Object Structure from Noisy Images

G. Peters (Germany)


Computer Vision, Noise, Pose Estimation, Tracking, 3DObject Recognition


We describe the establishment of a compound object model for object recognition purposes which provides the frame for the extraction of object structure from images degraded by noise. Our vision system is inspired by cognitive prin ciples. From a set of sample views we automatically gen erate a sparse and view-based object representation, which contains enough information to represent the object for all poses. To verify this property we apply it in a pose es timation task with noisy and unfamiliar test views of the object. With an appropriate number of views in the object representation the proposed method shows a good selectiv ity and is able to distinguish views with a distance of only , even if they are degraded considerably by Gaussian noise.

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