Orthogonal and biorthogonal wavelets became very popular image processing tools but
exhibit major drawbacks, namely a poor resolution in orientation and the lack of translation …

Today, multiresolution (MR) transforms are a widespread tool for image fusion. They
decorrelate the image into several scaled and oriented sub-bands, which are usually …

Algorithms that encode images using a sparse set of basis functions have previously been
shown to explain aspects of the physiology of a primary visual cortex (V1), and have been …

We present an analysis of the Locally Competitive Algorithm (LCA), which is a Hopfield-style
neural network that efficiently solves sparse approximation problems (eg, approximating a …

Neurons in the input layer of primary visual cortex in primates develop edge-like receptive
fields. One approach to understanding the emergence of this response is to state that neural …

Physiological and psychophysical studies have demonstrated the importance of colinearity
in visual processing. Motivated by these empirical findings we present a novel …

Making a judgment about the semantic category of a visual scene, such as whether it
contains an animal, is typically assumed to involve high-level associative brain areas …

The formation of structure in the visual system, that is, of the connections between cells
within neural populations, is by and large an unsupervised learning process. In the primary …

Representing signals as linear combinations of basis vectors sparsely selected from an
overcomplete dictionary has proven to be advantageous for many applications in pattern …

The representation of images in the brain is known to be sparse, that is, as neural activity is
recorded in a visual area—for instance, the primary visual cortex of primates—only a few …