Applied Computational Neuroscience UGR
Applied Computational Neuroscience UGR
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J. Díaz
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Prediction of Manipulation Actions
On-chip semidense representation map for dense visual features driven by attention processes
Real-time visual saliency architecture for FPGA with top-down attention modulation
Pipelined architecture for real-time cost-optimized extraction of visual primitives based on FPGAs
A multi-resolution approach for massively-parallel hardware-friendly optical flow estimation
Bottom-up visual attention model based on FPGA
Massive parallel-hardware architecture for multiscale stereo, optical flow and image-structure computation
Parallel architecture for hierarchical optical flow estimation based on FPGA
Real-time architecture for a robust multi-scale stereo engine on FPGA
Vector disparity sensor with vergence control for active vision systems
Local image phase, energy and orientation extraction using FPGAs
Image Processing Architecture for Local Features Computation
Real-Time System for High-Image Resolution Disparity Estimation
Dealing with the Perspective Distortion to Detect Overtaking Cars for Driving Assistance, Pattern Recognition and Image Analysis
Hardware Event-driven Simulation Engine for Spiking Neural Networks
A Space Variant Mapping Architecture for Reliable Car Segmentation
Event-driven simulation engine for spiking neural networks on a chip
Sistema de control basado en visión y propiacepción de robots con FPGA
Cómputo de flujo óptico en tiempo real mediante FPGAs
Real-time visual motion detection of overtaking cars for driving assistance using FPGAs
Real time optical flow processing system
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