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Motion diagram transition point selecting method based on nonlinearity manifold learning

A popular learning and motion graphics technology, applied in animation production, image data processing, instruments, etc., can solve the problems of high time complexity and inaccurate selection of jump points

Active Publication Date: 2013-04-03
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 popular learning. The method focuses on solving the problem of jump point selection time in the motion map construction process by establishing and calculating the similarity between key motion data segments. The problem of high complexity and 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 nonlinearity manifold learning
  • Motion diagram transition point selecting method based on nonlinearity manifold learning
  • Motion diagram transition point selecting method based on nonlinearity manifold learning

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Embodiment Construction

[0025] The present invention will be further explained below in conjunction with the accompanying drawings. One embodiment of the present invention unit is:

[0026] The embodiments of the present invention are implemented on the premise of the technical solution of the present invention, and detailed implementation manners and specific operation procedures are given, but the protection scope of the present invention is not limited to the following embodiments.

[0027] Such as figure 1 Shown is a flowchart of the algorithm of the present invention, which specifically includes the following technical links:

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

[0029] The ISOMAP nonlinear manifold learning algorithm is used to reduce the dimensionality of the high-dimensional human motion data to obtain the low-dimensional manifold structure of the original motion sequence. According to the different types of motion, draw matching low-dimensional characteristic ...

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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 transition points of a motion graph based on nonlinear popular 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 physical 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 principles, video technology and art, and has gradually become a comprehensive field of multiple 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 genera...

Claims

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Application Information

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IPC IPC(8): G06T13/40
Inventor 魏小鹏张强姚一
Owner DALIAN UNIV
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