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Static gesture recognition method based on finger contour and decision-making trees

A technology of gesture recognition and decision tree, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as slow recognition speed

Inactive Publication Date: 2014-08-06
NANJING UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

The disadvantage of this method is that when the template library is large, the recognition speed of this method will be slower

Method used

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  • Static gesture recognition method based on finger contour and decision-making trees
  • Static gesture recognition method based on finger contour and decision-making trees
  • Static gesture recognition method based on finger contour and decision-making trees

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Embodiment Construction

[0059] A static gesture recognition method based on finger outline and decision tree described in the present invention first uses the depth data of Kinect and bone tracking technology to cut out the palm area, and uses the self-adaptive proximity value method to perform foreground detection, and repositions the coordinates of the palm center . Use the circular sequence curve to model the palm outline, calculate the maximum and minimum points on the curve, construct extreme point pairs, and segment the contours of each finger and the position of the wrist. Then, gestures with the same number of fingers are learned and classified by extracting finger features and using decision trees. For different numbers of fingers, the present invention adopts different decision trees, so it is a multi-decision tree classification method.

[0060] Below in conjunction with accompanying drawing, the present invention is explained in more detail:

[0061] Step 1: If figure 1 As shown, use K...

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Abstract

The invention discloses a static gesture recognition method based on a finger contour and decision-making trees. The method comprises the steps that a Kinect depth image is used as a data source at first, the approximate coordinates of the palm are positioned through the Kinect skeleton tracking function, and a square area containing the palm is cut out with the coordinates as a center; the self-adaptive adjacent value method is used for conducting foreground detection on the area, and the palm contour is detected after appropriate image morphology processing is conducted on the foreground image; a circumference sequence curve is used for conducting modeling on the palm contour, and the extreme point pair method is utilized for accurately distinguishing each finger contour and a wrist contour and building gesture feature sets; at last, the decision-making trees are used for training and recognizing the gesture feature sets with different finger numbers.

Description

technical field [0001] The invention relates to an image processing method of computer vision, in particular to a static gesture recognition method based on a finger outline and a decision tree from a depth image. Background technique [0002] The focus of research on static gesture recognition is the gesture of the hand and a single hand shape. It is usually based on vision-based 2D gesture recognition. The hand area is segmented by color, depth or motion, and various features are extracted from it, and then the classifier is trained. , and finally run the test. The simplest static gesture recognition system, which distinguishes digital gestures by looking for several fingers, does not need to design a classifier, but it is ineffective for complex hand shape recognition. Currently commonly used algorithms for recognizing complex hand shapes include methods based on template matching and methods based on neural networks. [0003] Template matching is the most primitive and...

Claims

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Application Information

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IPC IPC(8): G06K9/00
Inventor 路通胡炜
Owner NANJING UNIV
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