论文信息 - High-Order Statistics for Point Prediction in Natural Images

High-Order Statistics for Point Prediction in Natural Images

Results are presented for a simple conditional-moments method that directly measures high-order statistics of natural images. In four estimation tasks significant increases in performance are obtained in comparison to traditional methods.

Wilson S. Geisler | Jeffrey S. Perry

[1] Eero P. Simoncelli,et al. Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

[2] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.

[3] Nick Fogt,et al. Ocular tracking of transiently occluded targets , 2010 .

[4] W. Geisler. Visual perception and the statistical properties of natural scenes. , 2008, Annual review of psychology.

[5] Heidi Hofer,et al. Trichromatic reconstruction from the interleaved cone mosaic: Bayesian model and the color appearance of small spots. , 2008, Journal of vision.

[6] D Kersten,et al. Predictability and redundancy of natural images. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[7] David Williams,et al. Different sensations from cones with the same photopigment. , 2005, Journal of vision.

[8] Kim Steenstrup Pedersen,et al. The Nonlinear Statistics of High-Contrast Patches in Natural Images , 2003, International Journal of Computer Vision.