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Recognizing Volumetric Models in the Presence of Uncertainty

Classically, the problem of object recognition has long been formulated as that of recognizing the instance of some object in a scene through information acquired from an image. This has motivated such questions as how to compute stable and effective representations for objects from image data and how to correctly identify an unknown object from its representation. Our interest, and the primary focus of this research, is how such decisions can be made in the presence of uncertainty and how one can explicitly represent and reason about the uncertainty. We propose a new framework for parametric shape recognition based on a probabilistic model of inverse theory first introduced by Tarantola in [Tarantola:87]. The key result is a method for generating classifiers in the form of conditional probability densities in order to recognize an unknown from a set of reference models. Our procedure is automatic. Off-line, it invokes an autonomous process to estimate reference model parameters and their statistics. On-line, during measurement, it combines these with a priori context-dependent information, as well as the parameters and statistics estimated for an unknown object, into a single description. That description, a conditional probability density function, represents the likelihood of correspondence between the unknown and a particular reference model. The context of this project is three-dimensional object recognition in which objects are represented by parametric shape descriptors and their uncertainties. Object models are constructed through a process of autonomous exploration [Whaite:94] in which a part-oriented, articulated description of an object is inferred through successive probes with a laser range-finding system. The set-up used to perform experiments is a two-axis laser range-finder mounted on an inverted PUMA-560 manipulator, and a rotary table. For any particular viewpoint, a process of bottom-up shape analysis leads to an articulated model of the object's shape in which each part is represented by a superellipsoid primitive. An experimental system has been implemented in which objects are represented by either single or multiple parametric models (e.g. superellipsoids, deformable solids). These experiments have indicated that recognition performance is near perfect for cases in which complete surface information is accessible to the algorithm, and falls off gracefully (minimal false-positive response) when only partial information is available. This has led to the development of a sequential recognition strategy based on the concept of an informative viewpoint in which belief measures associated with each classification can be used as feedback for the acquisition of further evidence as required.
T. Arbel, F.P. Ferrie

Scene and Composite Model



next up previous contents
Next: Partitioning Range Images Up: Active Perception Previous: Multiple View Integration



Thierry Baron
Mon Nov 13 10:43:02 EST 1995