Higher-dimensional dynamic data processing method

Abstract

The invention provides a high-dimensional dynamic data processing method. Firstly, a low-dimensional variable corresponding to high-dimensional dynamic data is calculated; secondly, the map that lower-dimensional space spanned by the low-dimensional variable is mapped to the high-dimensional space where the high-dimensional data is positioned is calculated; finally, samples are acquired in the lower-dimensional space and are mapped to the high-dimensional space by utilizing the map to form new high-dimensional dynamic data. The high-dimensional dynamic data processing method obtains more vivid visual effect and can process dynamic textures in most natural scenes when being applied to the aspect of a dynamic texture image sequence, and can synthesize somatic movement data which is appointed and constrained by a user when being applied to the aspect for processing the three-dimensional human movement acquisition data.

Description

technical field [0001] The invention relates to a high-dimensional dynamic data processing method, in particular to the analysis, synthesis and control of dynamic texture image sequences and 3D human motion data. Background technique [0002] Dynamic data generally refers to a type of data that has a time series relationship between data units. Typical dynamic data include dynamic texture image sequences, 3D human motion data, etc. Due to the high dimensionality of these dynamic data, how to deal with these high-dimensional data has become a difficult point in various application problems. [0003] A dynamic texture image sequence refers to an image sequence that presents a statistical characteristic in time and space. Such as jumping flames, fluttering flags, etc. An image I whose length and width are m and n respectively m×n , which can be seen as R m×n A point in image space. The value of m×n is usually greater than 1000 (for example, for an image with a size of 35×...

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