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A Gesture Recognition Method Based on Image Space Pyramid Feature Pack

A pyramid feature and gesture recognition technology, which is applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of slow calculation speed, large calculation amount, and large calculation amount, so as to facilitate design, improve recognition rate, and realize The effect of gesture recognition

Inactive Publication Date: 2017-09-29
LIAONING UNIVERSITY OF PETROLEUM AND CHEMICAL TECHNOLOGY
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AI Technical Summary

Problems solved by technology

[0006] (2) Using Fourier operators to describe gesture features and using the spatial distribution characteristics of palm fingers to represent gestures. These two methods solve the adaptability of features to scale, rotation, and illumination, but the amount of calculation is large and the calculation time is long.
[0012] (1) Template matching requires a large number of training images, and the calculation speed is slow
(2) The design of Adaboost classifier is complex, the amount of calculation is large, and the calculation speed is slow
However, the selected kernel function and the parameters of the kernel function have a great influence on the recognition results.

Method used

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  • A Gesture Recognition Method Based on Image Space Pyramid Feature Pack
  • A Gesture Recognition Method Based on Image Space Pyramid Feature Pack
  • A Gesture Recognition Method Based on Image Space Pyramid Feature Pack

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Embodiment Construction

[0048] Below in conjunction with example the present invention is described in further detail.

[0049] The working process of the present invention includes: firstly performing illumination compensation on all images and using pre-statistical skin color information to set the thresholds of each component in YCrCb color space to realize gesture segmentation. Then, the image space pyramid is constructed by dividing the original image into different blocks, and the description vector is generated by using the feature bag algorithm for each sub-block image of each layer in the image space pyramid. Normalize the generated description vectors. Finally, the training set gesture images are used to train the histogram intersection kernel support vector machine to classify the training set gestures and obtain the optimal classification hyperplane. When the gesture is re-input, the gesture feature vector is obtained through gesture segmentation and feature extraction. Use the trained ...

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Abstract

The invention relates to a gesture recognition method based on an image space pyramid bag of features. The method comprises the following steps of carrying out gesture division of an image with a gesture; extracting and describing features of the divided gesture image; using extracted features to train a histogram intersection core support vector machine; obtaining a gesture type to which a feature vector of the gesture image belongs according to the support vector machine, in order to achieve gesture recognition. The method combines a space pyramid algorithm and a bag of features algorithm, and describes features of overall quantity and distribution of feature points of the gesture image. By means of the histogram intersection core support vector machine, classification of gesture features is achieved, and gesture recognition is achieved further. In addition, the rate of recognizing gestures of multiple similar types is improved.

Description

technical field [0001] The invention belongs to the field of image processing and pattern recognition, in particular to a gesture recognition method based on image feature extraction and support vector machine. Background technique [0002] Modern human-computer interaction is developing towards a more harmonious and natural direction. A hot issue in human-computer interaction research is to enable users to use computers in a convenient and natural way that humans are familiar with. Gesture language is simple and intuitive, and is an effective extension of human-computer interaction. It has a wide range of applications in smart home appliance control, robot control, sign language recognition, and computer game control. [0003] The key technology of gesture recognition lies in the two steps of feature extraction of gesture image and gesture recognition. Currently, the most commonly used image feature extraction methods include feature extraction using edge feature pixels, ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
Inventor 曹江涛余思泉李平
Owner LIAONING UNIVERSITY OF PETROLEUM AND CHEMICAL TECHNOLOGY
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