GESTURE RECOGNITION SYSTEM AND METHOD THEREOF
First Claim
1. A gesture recognition system, comprising:
- an image pick-up device, for capturing an image data containing a hand image;
a template database, for recording multiple gesture templates representing different gestures, wherein the gesture templates are classified by angles and are respectively stored in gesture template libraries of different angle classes;
a processor, for communicating with the image pick-up device and obtaining the image data, finding out a skin part from the image data, producing a skin edge by using an edge detection means, and then classifying the skin edge into multiple edge parts of different angle classes according to angles of the skin edge;
an operation engine, having multiple parallel operation units respectively for performing template matching at different angles, wherein the edge parts of different angle classes are respectively sent to different parallel operation units for template matching, so as to find out the gesture templates most resembling the edge parts in the corresponding gesture template libraries of different angle classes;
an optimal template selection means, for further selecting an optimal gesture template from the resembling gesture templates found out by the parallel operation units; and
a display terminal, for displaying a gesture image represented by the optimal gesture template.
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Abstract
A gesture recognition system includes an image pick-up device, a processor, an operation engine, an optimal template selection means, and a display terminal. The image pick-up device is for capturing an image containing a natural gesture. The processor is for finding out a skin edge of a skin part from the image, and then classifying the skin edge into multiple edge parts at different angles. The operation engine has multiple parallel operation units and multiple gesture template libraries of different angle classes. These parallel operation units respectively find out gesture templates most resembling the edge parts in the gesture template libraries of different angle classes. The optimal template selection means selects an optimal gesture template from the resembling gesture templates found out by the parallel operation units. The display terminal is for displaying an image of the optimal gesture template. Thereby, marker-less and real-time gesture recognition is achieved.
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Citations
28 Claims
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1. A gesture recognition system, comprising:
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an image pick-up device, for capturing an image data containing a hand image; a template database, for recording multiple gesture templates representing different gestures, wherein the gesture templates are classified by angles and are respectively stored in gesture template libraries of different angle classes; a processor, for communicating with the image pick-up device and obtaining the image data, finding out a skin part from the image data, producing a skin edge by using an edge detection means, and then classifying the skin edge into multiple edge parts of different angle classes according to angles of the skin edge; an operation engine, having multiple parallel operation units respectively for performing template matching at different angles, wherein the edge parts of different angle classes are respectively sent to different parallel operation units for template matching, so as to find out the gesture templates most resembling the edge parts in the corresponding gesture template libraries of different angle classes; an optimal template selection means, for further selecting an optimal gesture template from the resembling gesture templates found out by the parallel operation units; and a display terminal, for displaying a gesture image represented by the optimal gesture template. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A gesture recognition method, comprising:
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a. establishing a template database, storing gesture edges representing different gestures as gesture templates, and classifying the gesture templates by angles and storing the classified gesture templates into gesture template libraries of different angle classes; b. capturing an image data containing a hand image; c. finding out a skin part from the image, and performing an edge detection on the skin part to obtain a skin edge of the skin part; d. classifying by angles the skin edge into multiple edge parts of different angle classes; e. matching the edge parts of different angle classes with the gesture templates in the gesture template libraries of different angle classes, so as to find out the gesture templates most resembling the edge parts in the gesture template libraries of different angle classes, respectively; f. finding out an optimal gesture template from the resembling gesture templates at different angles found out in the previous step; and g. displaying a gesture image represented by the optimal gesture template found out in the previous step. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28)
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Specification