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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Neural Computation
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Neural Computation
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NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Image Processing
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SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
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This paper offers a sparse, multiscale representation of objects. It captures the object appearance by selection from a very large dictionary of Gaussian differential basis functions. The learning procedure results from the matching pursuit algorithm, while the recognition is based on polynomial approximation to the bases, turning image matching into a problem of polynomial evaluation. The method is suited for coarse recognition between objects and, by adding more bases, also for fine recognition of the object pose. The advantages over the common representation using PCA include storing sampled points for recognition is not required, adding new objects to an existing data set is trivial because retraining other object models is not needed, and significantly in the important case where one has to scan an image over multiple locations in search for an object, the new representation is readily available as opposed to PCA projection at each location. The experimental result on the COIL-100 data set demonstrates high recognition accuracy with real-time performance.