Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
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
Realistic Simulation Tool for Early Visual Processing Including Space, Time and Colour Data
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applying computational tools to predict gaze direction in interactive visual environments
ACM Transactions on Applied Perception (TAP)
Model of Frequency Analysis in the Visual Cortex and the Shape from Texture Problem
International Journal of Computer Vision
A generic framework of user attention model and its application in video summarization
IEEE Transactions on Multimedia
Relevance of Interest Points for Eye Position Prediction on Videos
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Probabilistic Multi-Task Learning for Visual Saliency Estimation in Video
International Journal of Computer Vision
Parallel implementation of a spatio-temporal visual saliency model
Journal of Real-Time Image Processing
A dynamic saliency attention model based on local complexity
Digital Signal Processing
Video saliency detection with robust temporal alignment and local-global spatial contrast
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Journal of Visual Communication and Image Representation
Stochastic bottom-up fixation prediction and saccade generation
Image and Vision Computing
Efficient implementation of data flow graphs on multi-gpu clusters
Journal of Real-Time Image Processing
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This paper presents a spatio-temporal saliency model that predicts eye movement during video free viewing. This model is inspired by the biology of the first steps of the human visual system. The model extracts two signals from video stream corresponding to the two main outputs of the retina: parvocellular and magnocellular. Then, both signals are split into elementary feature maps by cortical-like filters. These feature maps are used to form two saliency maps: a static and a dynamic one. These maps are then fused into a spatio-temporal saliency map. The model is evaluated by comparing the salient areas of each frame predicted by the spatio-temporal saliency map to the eye positions of different subjects during a free video viewing experiment with a large database (17000 frames). In parallel, the static and the dynamic pathways are analyzed to understand what is more or less salient and for what type of videos our model is a good or a poor predictor of eye movement.