Model of frequency analysis in the visual cortex and the shape from texture problem
Model of frequency analysis in the visual cortex and the shape from texture problem
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DOI：
10.1007/s11263-007-0048-x
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发表时间：
2008-02-01
影响因子：
19.5
通讯作者：
Herault, Jeanny
中科院分区：
文献类型：
--
作者：
Massot, Corentin;Herault, Jeanny
本文解决的问题：在初级区域V1中存在的皮层细胞的水平，是足够的信息，以提取局部的角度从纹理？从视觉V1区的复杂细胞模型出发，提出了一种生物学上合理的频率分析算法，用于从纹理恢复形状的问题。首先，特定的对数正态滤波器的设计，以取代经典的Gabor滤波器，因为它们的理论特性和它们的生物相容性。这些滤波器在频率和方向上是可分离的，并且它们更好地对图像频谱进行采样，这使得它们适合于任何模式分析技术。设计了一种无需选择最佳局部尺度的局部频率估计方法。基于此频率分析模型，局部分解的图像到补丁导致估计的局部频率变化，这是用来解决的问题，恢复的形状从纹理。根据局部频率与几何参数之间的解析关系，在透视投影下，可以恢复原始图像的方向和形状。该方法的准确性进行评估和讨论不同种类的纹理，规则和不规则，平面和曲面，也对自然场景和心理物理刺激。它与现有的最佳方法相比，具有较低的计算成本。最后讨论了模型的生物相容性。
This paper addresses the question: at the level of cortical cells present in the primary area V1, is the information sufficient to extract the local perspective from the texture? Starting from a model of complex cells in visual area V1, we propose a biologically plausible algorithm for frequency analysis applied to the shape from texture problem. First, specific log-normal filters are designed in replacement of the classical Gabor filters because of their theoretical properties and of their biological plausibility. These filters are separable in frequency and orientation and they better sample the image spectrum which makes them appropriate for any pattern analysis technique. A method to estimate the local frequency in the image, which discards the need to choose the best local scale, is designed. Based on this frequency analysis model, a local decomposition of the image into patches leads to the estimation of the local frequency variation which is used to solve the problem of recovering the shape from the texture. From the analytical relation between the local frequency and the geometrical parameters, under perspective projection, it is possible to recover the orientation and the shape of the original image. The accuracy of the method is evaluated and discussed on different kind of textures, both regular and irregular, with planar and curved surfaces and also on natural scenes and psychophysical stimuli. It compares favorably to the best existing methods, with in addition, a low computational cost. The biological plausibility of the model is finally discussed.