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2014 | OriginalPaper | Buchkapitel

Texture Data Compression of 3D Face Based on Multi-color Simultaneous Algebra

verfasst von : Lijun Ding

Erschienen in: Unifying Electrical Engineering and Electronics Engineering

Verlag: Springer New York

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Abstract

To resolve texture data of the 3D face data capacity storage consumption of larger, computationally intensive problems, this chapter presents a 3D face color texture data compression method, which is constructing a GHA (General Hebb Algorithm) neural network within the framework of the Young–Helmholtz three-color simultaneous algebra, constructing the corresponding network structure and learning algorithm, extracting the principal component of a large amount of the color texture data, and storing them in the form of weight matrix to be used for the texture data compression. The experimental results show that proposed method not only has good PSNR value but also has good generalization ability. The weight matrix is able to compress the texture data of different person faces data with different postures. It has compression universality.

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Metadaten
Titel
Texture Data Compression of 3D Face Based on Multi-color Simultaneous Algebra
verfasst von
Lijun Ding
Copyright-Jahr
2014
Verlag
Springer New York
DOI
https://doi.org/10.1007/978-1-4614-4981-2_132

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