23.05.2014

David Janssen: Another new abstract accepted as a poster at EMPG from 30.07.-01.08.2014

Title: "A better image decomposition for computational vision models" by David Janssen and Felix Wichmann

Abstract:

A better image decomposition for computational vision models

Image-driven models of vision attempt to express the perceptibility of images by relating the actual image to measures of human behavior. Typically, such models combine a subband decomposition (e.g. Steerable Pyramid, Gabor wavelet banks) with subsequent non-linearities (e.g. point-nonlinearities, Minkowski-norm pooling, divisive contrast normalization). The combination of these two, however, cause aliasing-like artifacts to occur in model behavior in the domains of spatial frequency and orientation.
This aliasing behavior can lead to some very strange predictions like the perceptibility of certain image aspects oscillating with viewing distance. We propose a number of methods of spacing filters more homogenously in frequency and orientation. Our approach solves this problem, while additionally more closely resembling what we know about frequency sensitivities in V1.
One such method, which we explore in great detail, is based on methods of spatial statistics, namely a Gibbs process. A Gibbs process represents distributions of points in a space through a pair-potential function, a function that expresses the likelihood of certain interpoint distances occuring. We represent spatial filters as points in 4D space (x, y, frequency, orientation) and generate our sampling grids through statistical sampling from this Gibbs process.
These new methods of generating image decompositions, although much slower and less efficient numerically, offer robust and statistically rigorous methods of sampling frequency and orientation content from images. Additionally, they do not suffer from the frequency- and orientation aliasing problem that occurs in more traditional image decompositions.