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<p>A robot vision system that automatically generates an object recognition strategy from a 3D model and recognizes the object using this strategy is presented. The appearance of an object from various viewpoints is described in terms of visible 2D features such as parallel lines and ellipses. Features are then ranked according to the number of viewpoints from which they are visible. The rank and feature extraction cost of each feature are used to generate a treelike strategy graph. This graph gives an efficient feature search order when the viewpoint is unknown, starting with commonly occurring features and ending with features specific to a certain viewpoint. The system searches for features in the order indicated by the graph. After detection, the system compares a lines representation generated from the 3D model with the image features to localize the object. Perspective projection is used in the localization process to obtain the precise position and attitude of the object, whereas orthographic projection is used in the strategy generation process to allow symbolic manipulation. Experimental results are given.</p>
object localization; perspective projection; position measurement; attitude determination; feature search strategy; 3D object model; robot vision; parallel lines; ellipses; feature extraction; treelike strategy graph; orthographic projection; strategy generation; symbolic manipulation; computer vision; computerised pattern recognition; computerised picture processing; robots

Y. Okamoto, Y. Kuno and S. Okada, "Robot Vision Using a Feature Search Strategy Generated from a 3D Oobject Model," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 13, no. , pp. 1085-1097, 1991.
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