Object Detection and Contextual Priming
Motivation: There is general consensus that context can be a rich source of information about an object’s identity, location and scale. In fact, the structure of many real-world scenes is governed by strong configurational rules akin to those that apply to a single object. There is increasing evidence of an early use of contextual information in human perception. However, object-centered approaches dominate the research in computer vision. Under favorable conditions, the multiplicity of cues (color, shape, texture) in the retinal image produced by an object seems to provide enough information to unambiguously determine the object category. Under such high quality viewing conditions, the object recognition mechanisms could rely exclusively on intrinsic object features ignoring the background. However, in situations with poor viewing quality (caused, for instance, by large distances) context appears to play a major role in enhancing the reliability of recognition. In the absence of enough local evidence about an object’s identity, the scene structure and prior knowledge of world regularities provide additional information for recognizing and localizing an object. Even when objects can be identified via intrinsic information, context can simplify the object discrimination by cutting down on the number of object categories, scales and positions that need to be considered.