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

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.