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用于伽柏图像分解的松弛网络。

Relaxation network for Gabor image decomposition.

作者信息

Pattison T R

机构信息

Department of Electrical and Electronic Engineering, University of Adelaide, South Australia.

出版信息

Biol Cybern. 1992;67(1):97-102. doi: 10.1007/BF00201806.

Abstract

The so-called "simple cells" in layer IV of feline primary visual cortex have been shown to have Gabor function spatial receptive field profiles (RFP's). Since Gabor functions are not mutually orthogonal, the decomposition of an image into Gabor function coefficients is usually performed by minimising some measure of the error between the original image and that reconstructed from the coefficients. A cortical relaxation model is proposed which performs this minimisation implicitly, and is used to examine the biological relevance and feasibility of reconstruction error minimisation.

摘要

猫初级视觉皮层IV层中的所谓“简单细胞”已被证明具有伽柏函数空间感受野轮廓(RFP)。由于伽柏函数不是相互正交的,将图像分解为伽柏函数系数通常是通过最小化原始图像与从这些系数重建的图像之间的某种误差度量来进行的。本文提出了一种皮层松弛模型,该模型隐式地执行这种最小化,并用于检验重建误差最小化的生物学相关性和可行性。

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