92 research outputs found
Scale Invariant Interest Points with Shearlets
Shearlets are a relatively new directional multi-scale framework for signal
analysis, which have been shown effective to enhance signal discontinuities
such as edges and corners at multiple scales. In this work we address the
problem of detecting and describing blob-like features in the shearlets
framework. We derive a measure which is very effective for blob detection and
closely related to the Laplacian of Gaussian. We demonstrate the measure
satisfies the perfect scale invariance property in the continuous case. In the
discrete setting, we derive algorithms for blob detection and keypoint
description. Finally, we provide qualitative justifications of our findings as
well as a quantitative evaluation on benchmark data. We also report an
experimental evidence that our method is very suitable to deal with compressed
and noisy images, thanks to the sparsity property of shearlets
Space-Time Signal Analysis and the 3D Shearlet Transform
In this work, we address the problem of analyzing video sequences by representing meaningful local space\ue2\u80\u93time neighborhoods. We propose a mathematical model to describe relevant points as local singularities of a 3D signal, and we show that these local patterns can be nicely highlighted by the 3D shearlet transform, which is at the root of our work. Based on this mathematical framework, we derive an algorithm to represent space\ue2\u80\u93time points which is very effective in analyzing video sequences. In particular, we show how points of the same nature have a very similar representation, allowing us to compute different space\ue2\u80\u93time primitives for a video sequence in an unsupervised way
Local Spatio-Temporal Representation Using the 3D Shearlet Transform (STSIP)
In this work we address the problem of analyzing video sequences and of representing meaningful space-time points of interest by using the 3D shearlet transform. We introduce a local representation based on shearlet coe cients of the video, regarded as 2D+T signal. This representation turns out to be informative to understand the local spatio-temporal characteristics, which can be easily detected by an unsupervised clustering algorithm
Shearlets and Optimally Sparse Approximations
Multivariate functions are typically governed by anisotropic features such as
edges in images or shock fronts in solutions of transport-dominated equations.
One major goal both for the purpose of compression as well as for an efficient
analysis is the provision of optimally sparse approximations of such functions.
Recently, cartoon-like images were introduced in 2D and 3D as a suitable model
class, and approximation properties were measured by considering the decay rate
of the error of the best -term approximation. Shearlet systems are to
date the only representation system, which provide optimally sparse
approximations of this model class in 2D as well as 3D. Even more, in contrast
to all other directional representation systems, a theory for compactly
supported shearlet frames was derived which moreover also satisfy this
optimality benchmark. This chapter shall serve as an introduction to and a
survey about sparse approximations of cartoon-like images by band-limited and
also compactly supported shearlet frames as well as a reference for the
state-of-the-art of this research field.Comment: in "Shearlets: Multiscale Analysis for Multivariate Data",
Birkh\"auser-Springe
Spatio-Temporal Video Analysis and the 3D Shearlet Transform
Abstract
The automatic analysis of the content of a video sequence has captured the
attention of the computer vision community for a very long time. Indeed,
video understanding, which needs to incorporate both semantic and dynamic
cues, may be trivial for humans, but it turned out to be a very complex
task for a machine. Over the years the signal processing, computer vision,
and machine learning communities contributed with algorithms that are
today effective building blocks of more and more complex systems. In
the meanwhile, theoretical analysis has gained a better understanding of
this multifaceted type of data. Indeed, video sequences are not only high
dimensional data, but they are also very peculiar, as they include spatial as
well as temporal information which should be treated differently, but are
both important to the overall process. The work of this thesis builds a new
bridge between signal processing theory, and computer vision applications. It
considers a novel approach to multi resolution signal processing, the so-called
Shearlet Transform, as a reference framework for representing meaningful
space-time local information in a video signal. The Shearlet Transform
has been shown effective in analyzing multi-dimensional signals, ranging
from images to x-ray tomographic data. As a tool for signal denoising, has
also been applied to video data. However, to the best of our knowledge,
the Shearlet Transform has never been employed to design video analysis
algorithms. In this thesis, our broad objective is to explore the capabilities of
the Shearlet Transform to extract information from 2D+T-dimensional data.
We exploit the properties of the Shearlet decomposition to redesign a variety
of classical video processing techniques (including space-time interest point
detection and normal flow estimation) and to develop novel methods to better
understand the local behavior of video sequences. We provide experimental
evidence on the potential of our approach on synthetic as well as real data
drawn from publicly available benchmark datasets. The results we obtain
show the potential of our approach and encourages further investigations in
the near future
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