1
... evaluate the proposed algorithm, we collected a group of Hand-gestures as the test data set, and then classified all of them into 3 basic classes as shown in Fig. 1 (a). The first one is a hand with all fingers outstretched. The second one is considered as a fist, and the third one involves only 2 outstretched fingers (forefinger and middle finger) symbolizing a ...
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... feature of color is extracted in the YCbCr Color Space. Fig.1 (b) shows the results of the color extraction of every image in Fig.1. The feature of shape is extracted by Canny Edge Detector. ...
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... feature of color is extracted in the YCbCr Color Space. Fig.1 (b) shows the results of the color extraction of every image in Fig.1. The feature of shape is extracted by Canny Edge Detector. ...
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... feature of shape is extracted by Canny Edge Detector. The results are shown in Fig.1 (c). Orientation histogram [8,9] is one of useful features which offers robustness to lighting changes and give translational invariance. ...
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Gesture Recognition provides an efficient human-computer interaction for interactive and intelligent computing. In this work, we address the problem of gesture recognition us- ing the theory of random projection and by formulating the recognition problem as an l1-minimization problem. The gesture recognition uses a single 3-axis accelerometer for data acquisition and comprises two main stages: a training stage and a testing stage. For training, the system employs dynamic time warping as well as affinity propagation to create exemplars for each gesture while for testing, the system projects all candidate traces and also the unknown trace onto the same lower dimensional subspace for recogni- tion. A dictionary of 18 gestures is defined and a database of over 3,700 traces is created from 7 subjects on which the system is tested and evaluated. Simulation results reveal a superior performance, in terms of accuracy and computational complexity, compared to other systems in the literature.
