Object Structure from Noisy Images.
Gabriele Peters · 2003
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 3.6◦, even if they are degraded considerably by Gaussian noise.