A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data- and Model-Driven Gaze Control for an Active-Vision System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biologically Inspired Saliency Map Model for Bottom-up Visual Attention
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Object-based visual attention for computer vision
Artificial Intelligence
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating context-free and context-dependent attentional mechanisms for gestural object reference
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Fast and Robust Generation of Feature Maps for Region-Based Visual Attention
IEEE Transactions on Image Processing
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A region-based approach towards modelling of bottom-up visual attention is proposed with an objective to accelerate the internal processes of attention and make its output usable by the high-level vision procedures to facilitate intelligent decision making during pattern analysis and vision-based learning. A memory-based inhibition of return is introduced in order to handle the dynamic scenarios of mobile vision systems. Performance of the proposed model is evaluated on different categories of visual input and compared with human attention response and other existing models of attention. Results show success of the proposed model and its advantages over existing techniques in certain aspects.