Computer Vision and Image Understanding
Introduction to Robotics
Feature Space Trajectory Methods for Active Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-based similarity search in 3D object databases
ACM Computing Surveys (CSUR)
CSS-AFFN: a dataset representation model for active recognition systems
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
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This paper describes a new strategy of optimal view point selection for 3D object recognition purposes. Feature vectors of an object viewed from specific viewpoints are mapped on the nodes of a tessellated sphere which we call D-Sphere. The next best view (NBV) is established on the D-Sphere and is defined as the view that yields the maximum dissimilarity among the set of candidate objects (hypotheses). The purpose of finding a NBV is discriminating quickly between hypotheses. Our method aligns all hypotheses into a tessellated sphere. Then it calculates the view point (node) which exhibits the maximum dissimilarity among the corresponding views of the candidate objects. The node values of the D-Sphere are weighted taking into account the kinematics costs involved in the movement of the robot arm that carries the camera. Experiments with a robotic hand-eye system are also described