A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Theoretic Measure for Visual Target Distinctness
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Identification of Perceptually Important Regions in an Image
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Augmented metacognition addressing dynamic allocation of tasks requiring visual attention
FAC'07 Proceedings of the 3rd international conference on Foundations of augmented cognition
From computational attention to image fusion
Pattern Recognition Letters
Comparative visibility analysis of advertisement images
Image Communication
Sustainable image transmission
Journal of Visual Communication and Image Representation
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Here we describe, in terms of a decision problem, any situation in which a computational system will be forced to allocate attention at any time to one spatial location to improve the reconstruction fidelity on a neighborhood of the chosen point. The result is a rational model of computational attention in which a multi-bitrate attention map will provide us with the attention score for each spatial location at high and low quality versions of the image reconstruction. At any time a rational system should choose, even though without any outside knowledge, among alternative spatial locations in such a way as to avoid certain forms of behavioral inconsistency. We compare the performance between a rational approach of computational attention and various models for predicting visual target distinctness, using scenes that represent military vehicles in complex rural backgrounds.