Object Recognition Using Prediction And Probabilistic Match

PREMIO is a CAD-based object recognition and localization system that uses CAD models of 3D objects and knowledge of lighting and sensors to predict the detectability of features in various views of the object. The predictions that PREMIO produces are powerful new tools in recognizing and determining the pose of a 3D object. In order to take advantage of these tools, we have developed a new matching algorithm: an iterative-deepening-A* search that explicitly takes advantage of the predictions to guide the search and reduce the search space. The purpose of this paper is to describe the matching algorithm and illustrative results. I Introduction Most feature-based matching schemes assume that all the features that are potentially visible in a view of an object will appear with equal probability. The resultant matching algorithms have to allow for “errors” without really understanding what the errors mean. PREMIO [2] is an object recognition/localization system that attempts to model some of the physical processes that can cause these “errors”. It uses CAD models of 3D objects and knowledge of lighting and sensors to predict the detectability of features in various views of the object. From these predictions, PREMIO calculates probabilities for each feature of being detected as a whole, being missed entirely, or breaking into pieces and conditional probabilities of the detection of one feature given the detection or nondetection of other features. The predictions that PREMIO produces are powerful new tools in recognizing and determining the pose of a 3D object. In order to take advantage of these tools, we have developed a new matching algorithm: an iterative-deepening-A* search that explicitly takes advantage of the probabilities to guide the search and prune the tree. The matching algorithm represents a large theoretical effort that is actually independent of the PREMIO system. The algorithm has been implemented as a C program and teated on data specifically generated to fit the abstract paradigm for the probabilistic search. The purpose of this paper is to describe the theory, the algorithm, and illustrative results.