27 February 2015 Real-time object recognition in multidimensional images based on joined extended structural tensor and higher-order tensor decomposition methods
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Abstract
In this paper a system for real-time recognition of objects in multidimensional video signals is proposed. Object recognition is done by pattern projection into the tensor subspaces obtained from the factorization of the signal tensors representing the input signal. However, instead of taking only the intensity signal the novelty of this paper is first to build the Extended Structural Tensor representation from the intensity signal that conveys information on signal intensities, as well as on higher-order statistics of the input signals. This way the higher-order input pattern tensors are built from the training samples. Then, the tensor subspaces are built based on the Higher-Order Singular Value Decomposition of the prototype pattern tensors. Finally, recognition relies on measurements of the distance of a test pattern projected into the tensor subspaces obtained from the training tensors. Due to high-dimensionality of the input data, tensor based methods require high memory and computational resources. However, recent achievements in the technology of the multi-core microprocessors and graphic cards allows real-time operation of the multidimensional methods as is shown and analyzed in this paper based on real examples of object detection in digital images.
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Boguslaw Cyganek, Boguslaw Cyganek, Bogdan Smolka, Bogdan Smolka, } "Real-time object recognition in multidimensional images based on joined extended structural tensor and higher-order tensor decomposition methods", Proc. SPIE 9400, Real-Time Image and Video Processing 2015, 94000T (27 February 2015); doi: 10.1117/12.2083367; https://doi.org/10.1117/12.2083367
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