Statistics of Images for Feedback in Vision: with Applications to Different Vision Problems

Processing in most artificial or biological vision systems starts with early vision which involves the extraction of local visual modalities (like optical flow, disparity and contrast transition etc.) and local image structures (edge-like, junction-like and texture-like structures). Since information in early vision is processed only locally, it is inherently ambiguous. One way to deal with the missing and ambiguous information is to make use of the redundancy of visual information by exploiting the statistical regularities of natural scenes. This book is interested in the ambiguities and the biased and missing information in the processing of optic flow, stereo and junctions using statistical means. It uses statistical properties of images to analyze the extent of the ambiguous processing in optical flow estimation and whether the missing information in stereo can be recovered using interpolation of depth information at edge-like structures. Moreover, it proposes a feedback mechanism for dealing with the bias in junction detection, and another model for recovering the missing depth information in stereo computation using only the depth information at the edges.