Redundancy reduction as a strategy for unsupervised learning
Neural Computation
Distributed representation and analysis of visual motion
Distributed representation and analysis of visual motion
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatio-Temporal Image Processing: Theory and Scientific Applications
Spatio-Temporal Image Processing: Theory and Scientific Applications
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Active Contours: The Application of Techniques from Graphics,Vision,Control Theory and Statistics to Visual Tracking of Shapes in Motion
Contour Continuity in Region Based Image Segmentation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Object-based visual attention for computer vision
Artificial Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Modeling dynamic perceptual attention in complex virtual environments
Lecture Notes in Computer Science
Object-based Visual Attention: a Model for a Behaving Robot
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Evaluation of Visual Attention Models for Robots
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Image-based robot navigation from an image memory
Robotics and Autonomous Systems
A mean field annealing approach to robust corner detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Coarse-to-fine vision-based localization by indexing scale-Invariant features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Bottom-Up Gaze Shifts and Fixations Learning by Imitation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Active vision for sociable robots
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A novel biologically inspired attention mechanism for a social robot
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
Global salient information maximization for saliency detection
Image Communication
Tag-Saliency: Combining bottom-up and top-down information for saliency detection
Computer Vision and Image Understanding
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By extending integrated competition hypothesis, this paper presents an object-based visual attention model, which selects one object of interest using low-dimensional features, resulting that visual perception starts from a fast attentional selection procedure. The proposed attention model involves seven modules: learning of object representations stored in a long-term memory (LTM), preattentive processing, top-down biasing, bottom-up competition, mediation between top-down and bottom-up ways, generation of saliency maps, and perceptual completion processing. It works in two phases: learning phase and attending phase. In the learning phase, the corresponding object representation is trained statistically when one object is attended. A dual-coding object representation consisting of local and global codings is proposed. Intensity, color, and orientation features are used to build the local coding, and a contour feature is employed to constitute the global coding. In the attending phase, the model preattentively segments the visual field into discrete proto-objects using Gestalt rules at first. If a task-specific object is given, the model recalls the corresponding representation from LTM and deduces the task-relevant feature(s) to evaluate top-down biases. The mediation between automatic bottom-up competition and conscious top-down biasing is then performed to yield a location-based saliency map. By combination of location-based saliency within each proto-object, the proto-object-based saliency is evaluated. The most salient proto-object is selected for attention, and it is finally put into the perceptual completion processing module to yield a complete object region. This model has been applied into distinct tasks of robots: detection of task-specific stationary and moving objects. Experimental results under different conditions are shown to validate this model.