1,081 research outputs found
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
Bioplausible multiscale filtering in retino-cortical processing as a mechanism in perceptual grouping
Why does our visual system fail to reconstruct reality, when we look at
certain patterns? Where do Geometrical illusions start to emerge in the visual
pathway? How far should we take computational models of vision with the same
visual ability to detect illusions as we do? This study addresses these
questions, by focusing on a specific underlying neural mechanism involved in
our visual experiences that affects our final perception. Among many types of
visual illusion, Geometrical and, in particular, Tilt Illusions are rather
important, being characterized by misperception of geometric patterns involving
lines and tiles in combination with contrasting orientation, size or position.
Over the last decade, many new neurophysiological experiments have led to new
insights as to how, when and where retinal processing takes place, and the
encoding nature of the retinal representation that is sent to the cortex for
further processing. Based on these neurobiological discoveries, we provide
computer simulation evidence from modelling retinal ganglion cells responses to
some complex Tilt Illusions, suggesting that the emergence of tilt in these
illusions is partially related to the interaction of multiscale visual
processing performed in the retina. The output of our low-level filtering model
is presented for several types of Tilt Illusion, predicting that the final tilt
percept arises from multiple-scale processing of the Differences of Gaussians
and the perceptual interaction of foreground and background elements. Our
results suggest that this model has a high potential in revealing the
underlying mechanism connecting low-level filtering approaches to mid- and
high-level explanations such as Anchoring theory and Perceptual grouping.Comment: 23 pages, 8 figures, Brain Informatics journal: Full text access:
https://link.springer.com/article/10.1007/s40708-017-0072-
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