Perceptual Organization of Two Dimensional Patterns

人类视觉系统的力量和多功能性在很大程度上来自于它在视网膜编码的图像中发现结构和组织的惊人能力。为了发现和描述结构，视觉系统使用广泛的感知分组/分离机制。这种复杂的机制对于人类识别物体和正确解释视觉场景的能力是绝对必要的。在之前的资助期间，我们对知觉分组机制的研究采取了系统的量化方法。我们建议继续和扩展我们的一般方法，它包括三个主要部分：(1)测量与知觉分组相关的视觉图像的统计特性；(2)建立知觉分组的模型，该模型受视觉图像的统计特性、低水平视觉的生理和心理物理以及计算原理的影响；以及(3)在心理物理实验中检验这些模型和竞争模型的预测。我们提出了两种测量图像统计量的方法。一种方法是从图像中提取局部特征，然后计算简单的共现统计量；例如，从自然图像的代表性集合中提取的局部边缘元素对之间所有可能的几何关系的联合概率。另一种方法是从图像中提取局部特征，然后使用图像跟踪过程来测量贝叶斯共生统计；例如，任何给定的边缘元素对相对于不同的物理轮廓属于相同的物理轮廓的可能性。大多数拟议的建模和心理物理工作将集中在轮廓分组和运动分组的机制上。轮廓分组实验旨在测试和扩展我们成功的基于自然图像统计的轮廓分组模型。运动分组实验将考察运动信息在轮廓分组中的作用以及空间信息在运动分组中的作用。我们还计划测试我们将根据自然视频图像统计数据的测量结果开发的运动分组模型。

The power and versatility of the human visual system derives in large part from its amazing ability to find structure and organization in the images encoded by the retinas. To discover and describe structure, the visual system uses a wide array of perceptual grouping/segregation mechanisms. This sophisticated array of mechanisms is absolutely essential for human ability to recognize objects and correctly interpret visual scenes. During the previous funding period, we have taken a systematic quantitative approach to the study of the perceptual grouping mechanisms. We propose to continue and extend our general approach, which consists of three major parts: (1) measuring the statistical properties of the visual images that are relevant for perceptual grouping, (2) developing models of perceptual grouping that are informed by the statistical properties of visual images, by the physiology and psychophysics of low-level vision, and by computational principles, and (3) testing the predictions of these models and competing models in psychophysical experiments. We propose two methods for measuring image statistics. One method is to extract local features from images and then compute simple co- occurrence statistics; e.g., the joint probabilities of all possible geometrical relationships between pairs of local edge elements extracted from representative collections of natural images. The other method is to extract local features from images and then use an image- tracing procedure to measure the Bayesian co-occurrence statistics; e.g., the likelihood that any given pair of edge elements belong to the same physical contour versus different physical contours. Most of the proposed modeling and psychophysical work will focus on the mechanisms of contour grouping and motion grouping. The contour grouping experiments are directed at testing and extending our successful model of contour grouping based upon natural image statistics. The motion grouping experiments will examine the role of motion information in contour grouping and role of spatial information in motion grouping. We also plan to test motion-grouping models that we will develop from measurements of natural video image statistics.