Edges and bars: where do people see features in 1-D images?
Edges and bars: where do people see features in 1-D images?
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DOI：
10.1016/j.visres.2004.09.013
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发表时间：
2005-02-01
期刊：
影响因子：
1.8
通讯作者：
Georgeson, MA
中科院分区：
文献类型：
--
作者：
Hesse, GS;Georgeson, MA
在人类和计算机视觉中，有两种主要的特征检测方法-基于亮度分布及其空间导数，或者基于局部对比度能量的空间分布。因此，条和边缘可能分别从亮度和亮度梯度的峰值产生，或者条和边缘可能在局部能量的峰值处找到，其中局部相位跨空间频率对齐。这个定义的基本问题是重要的，因为它指导更详细的模型和早期视觉的解释。哪种方法更好地描述了图像中特征的感知位置？我们使用Morrone和Burr定义的I-D图像类，其中振幅谱是（部分模糊的）方波，并且所有傅立叶分量具有共同的相位。观察者使用光标标记不同测试阶段（实验1）的条形和边缘，或判断具有不同阶段（例如0度和45度;实验2）的轮廓的空间对齐。由两个任务定义的特征位置根据相位偏移的符号系统地向左或向右移动，随着模糊程度的增加而增加。通过亮度峰值（条）和梯度峰值（边缘）的位置很好地预测了这些偏移，但通过能量峰值（通过设计）预测根本没有偏移，却不能很好地预测这些偏移。这些结果鼓励基于高斯导数框架的模型，但不支持人类视觉使用相位对准点来找到局部一阶特征的想法。然而，我们认为，这两种方法目前是不完整的，更好地了解早期视力可能会联合收割机的见解。(C)2004 Elsevier Ltd.保留所有权利。
There have been two main approaches to feature detection in human and computer vision-based either on the luminance distribution and its spatial derivatives, or on the spatial distribution of local contrast energy. Thus, bars and edges might arise from peaks of luminance and luminance gradient respectively, or bars and edges might be found at peaks of local energy, where local phases are aligned across spatial frequency. This basic issue of definition is important because it guides more detailed models and interpretations of early vision. Which approach better describes the perceived positions of features in images? We used the class of I-D images defined by Morrone and Burr in which the amplitude spectrum is that of a (partially blurred) square-wave and all Fourier components have a common phase. Observers used a cursor to mark where bars and edges were seen for different test phases (Experiment 1) or judged the spatial alignment of contours that had different phases (e.g. 0degrees and 45degrees; Experiment 2). The feature positions defined by both tasks shifted systematically to the left or right according to the sign of the phase offset, increasing with the degree of blur. These shifts were well predicted by the location of luminance peaks (bars) and gradient peaks (edges), but not by energy peaks which (by design) predicted no shift at all. These results encourage models based on a Gaussian-derivative framework, but do not support the idea that human vision uses points of phase alignment to find local, first-order features. Nevertheless, we argue that both approaches are presently incomplete and a better understanding of early vision may combine insights from both. (C) 2004 Elsevier Ltd. All rights reserved.