988 research outputs found
Principled Design and Implementation of Steerable Detectors
We provide a complete pipeline for the detection of patterns of interest in
an image. In our approach, the patterns are assumed to be adequately modeled by
a known template, and are located at unknown position and orientation. We
propose a continuous-domain additive image model, where the analyzed image is
the sum of the template and an isotropic background signal with self-similar
isotropic power-spectrum. The method is able to learn an optimal steerable
filter fulfilling the SNR criterion based on one single template and background
pair, that therefore strongly responds to the template, while optimally
decoupling from the background model. The proposed filter then allows for a
fast detection process, with the unknown orientation estimation through the use
of steerability properties. In practice, the implementation requires to
discretize the continuous-domain formulation on polar grids, which is performed
using radial B-splines. We demonstrate the practical usefulness of our method
on a variety of template approximation and pattern detection experiments
Construction of Hilbert Transform Pairs of Wavelet Bases and Gabor-like Transforms
We propose a novel method for constructing Hilbert transform (HT) pairs of
wavelet bases based on a fundamental approximation-theoretic characterization
of scaling functions--the B-spline factorization theorem. In particular,
starting from well-localized scaling functions, we construct HT pairs of
biorthogonal wavelet bases of L^2(R) by relating the corresponding wavelet
filters via a discrete form of the continuous HT filter. As a concrete
application of this methodology, we identify HT pairs of spline wavelets of a
specific flavor, which are then combined to realize a family of complex
wavelets that resemble the optimally-localized Gabor function for sufficiently
large orders.
Analytic wavelets, derived from the complexification of HT wavelet pairs,
exhibit a one-sided spectrum. Based on the tensor-product of such analytic
wavelets, and, in effect, by appropriately combining four separable
biorthogonal wavelet bases of L^2(R^2), we then discuss a methodology for
constructing 2D directional-selective complex wavelets. In particular,
analogous to the HT correspondence between the components of the 1D
counterpart, we relate the real and imaginary components of these complex
wavelets using a multi-dimensional extension of the HT--the directional HT.
Next, we construct a family of complex spline wavelets that resemble the
directional Gabor functions proposed by Daugman. Finally, we present an
efficient FFT-based filterbank algorithm for implementing the associated
complex wavelet transform.Comment: 36 pages, 8 figure
On The Continuous Steering of the Scale of Tight Wavelet Frames
In analogy with steerable wavelets, we present a general construction of
adaptable tight wavelet frames, with an emphasis on scaling operations. In
particular, the derived wavelets can be "dilated" by a procedure comparable to
the operation of steering steerable wavelets. The fundamental aspects of the
construction are the same: an admissible collection of Fourier multipliers is
used to extend a tight wavelet frame, and the "scale" of the wavelets is
adapted by scaling the multipliers. As an application, the proposed wavelets
can be used to improve the frequency localization. Importantly, the localized
frequency bands specified by this construction can be scaled efficiently using
matrix multiplication
Polarized wavelets and curvelets on the sphere
The statistics of the temperature anisotropies in the primordial cosmic
microwave background radiation field provide a wealth of information for
cosmology and for estimating cosmological parameters. An even more acute
inference should stem from the study of maps of the polarization state of the
CMB radiation. Measuring the extremely weak CMB polarization signal requires
very sensitive instruments. The full-sky maps of both temperature and
polarization anisotropies of the CMB to be delivered by the upcoming Planck
Surveyor satellite experiment are hence being awaited with excitement.
Multiscale methods, such as isotropic wavelets, steerable wavelets, or
curvelets, have been proposed in the past to analyze the CMB temperature map.
In this paper, we contribute to enlarging the set of available transforms for
polarized data on the sphere. We describe a set of new multiscale
decompositions for polarized data on the sphere, including decimated and
undecimated Q-U or E-B wavelet transforms and Q-U or E-B curvelets. The
proposed transforms are invertible and so allow for applications in data
restoration and denoising.Comment: Accepted. Full paper will figures available at
http://jstarck.free.fr/aa08_pola.pd
Left-invariant evolutions of wavelet transforms on the Similitude Group
Enhancement of multiple-scale elongated structures in noisy image data is
relevant for many biomedical applications but commonly used PDE-based
enhancement techniques often fail at crossings in an image. To get an overview
of how an image is composed of local multiple-scale elongated structures we
construct a multiple scale orientation score, which is a continuous wavelet
transform on the similitude group, SIM(2). Our unitary transform maps the space
of images onto a reproducing kernel space defined on SIM(2), allowing us to
robustly relate Euclidean (and scaling) invariant operators on images to
left-invariant operators on the corresponding continuous wavelet transform.
Rather than often used wavelet (soft-)thresholding techniques, we employ the
group structure in the wavelet domain to arrive at left-invariant evolutions
and flows (diffusion), for contextual crossing preserving enhancement of
multiple scale elongated structures in noisy images. We present experiments
that display benefits of our work compared to recent PDE techniques acting
directly on the images and to our previous work on left-invariant diffusions on
orientation scores defined on Euclidean motion group.Comment: 40 page
Recommended from our members
De-noising SPECT/PET Images Using Cross-Scale Regularization
De-noising of SPECT and PET images is a challenging task due to the inherent low signal-to-noise ratio of acquired data. Wavelet based multi-scale denoising methods typically apply thresholding operators on sub-band coefficients to eliminate noise components in spatial-frequency space prior to reconstruction. In the case of high noise levels, detailed scales of sub-band images are usually dominated by noise which cannot be easily removed using traditional thresholding schemes. To address this issue, a cross-scale regularization scheme is introduced, which takes into account cross-scale coherence of structured signals. Preliminary results show promising performance in denoising clinical SPECT and PET images for liver and brain studies. Wavelet thresholding was also compared to denoising with a brushlet expansion. The proposed regularization scheme eliminates the need for threshold parameter settings, making the denoising process less tedious and suitable for clinical practice
FFD:Fast Feature Detector
Scale-invariance, good localization and robustness to noise and distortions
are the main properties that a local feature detector should possess. Most
existing local feature detectors find excessive unstable feature points that
increase the number of keypoints to be matched and the computational time of
the matching step. In this paper, we show that robust and accurate keypoints
exist in the specific scale-space domain. To this end, we first formulate the
superimposition problem into a mathematical model and then derive a closed-form
solution for multiscale analysis. The model is formulated via
difference-of-Gaussian (DoG) kernels in the continuous scale-space domain, and
it is proved that setting the scale-space pyramid's blurring ratio and
smoothness to 2 and 0.627, respectively, facilitates the detection of reliable
keypoints. For the applicability of the proposed model to discrete images, we
discretize it using the undecimated wavelet transform and the cubic spline
function. Theoretically, the complexity of our method is less than 5\% of that
of the popular baseline Scale Invariant Feature Transform (SIFT). Extensive
experimental results show the superiority of the proposed feature detector over
the existing representative hand-crafted and learning-based techniques in
accuracy and computational time. The code and supplementary materials can be
found at~{\url{https://github.com/mogvision/FFD}}
An efficient computational scheme for the two-dimensional overcomplete wavelet transform
2002-2003 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
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