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Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-8
Kris Woodbeck , School of Information Technology and Engineering, University of Ottawa, Ontario, Canada
Gerhard Roth , School of Information Technology and Engineering, University of Ottawa, Ontario, Canada
Huiqiong Chen , Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
ABSTRACT
We present a biologically motivated classifier and feature descriptors that are designed for execution on Single Instruction Multi Data hardware and are applied to high speed multiclass object recognition. Our feature extractor uses a cellular tuning approach to select the optimal Gabor filters to process a given input, followed by the computation of scale and rotation-invariant features that are sparsified with a lateral inhibition mechanism. Neighboring features are pooled into feature hierarchies whose resonant properties are used to select the most representative hierarchies for each object class. The feature hierarchies are used to train a novel form of Adaptive Resonance Theory classifier for multiclass object recognition. Our model has unprecedented biologically plausibility at all stages and uses the programmable Graphics Processing Unit (GPU) for high speed feature extraction and object classification. We demonstrate the speedup achieved with the use of the GPU and test our model on the Caltech 101 and 15 Scene datasets, where our system achieves state-of-the-art performance.
CITATION
Kris Woodbeck, Gerhard Roth, Huiqiong Chen, "Visual cortex on the GPU: Biologically inspired classifier and feature descriptor for rapid recognition", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-8, doi:10.1109/CVPRW.2008.4563091
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