Contextual Priming for Object Detection
International Journal of Computer Vision
Hierarchical Top Down Enhancement of Robust PCA
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Automatic browsing of large pictures on mobile devices
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Effective browsing of web image search results
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Vision pyramids that do not grow too high
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
Visual attention based image browsing on mobile devices
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Foveal versus parafoveal scanpaths of visual imagery in virtual hemianopic subjects
Computers in Biology and Medicine
Approximative graph pyramid solution of the E-TSP
Image and Vision Computing
A multicue Bayesian state estimator for gaze prediction in open signed video
IEEE Transactions on Multimedia
A probabilistic model of overt visual attention for cognitive robots
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information Sciences: an International Journal
Key frame extraction based on visual attention model
Journal of Visual Communication and Image Representation
Combining conspicuity maps for hROIs prediction
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
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Eye movements (EMs) are an important aspect of human visual behavior. The temporal and space-variant nature of sampling a visual scene requires frequent attentional gaze shifts (saccades) to fixate onto different parts of an image. Fixations are often directed toward the most informative regions in the visual scene. We introduce a model and its simulation that can select such regions based on prior knowledge of similar scenes. Having representations of scenes as a probabilistic combination of regions with certain properties, it is possible to assess the likely contribution of each region in the successive recognition process. Using Bayesian conditional probabilities for each region given the scene category, the model can then predict the informative value of that region and initiate a spatial information-gathering algorithm analogous to an EM saccade to a new fixation