A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields
International Journal of Computer Vision
FPGA Accelerated Simulation of Biologically Plausible Spiking Neural Networks
FCCM '09 Proceedings of the 2009 17th IEEE Symposium on Field Programmable Custom Computing Machines
A high-speed multi-GPU implementation of bottom-up attention using CUDA
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
An FPGA Implementation of Information Theoretic Visual-Saliency System and Its Optimization
FCCM '11 Proceedings of the 2011 IEEE 19th Annual International Symposium on Field-Programmable Custom Computing Machines
Robot steering with spectral image information
IEEE Transactions on Robotics
Accelerating neuromorphic vision algorithms for recognition
Proceedings of the 49th Annual Design Automation Conference
Emulating Mammalian Vision on Reconfigurable Hardware
FCCM '12 Proceedings of the 2012 IEEE 20th International Symposium on Field-Programmable Custom Computing Machines
A framework for accelerating neuromorphic-vision algorithms on FPGAs
ICCAD '11 Proceedings of the 2011 IEEE/ACM International Conference on Computer-Aided Design
An FPGA-based accelerator for cortical object classification
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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Video and image content has begun to play a growing role in many applications, ranging from video games to autonomous self-driving vehicles. In this paper, we present accelerators for gist-based scene recognition, saliency-based attention, and HMAX-based object recognition that have multiple uses and are based on the current understanding of the vision systems found in the visual cortex of the mammalian brain. By integrating them into a two-level hierarchical system, we improve recognition accuracy and reduce computational time. Results of our accelerator prototype on a multi-FPGA system show real-time performance and high recognition accuracy with large speedups over existing CPU, GPU and FPGA implementations.