284 research outputs found
Learning Probabilistic Coordinate Fields for Robust Correspondences
We introduce Probabilistic Coordinate Fields (PCFs), a novel
geometric-invariant coordinate representation for image correspondence
problems. In contrast to standard Cartesian coordinates, PCFs encode
coordinates in correspondence-specific barycentric coordinate systems (BCS)
with affine invariance. To know \textit{when and where to trust} the encoded
coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which
parameterizes the distribution of coordinate fields as Gaussian mixture models.
By jointly optimizing coordinate fields and their confidence conditioned on
dense flows, PCF-Net can work with various feature descriptors when quantifying
the reliability of PCFs by confidence maps. An interesting observation of this
work is that the learned confidence map converges to geometrically coherent and
semantically consistent regions, which facilitates robust coordinate
representation. By delivering the confident coordinates to keypoint/feature
descriptors, we show that PCF-Net can be used as a plug-in to existing
correspondence-dependent approaches. Extensive experiments on both indoor and
outdoor datasets suggest that accurate geometric invariant coordinates help to
achieve the state of the art in several correspondence problems, such as sparse
feature matching, dense image registration, camera pose estimation, and
consistency filtering. Further, the interpretable confidence map predicted by
PCF-Net can also be leveraged to other novel applications from texture transfer
to multi-homography classification.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligenc
Métodos matemáticos e computacionais para modelagem e edição de deformações
Orientador: Jorge StolfiTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Nesta tese, descrevemos primeiramente o algoritmo ECLES (Editing by Constrained LEast Squares), um método geral para edição interativa de objetos definidos por parâmetros sujeitos a restrições lineares ou afins. Neste método, as restrições e as ações de edição do usuário são combinadas usando mínimos quadrados restritos, ao invés da abordagem mais comum de elementos finitos. Usamos aritmética exata para detectar e eliminar redundâncias no conjunto de restrições e evitar falhas devido a erros de arredondamento. O algoritmo ECLES tem diversas aplicações. Entre elas, podemos citar a edição de deformações spline com continuidade C¹. Nesta tese, descrevemos um método interativo de edição de deformações do plano, o algoritmo 2DSD (2D Spline Deformation). As deformações são definidas por splines de grau 5 sobre uma malha triangular arbitrária. Estas deformações são editadas alterando-se as posições dos pontos de controle da malha. O algoritmo ECLES é usado em cada ação de edição do usuário para detectar, de forma robusta e eficiente, o conjunto de restrições de continuidade C¹ que são relevantes, garantindo que não existam redundâncias. Em seguida, como os parâmetros são modificados pelo usuário, o ECLES é chamado para calcular as novas posições dos pontos de controle satisfazendo as restrições e as posições especificadas pelo usuário. A fim de validar nosso método 2DSD, ele foi utilizado como parte de um editor interativo para deformações do espaço 2.5D, o editor PrisMystic. Este editor foi utilizado, principalmente, para deformar modelos tridimensionais de organismos microscópicos não-rígidos de modo a coincidir com imagens reais de microscopia ótica. Também utilizamos o editor para editar modelos de terrenosAbstract: In this thesis, we present the ECLES algorithm (Editing by Constrained LEast Squares), a general method for interactive editing of objects that are defined by parameters subject to linear or affine constraints. In this method, the constraints and the user editing actions are combined using constrained least squares instead of the usual finite element approach. We use exact integer arithmetic in order to detect and eliminate redundancies in the set of constraints and to avoid failures due to rounding errors. The ECLES algorithm has various applications. Among them, we can cite the editing of C¹-continuous spline deformations. In this thesis, we describe an interactive editing method for deformations of the plane, the 2DSD algorithm (2D Spline Deformation). The deformations are defined by splines of degree 5 on an arbitrary triangular mesh. The deformations are edited by changing the positions of its control points. The ECLES algorithm is first used in each user editing action in order to detect, in a robust and efficient way, the set of relevant constraints of C¹ continuity, ensuring that there are no redundancies. Then, as the parameters are changed by the user, ECLES is called to compute the new positions of the control points satisfying the constraints and the positions specified by the user. To validate our 2DSD algorithm, we used it as part of an interactive editor for 2.5D space deformations, the PrisMystic editor. This editor has been used, mainly, to deform 3D models of non-rigid living microscopic organisms as seen in actual optical microscope images. We also used the editor to edit terrain modelsDoutoradoCiência da ComputaçãoDoutora em Ciência da Computação140780/2013-001-P-04554-2013CNPQCAPE
Alternative Data Reduction Procedures for UVES: Wavelength Calibration and Spectrum Addition
This paper addresses alternative procedures to the ESO supplied pipeline
procedures for the reduction of UVES spectra of two quasar spectra to determine
the value of the fundamental constant mu = Mp/Me at early times in the
universe. The procedures utilize intermediate product images and spectra
produced by the pipeline with alternative wavelength calibration and spectrum
addition methods. Spectroscopic studies that require extreme wavelength
precision need customized wavelength calibration procedures beyond that usually
supplied by the standard data reduction pipelines. An example of such studies
is the measurement of the values of the fundamental constants at early times in
the universe. This article describes a wavelength calibration procedure for the
UV-Visual Echelle Spectrometer on the Very Large Telescope, however, it can be
extended to other spectrometers as well. The procedure described here provides
relative wavelength precision of better than 3E-7 for the long-slit
Thorium-Argon calibration lamp exposures. The gain in precision over the
pipeline wavelength calibration is almost entirely due to a more exclusive
selection of Th/Ar calibration lines.Comment: Accepted for publication in New Astronom
Solving Correspondences for Non-Rigid Deformations
Projecte final de carrera realitzat en col.laboració amb l'IR
COMPOSE: Compacted object sample extraction a framework for semi-supervised learning in nonstationary environments
An increasing number of real-world applications are associated with streaming data drawn from drifting and nonstationary distributions. These applications demand new algorithms that can learn and adapt to such changes, also known as concept drift. Proper characterization of such data with existing approaches typically requires substantial amount of labeled instances, which may be difficult, expensive, or even impractical to obtain. In this thesis, compacted object sample extraction (COMPOSE) is introduced - a computational geometry-based framework to learn from nonstationary streaming data - where labels are unavailable (or presented very sporadically) after initialization. The feasibility and performance of the algorithm are evaluated on several synthetic and real-world data sets, which present various different scenarios of initially labeled streaming environments. On carefully designed synthetic data sets, we also compare the performance of COMPOSE against the optimal Bayes classifier, as well as the arbitrary subpopulation tracker algorithm, which addresses a similar environment referred to as extreme verification latency. Furthermore, using the real-world National Oceanic and Atmospheric Administration weather data set, we demonstrate that COMPOSE is competitive even with a well-established and fully supervised nonstationary learning algorithm that receives labeled data in every batch
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