Jagdish Sabarad

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Advances in neuroscience have enabled researchers to develop computational models of auditory, visual and learning perceptions in the human brain. HMAX, which is a biologically inspired model of the visual cortex, has been shown to outperform standard computer vision approaches for multi-class object recognition. HMAX, while computationally demanding, can(More)
A significant challenge in creating machines with artificial vision is designing systems which can process visual information as efficiently as the brain. To address this challenge, we identify key algorithms which model the process of attention and recognition in the visual cortex of mammals. This paper presents Cover - an FPGA framework for generating(More)
Recently significant advances have been achieved in understanding the visual information processing in the human brain. The focus of this work is on the design of an architecture to support HMAX, a widely accepted model of the human visual pathway. The computationally intensive nature of HMAX and wide applicability in real-time visual analysis application(More)
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