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Ihis paper proposes a new object recognition algorithm using robotic context information for humanoid robots. For more robust object recognition for less textured objects, we combine shape-based interest points and local appearance-based descriptors computed in a neighborhood around each detected interest point. The combination is used as a basic feature for matching, and candidate feature correspondences...
In this paper, we propose a region-based object recognition (RBOR) method to identify objects from complex real-world scenes. First, the proposed method performs color image segmentation by a simplified pulse-coupled neural network (SPCNN) for the object model image and test image, and then conducts a region-based matching between them. Hence, we name it as RBOR with SPCNN (SPCNN-RBOR). Hereinto,...
We propose a new categorical object recognition algorithm robust to scale changes. We first partition an input image into k regions by using depth data from an RGB-D sensor, and then we estimate the object scale for each partitioned region. Finally, scaled model is applied to recognize the object.
This paper proposes a new method to detect doors using context-based object recognition. Particularly, in order to improve the efficiency of object recognition, we utilize robotic context such as the robot's viewpoint and the average height of doorknobs. The robotic context is used to make a region of interest in a captured image which reduces both the computational time and false-postive rate in...
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