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Approximations in the HMAX Model
dc.description.abstractThe HMAX model is a biologically motivated architecture for computer vision whose components are in close agreement with existing physiological evidence. The model is capable of achieving close to human level performance on several rapid object recognition tasks. However, the model is computationally bound and has limited engineering applications in its current form. In this report, we present several approximations in order to increase the efficiency of the HMAX model. We outline approximations at several levels of the hierarchy and empirically evaluate the trade-offs between efficiency and accuracy. We also explore ways to quantify the representation capacity of the model.en_US
dc.format.extent12 p.en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Unporteden
dc.subjectobject recognition, approximationen_US
dc.titleApproximations in the HMAX Modelen_US

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Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported