Efficient Recognition of Partially Visible Objects




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Paul G Gottschalk Jerry L Turney Trevor N Mudge Efficient Recognition of Partially Visible Robotics Research Laboratory University of Michigan Ann Arbor Michigan 48109 Objects Using a Logarithmic Complexity Matching Technique Abstract 1 Introduction An important task in computer vision is the recognition of partially visible two dimensional objects in a gray scale image Recent works addressing this problem have attempted to match spatially local features from the image to features generated by models of the objects However many algorithms are considerably less efficient than they might be typically being O IN or worse where I is the number offeatures in the image and N is the number of features in the model set This is invariably due to the feature matching portion of the algorithm In this paper we discuss an algorithm that significantly improves the efficiency offeature matching In addition we show experimentally that our recognition algorithm is and robust Our algorithm uses the local shape of contour segments near critical points represented in accurate slope angle arclength space thetas s space as fundamental feature vectors These feature vectors are further processed by projecting them onto a subspace in thetas s space that is obtained by applying the Karhunen Lo egrave ve expansion to all such features in the set of models yielding the final feature vectors This allows the data needed to store the features to be re duced while retaining nearly all information important for recognition The heart of the algorithm is a technique for performing matching between the observed image features and the precomputed model features which reduces the runtime complexity from O IN to O I log I I log N where I and N are as above The matching is performed using a a kD tree which enables multidibe performed in O log time tree data structure called mensional searches 110 to A problem that has received considerable attention in the computer vision literature is that of recognizing






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