Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
A Bio-inspired Connectionist Architecture for Visual Classification of Moving Objects
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Quantum Bio-inspired Vision Model on System-on-a-Chip (SoC)
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
Scaling analysis of a neocortex inspired cognitive model on the Cray XD1
The Journal of Supercomputing
A context switching streaming memory architecture to accelerate a neocortex model
Microprocessors & Microsystems
A bio-inspired connectionist approach for motion description through sequences of images
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Neuromimetic indicators for visual perception of motion
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
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Visual motion provides useful information to understand the dynamics of a scene to allow intelligent systems interact with their environment. Motion computation is usually restricted by real time requirements that need the design and implementation of specific hardware architectures. In this paper, the design of hardware architecture for a bio-inspired neural model for motion estimation is presented. The motion estimation is based on a strongly localized bio-inspired connectionist model with a particular adaptation of spatio-temporal Gabor-like filtering. The architecture is constituted by three main modules that perform spatial, temporal, and excitatory-inhibitory connectionist processing. The biomimetic architecture is modeled, simulated and validated in VHDL. The synthesis results on a Field Programmable Gate Array (FPGA) device show the potential achievement of real-time performance at an affordable silicon area.