Malik J, Perona P
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley 94720.
J Opt Soc Am A. 1990 May;7(5):923-32. doi: 10.1364/josaa.7.000923.
We present a model of human preattentive texture perception. This model consists of three stages: (1) convolution of the image with a bank of even-symmetric linear filters followed by half-wave rectification to give a set of responses modeling outputs of V1 simple cells, (2) inhibition, localized in space, within and among the neural-response profiles that results in the suppression of weak responses when there are strong responses at the same or nearby locations, and (3) texture-boundary detection by using wide odd-symmetric mechanisms. Our model can predict the salience of texture boundaries in any arbitrary gray-scale image. A computer implementation of this model has been tested on many of the classic stimuli from psychophysical literature. Quantitative predictions of the degree of discriminability of different texture pairs match well with experimental measurements of discriminability in human observers.
我们提出了一种人类前注意纹理感知模型。该模型由三个阶段组成：(1) 图像与一组偶对称线性滤波器进行卷积，然后进行半波整流，以给出一组模拟V1简单细胞输出的响应；(2) 抑制作用，在空间上局部化，在神经响应轮廓内和轮廓之间，当相同或附近位置存在强响应时，会抑制弱响应；(3) 使用宽奇对称机制进行纹理边界检测。我们的模型可以预测任意灰度图像中纹理边界的显著性。该模型的计算机实现已经在许多来自心理物理学文献的经典刺激上进行了测试。不同纹理对可辨别程度的定量预测与人类观察者可辨别性的实验测量结果非常匹配。
