High-Order Statistics for Point Prediction in Natural Images

2011 ◽  
Author(s):  
Wilson S. Geisler ◽  
Jeffrey S. Perry
Keyword(s):  
Order Statistics ◽  
High Order ◽  
Natural Images ◽  
High Order Statistics

scholarly journals Two-Dimensional Hermite Filters Simplify the Description of High-Order Statistics of Natural Images

2016 ◽  
Author(s):  
Qin Hu ◽  
Jonathan Victor
Keyword(s):  
Order Statistics ◽  
Dimensional Subspace ◽  
High Order ◽  
Natural Images ◽  
Natural Image ◽  
Two Dimensional ◽  
Natural Image Statistics ◽  
Image Statistics ◽  
High Order Statistics ◽  
Orthogonal Set

AbstractNatural image statistics play a crucial role in shaping biological visual systems, understanding their function and design principles, and designing effective computer-vision algorithms. High-order statistics are critical for conveying local features, but they are challenging to study – largely because their number and variety is large. Here, via the use of two-dimensional Hermite (TDH) functions, we identify a covert symmetry in high-order statistics of natural images that simplifies this task. This emerges from the structure of TDH functions, which are an orthogonal set of functions that are organized into a hierarchy of ranks. Specifically, we find that the shape (skewness and kurtosis) of the distribution of filter coefficients depends only on the projection of the function onto a 1-dimensional subspace specific to each rank. The characterization of natural image statistics provided by TDH filter coefficients reflects both their phase and amplitude structure, and we suggest an intuitive interpretation for the special subspace within each rank.

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