A simple and successful design for a panoramic stereo system that uses a single camera and multiple mirrors is presented. A wide-baseline stereo enhances the three-dimensional reconstruction accuracy even with a single camera. The feasibility of the system as a practical stereo sensor has been demonstrated with experiments in an indoor environment.
This paper presents a combined model-based 3D object recognition method motivated by the robust properties of human vision. The human visual system (HVS) is very efficient and robust in identifying and grabbing objects, in part because of its properties of visual attention, contrast mechanism, feature binding, multiresolution and part-based representation. In addition, the HVS combines bottom-up and top-down information effectively using combined model representation. We propose a method for integrating these aspects under a Monte Carlo method. In this scheme, object recognition is regarded as a parameter optimization problem. The bottom-up process initializes parameters, and the top-down process optimizes them. Experimental results show that the proposed recognition model is feasible for 3D object identification and pose estimation.
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