Motion diagram transition point selecting method based on nonlinear manifold learning

A nonlinear manifold and motion graph technology, applied in animation production, image data processing, instruments, etc., can solve the problems of high time complexity and inaccurate selection

Active Publication Date: 2015-02-11
DALIAN UNIV
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Problems solved by technology

[0006] In view of the above problems, the present invention develops a motion map transition point selection method based on nonlinear manifold learning. The method focuses on solving the jump in the motion map construction process by establishing and calculating the similarity between key motion data segments The time complexity of point selection is high, and the problem of inaccurate selection can improve the construction efficiency of the motion map and make the generated motion data smoother and more natural.

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  • Motion diagram transition point selecting method based on nonlinear manifold learning
  • Motion diagram transition point selecting method based on nonlinear manifold learning
  • Motion diagram transition point selecting method based on nonlinear manifold learning

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[0025] Below in conjunction with accompanying drawing, the present invention is further explained, and an embodiment of the unit of the present invention is:

[0026] The embodiments of the present invention are carried out on the premise of the technical solutions of the present invention, and detailed implementation methods and specific operation processes are given, but the protection scope of the present invention is not limited to the following embodiments.

[0027] Such as figure 1 Shown is the algorithm flow chart of the present invention, and it specifically comprises the following technical links:

[0028] Step 1: Dimensionality reduction analysis of high-dimensional data

[0029] Use the ISOMAP nonlinear manifold learning algorithm to reduce the dimensionality of the high-dimensional human motion data to obtain the low-dimensional manifold structure of the original motion sequence, and draw the matching low-dimensional characteristic curve according to the differen...

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Abstract

The invention discloses a motion diagram transition point selecting method based on nonlinearity manifold learning and belongs to the technical field of computer image processing. The method includes the steps of dimensionality reduction analysis of high dimensional data, extraction of key data segments, calculation of key data segment interframe similarity and construction of motion diagrams.

Description

technical field [0001] The invention relates to a method for selecting a transition point of a motion graph based on nonlinear manifold learning, and belongs to the technical field of computer image processing. Background technique [0002] In recent years, with the advancement of computer software and hardware technology, computer animation technology has developed rapidly. Computer animation refers to the use of graphics and image processing technology, based on solid modeling and realistic display technology, with the help of programming or animation production The software generates a series of scene pictures. It involves many fields such as image processing technology, motion control principle, video technology and art, and has gradually become a comprehensive field of various disciplines and technologies with its unique characteristics. Among them, with the continuous development of motion capture technology, people can use the data captured by capture devices to gene...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T13/40
Inventor 魏小鹏张强姚一
Owner DALIAN UNIV
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