Local edge statistics provide information regarding occlusion and nonocclusion edges in natural scenes

Abstract

Edges in natural scenes can result from a number of different causes. In this study, we investigated the statistical differences between edges arising from occlusions and nonocclusions (reflectance differences, surface change, and cast shadows). In the first experiment, edges in natural scenes were identified using the Canny edge detection algorithm. Observers then classified these edges as either an occlusion edge (one region of an image occluding another) or a nonocclusion edge. The nonocclusion edges were further subclassified as due to a reflectance difference, a surface change, or a cast shadow. We found that edges were equally likely to be classified as occlusion or nonocclusion edges. Of the nonocclusion edges, approximately 33% were classified as reflectance changes, 9% as cast shadows, and 58% as surface changes. We also analyzed local statistical properties like contrast, average edge profile, and slope of the edges. We found significant differences between the contrast values for each category. Based on the local contrast statistics, we developed a maximum likelihood classifier to label occlusion and nonocclusion edges. An 80%-20% cross validation demonstrated that the human classification could be predicted with 83% accuracy. Overall, our results suggest that for many edges in natural scenes, there exists local statistical information regarding the cause of the edge. We believe that this information can potentially be used by the early visual system to begin the process of segregating objects from their backgrounds.

Shape from Contour: Computation and Representation

“…Computationally, color and texture cues can be useful in picking out important edges (e.g., Martin et al 2004), and contour blur information can signal edges caused by shadows and shading (Elder 1999, Figures 1c and 3). Luminance contrast can be used to discriminate boundary edges from other edges (Vilankar et al 2014), andEhinger et al (2017) have shown that a deep neural network (DNN) can be trained to use local luminance, color, texture, and orientation cues to distinguish depth from nondepth edges. Little is known about the physiological mechanisms underlying the human visual system's ability to distinguish between these different physical edge classes.…”

Local Depth Edge Detection in Humans and Deep Neural Networks

“…This may be a more efficient way to approach the problem, since edges (of any kind) make up only a very small percentage of an image. Previous work on this approach comes from Vilankar et al [17], who investigated depth versus non-depth edge classification by human observers. They found that luminance contrast was a particularly strong cue for this task and predicted human edge classification with 83% accuracy.…”

Testing models of peripheral encoding using metamerism in an oddity paradigm

“…A candidate model can be used to predict which images should be metamers. This logic allows the simultaneous experimental testing of a large number of image-based cues (that is, all cues encoded by a given model), complementing approaches to natural scene perception in which at most a handful of cues are manipulated at once (e.g., Alam, Vilankar, Field, & Chandler, 2014;Bex, 2010;Bex, Mareschal, & Dakin, 2007;Bex, Solomon, & Dakin, 2009;Bradley, Abrams, & Geisler, 2014;Dorr & Bex, 2013;Haun & Peli, 2013;McDonald & Tadmor, 2006;Tadmor & Tolhurst, 1994;Thomson, Foster, & Summers, 2000;To, Gilchrist, Troscianko, & Tolhurst, 2011;Vilankar, Golden, Chandler, & Field, 2014;Vilankar, Vasu, & Chandler, 2011; Wallis & Bex, 2012; Wallis, Dorr, & Figure 1. Model predictions and metamerism.…”