Gesture segmentation method and system based on global expectation-maximization algorithm

Abstract

The present invention discloses a gesture segmentation method and system based on a global expectation-maximization algorithm. The method comprises: establishing a Gaussian model of a complexion; substituting pixel values of all pixel points of a to-be-segmented image into the Gaussian model of the complexion, so as to obtain a complexion similarity degree of all the pixel points of the to-be-segmented image; according to depth information of the to-be-segmented image and the complexion similarity degree of all the pixel points thereof, obtaining a four-dimensional space model consisting of all points in a three-dimensional space and the complexion similarity degree thereof; and dividing the four-dimensional space model into a plurality of sub-spaces, constructing a loss function for evaluating an hypersurface fitting effect in each sub-space, and minimizing the loss function by using a gradient descent method to obtain a four-dimensional hypersurface of the subspace, and finally obtaining a maximum value of the four-dimensional hypersurface of each subspace according to a gradient ascending direction. According to the method provided by the present invention, comparable mathematical description can be generated, the base of two-model fusion is realized, thereby providing a new basis for fusion of different modal data.

Description

technical field [0001] The invention belongs to the technical field of human-computer interaction image information processing, and more specifically, relates to a gesture segmentation method and system based on a global expectation maximum algorithm. Background technique [0002] Biometric recognition based on image information is a hot spot in computer vision research in recent years. Among them, the recognition of human biometrics has inevitably become the main research content. Further, gesture recognition research is an important research content of human biometrics. , which mainly segments, tracks and recognizes different gestures from image data, and describes and understands them. Gesture recognition technology originates from the research of digital image processing and machine learning algorithms, and gesture segmentation is the foundation and precursor of the gesture recognition research pipeline, which has a decisive impact on the final gesture recognition result...

AI Technical Summary

Problems solved by technology

[0005] However, in the problem of gesture segmentation in the traditional computer vision field, many problems still stand out, such as the segmentation is susceptible to interference, and the EM algorithm is difficult to obtain the global optimal solution. Changes such as shadows, in addition to the background, there is also the interference of skin-like objects. For example, when a human face and gestures appear in the image at the same time, it is difficult to distinguish the two through skin color, so misjudgment is likely to occur

Images