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BMVC
1998

ORASSYLL: Object Recognition with Autonomously Learned and Sparse Symbolic Representations Based on Local Line Detectors

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ORASSYLL: Object Recognition with Autonomously Learned and Sparse Symbolic Representations Based on Local Line Detectors
We introduce an object recognition system in which objects are represented as a sparse and spatially organized set of local (bent) line segments. The line segments correspond to binarized Gabor wavelets or banana wavelets, which are bent and stretched Gabor wavelets. These features can be metrically organized, the metric enables an efficient learning of object representations. Learning can be performed autonomously by utilizing motor
Norbert Krüger, Niklas Lüdtke
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1998
Where BMVC
Authors Norbert Krüger, Niklas Lüdtke
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