Registration of Translated and Rotated Images Using Finite Fourier Transforms
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
Modeling surround suppression in V1 neurons with a statistically-derived normalization model
Proceedings of the 1998 conference on Advances in neural information processing systems II
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
2006 Special Issue: Pre-attentive visual selection
Neural Networks
Hypercomplex Fourier Transforms of Color Images
IEEE Transactions on Image Processing
Quaternion-Based spectral saliency detection for eye fixation prediction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Stochastic bottom-up fixation prediction and saccade generation
Image and Vision Computing
Attention selection using global topological properties based on pulse coupled neural network
Computer Vision and Image Understanding
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We provide a biological justification for the success of spectral domain models of visual attention and propose a refined spectral domain based spatiotemporal saliency map model including a more biologically plausible method for motion saliency generation. We base our approach on the idea of spectral whitening (SW), and show that this whitening process is an estimation of divisive normalization, a model of lateral surround inhibition. Experimental results reveal that SW is a better performer at predicating eye fixation locations than other state-of-the-art spatial domain models for color images, achieving a 92% consistency with human behavior in urban environments. In addition, the model is simple and fast, capable of generating saliency maps in real-time.