A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Spatio-temporal adaptation in the unsupervised development of networked visual neurons
IEEE Transactions on Neural Networks
A new model of visual attention selection based on amplitude modulation Fourier transform
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
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Previous research has shown that Fast-Fourier-Transform based method was an effective approach for studying computational attention model In this paper, a quantitative analysis was carried out to explore the intrinsic mechanism of FFT-based approach Based on it, a unified framework was proposed to summarize all existing FFT-based attention models A new saliency detective model called Frequency Spectrum Modification (FSM) was also derived from this framework Multiple feature channels and lateral competition were applied in this model for simulating human visual system The comparison between FSM and other FFT-based models was implemented by comparing their responses with the real human eye's fixation traces The result leads to the conclusion that FSM is more effective in saliency detection.