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基于奇对称滤波器对比度极性的纹理分离。

Texture segregation on the basis of contrast polarity of odd-symmetric filters.

作者信息

Grieco Alba, Casco Clara, Roncato Sergio

机构信息

Dipartimento di Psicologia Generale, Università degli Studi di Padova, Italy.

出版信息

Vision Res. 2006 Oct;46(20):3526-36. doi: 10.1016/j.visres.2006.05.002. Epub 2006 Jun 27.

Abstract

This is the first study to demonstrate the selectivity of learning for contrast polarity. The finding is the main result of an investigation into the existence of central and peripheral vision mechanisms selective for contrast polarity within the texture-segregation process, using the perceptual learning paradigm in a detection task. Energy models (Malik & Perona, 1990) exclude segregation of textures composed of elements of odd-symmetric luminance profile by contrast polarity differences. Here the target was a Gabor patch (0.8 deg) of 1 cyc/deg in sine phase (odd-symmetry) embedded in a background of mirror-image elements. Our results showed that, in fovea, segregation on the basis of contrast polarity was above threshold from the first session. After learning, the target popped-out in both central and peripheral vision for durations over 10 ms. Our major result is that learning is selective for contrast polarity; it is also selective for orientation and position, all characteristics distinctive of early processing. Since the learning effects were obtained with texture composed of odd-symmetric mirror-image elements, they indicate that the output from odd-symmetric filters was not excluded or inhibited in texture segmentation, but instead played an active role. Our data support models of texture segmentation, in which detection of texture gradient is achieved on the basis of early cortical process, before the non-linear transformation of their output.

摘要

这是第一项证明学习对对比度极性具有选择性的研究。该发现是一项调查的主要结果,该调查利用检测任务中的知觉学习范式,探究在纹理分离过程中对对比度极性具有选择性的中央和周边视觉机制的存在情况。能量模型(Malik & Perona,1990)排除了由具有奇对称亮度分布的元素组成的纹理通过对比度极性差异进行的分离。在这里,目标是一个嵌入在镜像元素背景中的1周/度正弦相位(奇对称)的0.8度加博尔斑。我们的结果表明,在中央凹,基于对比度极性的分离从第一次实验开始就高于阈值。学习后,目标在中央和周边视觉中都会弹出,持续时间超过10毫秒。我们的主要结果是,学习对对比度极性具有选择性;它对方向和位置也具有选择性,所有这些都是早期处理的独特特征。由于学习效果是通过由奇对称镜像元素组成的纹理获得的,这表明在纹理分割中,奇对称滤波器的输出并未被排除或抑制,而是发挥了积极作用。我们的数据支持纹理分割模型,在该模型中,纹理梯度的检测是在其输出的非线性变换之前基于早期皮层过程实现的。

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