A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust motion estimation using spatial Gabor-like filters
Signal Processing
Real-time visual attention on a massively parallel SIMD architecture
Real-Time Imaging
Attention guided MPEG compression for computer animations
SCCG '03 Proceedings of the 19th spring conference on Computer graphics
Brook for GPUs: stream computing on graphics hardware
ACM SIGGRAPH 2004 Papers
OpenVIDIA: parallel GPU computer vision
Proceedings of the 13th annual ACM international conference on Multimedia
A GPU based saliency map for high-fidelity selective rendering
AFRIGRAPH '06 Proceedings of the 4th international conference on Computer graphics, virtual reality, visualisation and interaction in Africa
Real-time tracking of visually attended objects in interactive virtual environments
Proceedings of the 2007 ACM symposium on Virtual reality software and technology
GpuCV: an opensource GPU-accelerated framework forimage processing and computer vision
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Modelling Spatio-Temporal Saliency to Predict Gaze Direction for Short Videos
International Journal of Computer Vision
EG PGV'06 Proceedings of the 6th Eurographics conference on Parallel Graphics and Visualization
Efficient implementation of data flow graphs on multi-gpu clusters
Journal of Real-Time Image Processing
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The human vision has been studied deeply in the past years, and several different models have been proposed to simulate it on computer. Some of these models concerns visual saliency which is potentially very interesting in a lot of applications like robotics, image analysis, compression, video indexing. Unfortunately they are compute intensive with tight real-time requirements. Among all the existing models, we have chosen a spatio-temporal one combining static and dynamic information. We propose in this paper a very efficient implementation of this model with multi-GPU reaching real-time. We present the algorithms of the model as well as several parallel optimizations on GPU with higher precision and execution time results. The real-time execution of this multi-path model on multi-GPU makes it a powerful tool to facilitate many vision related applications.