Combining Geometric and Probabilistic Structure for Active Recognition of 3D Objects

Abstract

Direct perception is incomplete: objects may show ambiguous appearances, and sensors have a limited sensitivity. Consequently, the recognition of complex 3D objects necessitates an exploratory phase to be able to deal with complex scenes or objects. The variation of object appearance when the viewpoint is modified or when the sensor parameters are changed is an idiosyncratic feature which can be organized in the form of an aspect graph . Standard geometric aspect graphs are difficult to build. This article presents a generalized probabilistic version of this concept. When fitted with a Markov chain dependance, the aspect graph acquires a quantitative predictive power. Tri-dimensional object recognition becomes translated into a problem of Markov chain discrimination. The asymptotic theory of hypothesis testing, in its relation to the theory of large deviations, gives then a global evaluation of the statistical complexity of the recognition problem.