Two dimensional CAD-based object recognition

Abstract

A local feature-aggregation method for recognizing two-dimensional objects based on their CAD models is presented. The method can handle cases in which the objects are translated, rotated, scaled and occluded, and it is well suited for parallel implementation. Two types of local features, the L structures and the U structures, are extracted from the input image and matched with those of a model to search for an object similar to the model. Each of the matches hypothesizes the locations of the object in the input image, and score (similarity measure) is computed and associated with the hypothesized location to indicate the probability of the match. Matches that hypothesize the same location will have the score associated with the location incremented. A cluster of hypothesized locations with high scores indicates the probable existence of the object in the input image.