Natural Object Recognition.

An autonomous vehicle that is to operate outdoors must be able to recognize features of the natural world as they appear in ground-level imagery. Geometric reconstruction alone is insufficient for an agent to plan its actions intelligently - objects in the world must be recognized, and not just located. Most work in visual recognition by computer has focused on recognizing objects by their geometric shape, or by the presence or absence of some prespecified collection of locally measurable attributes e.g. spectral reflectance, texture, or distinguished markings. On the other hand, most entities in the natural world defy compact description of their shapes, and have no characteristic features with discriminatory power. As a result, image understanding research has achieved little success in the interpretation of natural scenes. In this thesis we offer a new approach to visual recognition that avoids these limitations and has been used to recognize trees, bushes, grass, and trails in ground-level scenes of a natural environment. Reliable recognition is achieved by employing a large number of relatively simple procedures, and using contextual constraints to identify globally consistent hypotheses.