14,851 research outputs found
Deformable kernels for early vision
Early vision algorithms often have a first stage of linear-filtering that `extracts' from the image information at multiple scales of resolution and multiple orientations. A common difficulty in the design and implementation of such schemes is that one feels compelled to discretize coarsely the space of scales and orientations in order to reduce computation and storage costs. A technique is presented that allows: 1) computing the best approximation of a given family using linear combinations of a small number of `basis' functions; and 2) describing all finite-dimensional families, i.e., the families of filters for which a finite dimensional representation is possible with no error. The technique is based on singular value decomposition and may be applied to generating filters in arbitrary dimensions and subject to arbitrary deformations. The relevant functional analysis results are reviewed and precise conditions for the decomposition to be feasible are stated. Experimental results are presented that demonstrate the applicability of the technique to generating multiorientation multi-scale 2D edge-detection kernels. The implementation issues are also discussed
Statistical M-Estimation and Consistency in Large Deformable Models for Image Warping
The problem of defining appropriate distances between shapes or images and modeling the variability of natural images by group transformations is at the heart of modern image analysis. A current trend is the study of probabilistic and statistical aspects of deformation models, and the development of consistent statistical procedure for the estimation of template images. In this paper, we consider a set of images randomly warped from a mean template which has to be recovered. For this, we define an appropriate statistical parametric model to generate random diffeomorphic deformations in two-dimensions. Then, we focus on the problem of estimating the mean pattern when the images are observed with noise. This problem is challenging both from a theoretical and a practical point of view. M-estimation theory enables us to build an estimator defined as a minimizer of a well-tailored empirical criterion. We prove the convergence of this estimator and propose a gradient descent algorithm to compute this M-estimator in practice. Simulations of template extraction and an application to image clustering and classification are also provided
Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model
Estimating probabilistic deformable template models is a new approach in the
fields of computer vision and probabilistic atlases in computational anatomy. A
first coherent statistical framework modelling the variability as a hidden
random variable has been given by Allassonni\`ere, Amit and Trouv\'e in [1] in
simple and mixture of deformable template models. A consistent stochastic
algorithm has been introduced in [2] to face the problem encountered in [1] for
the convergence of the estimation algorithm for the one component model in the
presence of noise. We propose here to go on in this direction of using some
"SAEM-like" algorithm to approximate the MAP estimator in the general Bayesian
setting of mixture of deformable template model. We also prove the convergence
of this algorithm toward a critical point of the penalised likelihood of the
observations and illustrate this with handwritten digit images
Precise localization for aerial inspection using augmented reality markers
The final publication is available at link.springer.comThis chapter is devoted to explaining a method for precise localization using augmented reality markers. This method can achieve precision of less of 5 mm in position at a distance of 0.7 m, using a visual mark of 17 mm Ă 17 mm, and it can be used by controller when the aerial robot is doing a manipulation task. The localization method is based on optimizing the alignment of deformable contours from textureless images working from the raw vertexes of the observed contour. The algorithm optimizes the alignment of the XOR area computed by means of computer graphics clipping techniques. The method can run at 25 frames per second.Peer ReviewedPostprint (author's final draft
High-contrast Imaging from Space: Speckle Nulling in a Low Aberration Regime
High-contrast imaging from space must overcome two major noise sources to
successfully detect a terrestrial planet angularly close to its parent star:
photon noise from diffracted star light, and speckle noise from star light
scattered by instrumentally-generated wavefront perturbation. Coronagraphs
tackle only the photon noise contribution by reducing diffracted star light at
the location of a planet. Speckle noise should be addressed with
adaptative-optics systems. Following the tracks of Malbet, Yu and Shao (1995),
we develop in this paper two analytical methods for wavefront sensing and
control that aims at creating dark holes, i.e. areas of the image plane cleared
out of speckles, assuming an ideal coronagraph and small aberrations. The first
method, speckle field nulling, is a fast FFT-based algorithm that requires the
deformable-mirror influence functions to have identical shapes. The second
method, speckle energy minimization, is more general and provides the optimal
deformable mirror shape via matrix inversion. With a NxN deformable mirror, the
size of matrix to be inverted is either N^2xN^2 in the general case, or only
NxN if influence functions can be written as the tensor product of two
one-dimensional functions. Moreover, speckle energy minimization makes it
possible to trade off some of the dark hole area against an improved contrast.
For both methods, complex wavefront aberrations (amplitude and phase) are
measured using just three images taken with the science camera (no dedicated
wavefront sensing channel is used), therefore there are no non-common path
errors. We assess the theoretical performance of both methods with numerical
simulations, and find that these speckle nulling techniques should be able to
improve the contrast by several orders of magnitude.Comment: 31 pages, 8 figures, 1 table. Accepted for publication in ApJ (should
appear in February 2006
Indirect Image Registration with Large Diffeomorphic Deformations
The paper adapts the large deformation diffeomorphic metric mapping framework
for image registration to the indirect setting where a template is registered
against a target that is given through indirect noisy observations. The
registration uses diffeomorphisms that transform the template through a (group)
action. These diffeomorphisms are generated by solving a flow equation that is
defined by a velocity field with certain regularity. The theoretical analysis
includes a proof that indirect image registration has solutions (existence)
that are stable and that converge as the data error tends so zero, so it
becomes a well-defined regularization method. The paper concludes with examples
of indirect image registration in 2D tomography with very sparse and/or highly
noisy data.Comment: 43 pages, 4 figures, 1 table; revise
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