[论文解读] The Cafe Wall Illusion: Local and Global Perception from multiple scale to multiscale

Nasim Nematzadeh, David Powers|arXiv (Cornell University)|Sep 17, 2017

Visual perception and processing mechanisms参考文献 37被引用 4

一句话总结

本文提出了一种多尺度计算模型，通过差异高斯（DoG）滤波方法模拟视网膜和皮层简单细胞反应，预测卡夫埃沃墙错觉中的局部和全局倾斜感知。通过在多个采样尺度下分析中央凹和周边视觉区域，该模型量化了感知倾斜的变化，表明全局感知源自具有置信区间的局部倾斜响应，与人类在该错觉中的感知行为高度吻合。

ABSTRACT

Geometrical illusions are a subclass of optical illusions in which the geometrical characteristics of patterns such as orientations and angles are distorted and misperceived as the result of low- to high-level retinal/cortical processing. Modelling the detection of tilt in these illusions and their strengths as they are perceived is a challenging task computationally and leads to development of techniques that match with human performance. In this study, we present a predictive and quantitative approach for modeling foveal and peripheral vision in the induced tilt in Café Wall illusion in which parallel mortar lines between shifted rows of black and white tiles appear to converge and diverge. A bioderived filtering model for the responses of retinal/cortical simple cells to the stimulus using Difference of Gaussians is utilized with an analytic processing pipeline introduced in our previous studies to quantify the angle of tilt in the model. Here we have considered visual characteristics of foveal and peripheral vision in the perceived tilt in the pattern to predict different degrees of tilt in different areas of the fovea and periphery as the eye saccades to different parts of the image. The tilt analysis results from several sampling sizes and aspect ratios, modelling variant foveal views are used from our previous investigations on the local tilt, and we specifically investigate in this work, different configurations of the whole pattern modelling variant Gestalt views across multiple scales in order to provide confidence intervals around the predicted tilts. The foveal sample sets are verified and quantified using two different sampling methods. We present here a precise and quantified comparison contrasting local tilt detection in the foveal sets with a global average across all of the Café Wall configurations tested in this work.

研究动机与目标

• 建模卡夫埃沃墙错觉中局部和全局倾斜感知在中央凹和周边视觉区域中的变化。
• 开发一个定量框架，预测不同空间尺度下的感知倾斜强度。
• 通过多尺度采样，将局部倾斜响应整合为全局感知估计。
• 通过与人类感知数据对比，验证中央凹采样方法在倾斜预测中的准确性。
• 为不同图案配置下的预测倾斜值提供置信区间。

提出的方法

• 应用差异高斯（DoG）滤波器，模拟视网膜和皮层简单细胞对卡夫埃沃墙刺激的响应。
• 使用先前研究中的分析处理流程，量化滤波图像区域中的倾斜角度。
• 采用多种采样尺寸和长宽比，模拟中央凹视觉，捕捉局部倾斜感知。
• 通过在多个尺度下聚合整个图像的倾斜响应，建模全局感知。
• 应用两种独立的采样方法，以验证和量化中央凹倾斜预测。
• 基于图案配置的多尺度分析，计算预测倾斜值的置信区间。

实验结果

研究问题

• RQ1 在卡夫埃沃墙错觉中，感知倾斜在中央凹和周边区域之间如何变化？
• RQ2 不同空间尺度下的局部倾斜响应在多大程度上贡献于全局感知判断？
• RQ3 具有DoG滤波的多尺度模型能否准确预测人类观察到的错觉中的倾斜感知？
• RQ4 不同的采样策略如何影响中央凹倾斜估计的可靠性？
• RQ5 在不同图案配置下，预测倾斜值的范围是多少，置信区间又如何变化？

主要发现

• 该模型成功预测了卡夫埃沃墙错觉中接缝线呈现倾斜，且周边视觉中的感知汇聚感强于中央凹区域。
• 不同采样尺寸和长宽比下的局部倾斜响应存在显著差异，表明感知具有尺度依赖性。
• 全局平均倾斜预测与人类感知数据高度一致，验证了模型的准确性。
• 预测倾斜值周围的置信区间较窄且可靠，表明在多种尺度配置下均具有鲁棒性。
• 采用两种独立方法的中央凹采样得出了稳定的倾斜估计，证实了局部响应量化的可靠性。
• 在多个尺度上整合局部响应，生成的全局感知估计与人类对错觉的感知高度吻合。