Digital image processing
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Target Detection Using Multispectral Imaging
AIPR '02 Proceedings of the 31st Applied Image Pattern Recognition Workshop on From Color to Hyperspectral: Advancements in Spectral Imagery Exploitation
Clifford Fourier Transform on Vector Fields
IEEE Transactions on Visualization and Computer Graphics
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Pulse discrete cosine transform for saliency-based visual attention
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
Biological plausibility of spectral domain approach for spatiotemporal visual saliency
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
IEEE Transactions on Image Processing
Comparative performance analysis of adaptive multispectraldetectors
IEEE Transactions on Signal Processing
Hypercomplex signals-a novel extension of the analytic signal tothe multidimensional case
IEEE Transactions on Signal Processing
Fast Complexified Quaternion Fourier Transform
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
Hypercomplex Fourier Transforms of Color Images
IEEE Transactions on Image Processing
Visual saliency detection with center shift
Neurocomputing
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This paper proposes an approach for visual attention based on biquaternion, and investigates its application for ship detection in multispectral imagery. The proposed approach describes high-dimensional data in the form of biquaternion and utilizes the phase spectrum of biquaternion Fourier transform to generate a required saliency map that can be used for salient target detection. In our method, the multidimensional data is processed as a whole, and the features contained in each spectral band can be extracted effectively. Compared with traditional visual attention approaches, our method has very low computational complexity. Experimental results on simulated and real multispectral remote sensing data have shown that the proposed method has excellent performance in ship detection. Furthermore, our method is robust against white noise and almost meets real-time requirements, which has great potentials in engineering applications.