Spatio-temporal Saliency detection using phase spectrum of quaternion fourier transform

Salient areas in natural scenes are generally regarded as the candidates of attention focus in human eyes, which is the key stage in object detection. In computer vision, many models have been proposed to simulate the behavior of eyes such as SaliencyToolBox (STB), neuromorphic vision toolkit (NVT) and etc., but they demand high computational cost and their remarkable results mostly rely on the choice of parameters. Recently a simple and fast approach based on Fourier transform called spectral residual (SR) was proposed, which used SR of the amplitude spectrum to obtain the saliency map. The results are good, but the reason is questionable.

[1]  Stephen J. Sangwine,et al.  Hypercomplex Fourier Transforms of Color Images , 2001, IEEE Transactions on Image Processing.

[2]  Arni Kristjansson,et al.  Efficient visual search without top-down or bottom-up guidance , 2005, Perception & psychophysics.

[3]  Nanning Zheng,et al.  Learning to Detect a Salient Object , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[5]  S. Engel,et al.  Colour tuning in human visual cortex measured with functional magnetic resonance imaging , 1997, Nature.

[6]  Nuno Vasconcelos,et al.  Integrated learning of saliency, complex features, and object detectors from cluttered scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Christof Koch,et al.  Attentional Selection for Object Recognition - A Gentle Way , 2002, Biologically Motivated Computer Vision.

[8]  J. Wolfe,et al.  Guided Search 2.0 A revised model of visual search , 1994, Psychonomic bulletin & review.

[9]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[11]  Christof Koch,et al.  Modeling attention to salient proto-objects , 2006, Neural Networks.

[12]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[13]  Ronald A. Rensink.  Seeing, sensing, and scrutinizing , 2000, Vision Research.