Encoding visual information using anisotropic transformations

被引：16

作者：

Boccignone, G

Ferraro, M

Caelli, T

机构：

[1] Univ Salerno, Dipartimento Informaz & Ingn Elettr, I-84084 Salerno, Italy

[2] Univ Turin, Dipartimento Fis Sperimentale, I-10125 Turin, Italy

[3] Univ Alberta, Dept Comp Sci, Edmonton, AB T6G 2H1, Canada

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2001年 / 23卷 / 02期

关键词：

scale space; anisotropic diffusion; entropy production; feature encoding;

D O I：

10.1109/34.908970

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The evolution of information in images undergoing fine-to-coarse anisotropic transformations is analyzed by using an approach based on the theory of irreversible transformations. In particular, we show that, when an anisotropic diffusion model is used, local variation of entropy production over space and scale provides the basis for a general method to extract relevant image features.

引用

页码：207 / 211

页数：5

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