Motion capture data segmentation method based on metric mds and improved oblique space distance

A space distance and data segmentation technology, applied in image data processing, image analysis, instruments, etc., can solve the problems of high complexity, high-dimensional data processing and analysis time-consuming and labor-intensive, and achieve high stability and accurate segmentation point set effect
CN105374052BActive Publication Date: 2018-02-13DALIAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
DALIAN UNIV
Publication Date
2018-02-13

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Abstract

A human motion capture data segmentation method based on metric MDS and improved oblique space distance, including the following steps: S1: Perform multi-dimensional scale analysis on the original sequence to realize the mapping from high-dimensional original data to low-dimensional space; S2: In low-dimensional Draw a specified window in the space, and calculate the improved oblique spatial distance between the data in the specified window and each frame of the previous segment; S3: The final segmentation point set is obtained by performing similarity detection on the improved oblique spatial distance, and an independent Semantic motion fragments. On the premise of maintaining the correlation and topology of the original data, this method greatly improves the accuracy of data segmentation.
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Description

technical field

[0001] The invention relates to a human motion capture data segmentation method based on metric MDS and improved oblique space distance, which belongs to the technical field of computer image processing. Background technique

[0002] In recent years, with the development of human motion capture technology, the application field of computer animation is also expanding. We use motion capture equipment to efficiently and quickly obtain realistic 3D human motion data, and apply the obtained data to various field. Human body motion is usually composed of multiple motions, and is complex and changeable. Therefore, it is not only complicated but also time-consuming and labor-intensive to directly process long-sequence motion capture data. Therefore, long motion sequences need to be segmented into segments with independent semantics for later reuse.

[0003] Finding the segmentation features between different actions is the key to segmenting human motion sequences....

Claims

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