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模拟对比敏感度对光栅面积和空间频率的依赖性。

Modelling the dependence of contrast sensitivity on grating area and spatial frequency.

作者信息

Rovamo J, Luntinen O, Näsänen R

机构信息

Department of Vision Sciences, Aston University, Aston Triangle, Birmingham, England.

出版信息

Vision Res. 1993 Dec;33(18):2773-88. doi: 10.1016/0042-6989(93)90235-o.

DOI:10.1016/0042-6989(93)90235-o
PMID:8296472
Abstract

We modelled the human foveal visual system in a detection task as a simple image processor comprising (i) low-pass filtering due to the optical transfer function of the eye, (ii) high-pass filtering of neural origin, (iii) addition of internal neural noise, and (iv) detection by a local matched filter. Its detection efficiency for gratings was constant up to a critical area but then decreased with increasing area. To test the model we measured Michelson contrast sensitivity as a function of grating area at spatial frequencies of 0.125-32 c/deg for simple vertical and circular cosine gratings. In circular gratings luminance was sinusoidally modulated as a function of the radius of the grating field. In agreement with the model, contrast sensitivity at all spatial frequencies increased in proportion to the square-root of grating area at small areas. When grating area exceeded critical area, the increase saturated and contrast sensitivity became independent of area at large grating areas. Spatial integration thus obeyed Piper's law at small grating areas. The critical area of spatial integration, marking the cessation of Piper's law, was constant in solid degrees at low spatial frequencies but inversely proportional to spatial frequency squared at medium and high spatial frequencies. At low spatial frequencies the maximum contrast sensitivity obtainable by spatial integration increased in proportion to spatial frequency but at high spatial frequencies it decreased in proportion to the cube of the increasing spatial frequency. The increase was due to high-pass filtering of neural origin (lateral inhibition) and the decrease was mainly due to the optical transfer function of the eye. Our model explained 95% of the total variance of the contrast sensitivity data.

摘要

我们将人类中央凹视觉系统在检测任务中建模为一个简单的图像处理器,它包括:(i) 由于眼睛的光学传递函数导致的低通滤波;(ii) 神经源性高通滤波;(iii) 内部神经噪声的添加;以及(iv) 由局部匹配滤波器进行检测。其对光栅的检测效率在达到临界面积之前保持恒定,但随后随着面积增加而降低。为了测试该模型,我们测量了简单垂直和圆形余弦光栅在空间频率为0.125 - 32 c/deg时,作为光栅面积函数的迈克尔逊对比度敏感度。在圆形光栅中,亮度作为光栅场半径的函数进行正弦调制。与模型一致,在小面积时,所有空间频率下的对比度敏感度与光栅面积的平方根成比例增加。当光栅面积超过临界面积时,增加趋于饱和,并且在大光栅面积时对比度敏感度变得与面积无关。因此,在小光栅面积时空间积分遵循派珀定律。标志着派珀定律停止的空间积分临界面积,在低空间频率下以立体角为单位保持恒定,但在中高空间频率下与空间频率的平方成反比。在低空间频率下,通过空间积分可获得的最大对比度敏感度与空间频率成比例增加,但在高空间频率下,它与增加的空间频率的立方成比例降低。增加是由于神经源性高通滤波(侧向抑制),而降低主要是由于眼睛的光学传递函数。我们的模型解释了对比度敏感度数据总方差的95%。

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