Abstract
The paper presents a framework for object recognition with the multi- model space-variant approach in the log-polar domain built into the multilinear tensor classifier. Thanks to this the method allows recognition of rotated and/or scaled objects taking advantage of the foveal and peripheral information. Recognition is done in the multilinear subspaces obtained after the higher-order singular value decomposition of the pattern tensor. The experiments show high accuracy and robustness of the proposed method.
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Cyganek, B. (2011). Object Recognition with the HOSVD of the Multi-model Space-Variant Pattern Tensors. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23672-3_53
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DOI: https://doi.org/10.1007/978-3-642-23672-3_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23671-6
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