Fast Algorithms for Low-Level Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visit: an efficient computational model of human visual attention
Visit: an efficient computational model of human visual attention
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
Computing Visual Attention from Scene Depth
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Modeling Selective Attention Using a Neuromorphic Analog VLSI Device
Neural Computation
Assessing the contribution of color in visual attention
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Optimal Cue Combination for Saliency Computation: A Comparison with Human Vision
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Assessing the contribution of color in visual attention
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Parallel implementation of a spatio-temporal visual saliency model
Journal of Real-Time Image Processing
Linear vs. nonlinear feature combination for saliency computation: a comparison with human vision
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Model performance for visual attention in real 3d color scenes
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
A visual attention-based approach for automatic landmark selection and recognition
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
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Visual attention is the ability to rapidly detect the visually salient parts of a given scene on which higher level vision tasks, such as object recognition, can focus. Found in biological vision, this mechanism represents a fundamental tool for computer vision. This paper reports the first real-time implementation of the complete visual attention mechanism on a compact and low-power architecture. Specifically, the saliency-based model of visual attention was implemented on a highly parallel single instruction, multiple data architecture called ProtoEye. Conceived for general purpose low-level image processing, ProtoEye consists of a 2D array of mixed analog-digital processing elements. To reach real-time, the operations required for visual attention computation were optimally distributed on the analog and digital parts. The currently available prototype runs at a frequency of 14 images/s and operates on 64 × 64 gray level images. Extensive testing and run-time analysis of the system stress the strengths of the architecture.