May Keith A, Georgeson Mark A
School of Life & Health Sciences, Aston University, Birmingham B4 7ET, UK.
Vision Res. 2007 Jun;47(13):1721-31. doi: 10.1016/j.visres.2007.02.018. Epub 2007 Apr 30.
In many models of edge analysis in biological vision, the initial stage is a linear 2nd derivative operation. Such models predict that adding a linear luminance ramp to an edge will have no effect on the edge's appearance, since the ramp has no effect on the 2nd derivative. Our experiments did not support this prediction: adding a negative-going ramp to a positive-going edge (or vice-versa) greatly reduced the perceived blur and contrast of the edge. The effects on a fairly sharp edge were accurately predicted by a nonlinear multi-scale model of edge processing [Georgeson, M. A., May, K. A., Freeman, T. C. A., & Hesse, G. S. (in press). From filters to features: Scale-space analysis of edge and blur coding in human vision. Journal of Vision], in which a half-wave rectifier comes after the 1st derivative filter. But we also found that the ramp affected perceived blur more profoundly when the edge blur was large, and this greater effect was not predicted by the existing model. The model's fit to these data was much improved when the simple half-wave rectifier was replaced by a threshold-like transducer [May, K. A. & Georgeson, M. A. (2007). Blurred edges look faint, and faint edges look sharp: The effect of a gradient threshold in a multi-scale edge coding model. Vision Research, 47, 1705-1720.]. This modified model correctly predicted that the interaction between ramp gradient and edge scale would be much larger for blur perception than for contrast perception. In our model, the ramp narrows an internal representation of the gradient profile, leading to a reduction in perceived blur. This in turn reduces perceived contrast because estimated blur plays a role in the model's estimation of contrast. Interestingly, the model predicts that analogous effects should occur when the width of the window containing the edge is made narrower. This has already been confirmed for blur perception; here, we further support the model by showing a similar effect for contrast perception.
在生物视觉中许多边缘分析模型里,初始阶段是一个线性二阶导数运算。此类模型预测,给一条边缘添加线性亮度斜坡对边缘外观不会有影响,因为该斜坡对二阶导数没有作用。我们的实验并不支持这一预测:给正向边缘添加一个负向斜坡(反之亦然)会大大降低边缘的模糊感和对比度。一个非线性多尺度边缘处理模型 [乔治森,M. A.,梅,K. A.,弗里曼,T. C. A.,& 黑塞,G. S.(即将发表)。从滤波器到特征:人类视觉中边缘与模糊编码的尺度空间分析。《视觉杂志》] 准确预测了对相当清晰边缘的这些影响,在该模型中,一阶导数滤波器之后接一个半波整流器。但我们也发现,当边缘模糊度较大时,斜坡对感知模糊的影响更显著,而现有模型并未预测到这种更大的影响。当简单的半波整流器被类似阈值的换能器取代时 [梅,K. A. & 乔治森,M. A.(2007)。模糊边缘看起来模糊,而模糊的边缘看起来清晰:多尺度边缘编码模型中梯度阈值的影响。《视觉研究》,47,1705 - 1720。],该模型对这些数据的拟合有了很大改善。这个改进后的模型正确预测出,对于模糊感知而言,斜坡梯度与边缘尺度之间的相互作用比对对比度感知的相互作用要大得多。在我们的模型中,斜坡使梯度轮廓的内部表示变窄,导致感知模糊度降低。这反过来又降低了感知对比度,因为估计的模糊在模型的对比度估计中起作用。有趣的是,该模型预测,当包含边缘的窗口宽度变窄时,应该会出现类似的效果。这对于模糊感知已经得到证实;在此,我们通过展示对比度感知也有类似效果进一步支持了该模型。