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A variational model for P+XS image fusion

INTERNATIONAL JOURNAL OF computer vision 2006年第1期69卷 43-58页

作者： Ballester, Coloma Caselles, Vicent Igual, Laura Verdera, Joan Rougé, Bernard Univ Pompeu Fabra Dept Tecnol Barcelona 08003 Spain CNES F-31401 Toulouse France

We propose an algorithm to increase the resolution of multispectral satellite images knowing the panchromatic image at high resolution and the spectral channels at lower resolution. Our algorithm is based on the assumption that, to a large extent,. the geometry of the spectral channels is contained in the topographic map of its panchromatic image. This assumption, to-ether with the relation of the panchromatic image to the spectral channels. and the expression of the low-resolution pixel in terms of the high-resolution pixels given by some convolution kernel followed by subsampling, constitute the elements for constructing an energy functional (with several variants) whose minima will give the reconstructed spectral images at higher resolution. We discuss the validity of the above approach and describe our numerical procedure. Finally. some experiments on a set of multispectral satellite images are displayed.

关键词： multispectral images topographic map image fusion energy functional

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Flame front matching and tracking in PLIF images using geodesic paths and level sets

Flame front matching and tracking in PLIF images using geode...

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1st ieee workshop on variational and level set methods in computer vision

作者： Abu-Gharbieh, R Kaminski, C Gustavsson, T Hamarneh, G Chalmers Univ Technol Dept Signals & Syst S-41296 Gothenburg Sweden

ISBN: (纸本)076951278X

This paper treats the problem of tracking contours of flames captured by PLIF imaging using geodesic paths and level sets. Successive images of the combustion process captured in controlled experiments are smoothed by, non-linear diffusion filtering then active contour models are used to obtain the curves that most accurately describe the flame boundary. These curves are matched using the concept of shortest path (geodesic) computation on a cost surface. level set representation is employed so that complex curve evolutions including those with topological changes in their structures could be handled. A critical point detection algorithm is used to identify important curve landmarks that are then used to modify the cost surface so as to improve the quality and stability of the matching. Accordingly, the propagation of curves representing successive flame contours within a sequence is obtained and used for studying the flame dynamics.

关键词： Geodesy

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Fast constrained surface extraction by minimal paths

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INTERNATIONAL JOURNAL OF computer vision 2006年第1期69卷 127-136页

作者： Ardon, Roberto Cohen, Laurent D. Philips France MEDYSIS F-92120 Suresnes France Univ Paris 09 CEREMADE UMR 7534 F-75775 Paris 16 France

In this paper we consider a new approach for single object segmentation in 3D images. Our method improves the classical geodesic active surface model. It greatly simplifies the model initialization and naturally avoids local minima by incorporating user extra information into the segmentation process. The initialization procedure is reduced to introducing 3D curves into the image. These curves are supposed to belong to the surface to extract and thus, also constitute user given information. Hence. our model finds a surface that has these curves as boundary conditions and that minimizes the integral of a potential function that corresponds to the image features. Our goal is achieved by using globally minimal paths. We approximate the surface to extract by a discrete network of paths. Furthermore, an interpolation method is used to build a mesh or an implicit representation based on the information retrieved from the network of paths. Our paper describes a fast construction obtained by exploiting the Fast Marching algorithm and a fast analytical interpolation method. Moreover, a level set method can be used to refine the segmentation when higher accuracy is required. The algorithm has been successfully applied to 3D medical images and synthetic images.

关键词： active surfaces active contours minimal paths level set method object extraction

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variational methods for multimodal image matching

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INTERNATIONAL JOURNAL OF computer vision 2002年第3期50卷 329-343页

作者： Hermosillo, G Chefd'Hotel, C Faugeras, O INRIA Sophia Antipolis Odyssee Lab Sophia Antipolis France

Matching images of different modalities can be achieved by the maximization of suitable statistical similarity measures within a given class of geometric transformations. Handling complex, nonrigid deformations in this context turns out to be particularly difficult and has attracted much attention in the last few years. The thrust of this paper is that many of the existing methods for nonrigid monomodal registration that use simple criteria for comparing the intensities (e. g. SSD) can be extended to the multimodal case where more complex intensity similarity measures are necessary. To this end, we perform a formal computation of the variational gradient of a hierarchy of statistical similarity measures, and use the results to generalize a recently proposed and very effective optical flow algorithm (L. Alvarez, J. Weickert, and J. Sanchez, 2000, Technical Report, and IJCV 39(1):41-56) to the case of multimodal image registration. Our method readily extends to the case of locally computed similarity measures, thus providing the flexibility to cope with spatial non-stationarities in the way the intensities in the two images are related. The well posedness of the resulting equations is proved in a complementary work (O.D. Faugeras and G. Hermosillo, 2001, Technical Report 4235, INRIA) using well established techniques in functional analysis. We briefly describe our numerical implementation of these equations and show results on real and synthetic data.

关键词： image matching cross correlation correlation ratio mutual information partial differential equations regularization variational methods

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A summary of geometric level-set analogues for a general class of parametric active contour and surface models

A summary of geometric level-set analogues for a general cla...

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1st ieee workshop on variational and level set methods in computer vision

作者： Xu, CY Yezzi, A Prince, JL Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)076951278X

Geometric active contours (GACs) and surfaces (GASs) implemented via level set techniques enjoy many advantages over parametric active contours (PACs) and surfaces (PASs), such as computational stability and the ability to change topology during deformation. While many capabilities of earlier PACs and PASs have been reproduced by various GACs and GASs, and while relationships have been discussed for a variety of specific cases, a comprehensive accounting of the connections between these two worlds (particularly regarding rigid forces) has not been consolidated thus far. In this paper we present the precise mathematical relationships between the two for an extensive family of both active contour and surface models, encompassing spatially varying coefficients, both tension and rigidity, and both conservative and non-conservative external forces. The result is a very general geometric formulation for which the intuitive design principles of PACs and PASs can be applied. We also point out which type of PAC and PAS methodologies cannot be adapted to the geometric level set framework. We conclude by demonstrating several geometric adaptations of specific PACs and PASs in several simulations.

关键词： Numerical methods

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Using prior shapes in geometric active contours in a variational framework

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INTERNATIONAL JOURNAL OF computer vision 2002年第3期50卷 315-328页

作者： Chen, YM Tagare, HD Thiruvenkadam, S Huang, F Wilson, D Gopinath, KS Briggs, RW Geiser, EA Univ Florida Dept Math Gainesville FL 32611 USA Yale Univ Dept Diagnost Radiol New Haven CT 06520 USA Yale Univ Dept Elect Engn New Haven CT 06520 USA Univ Florida Dept Radiol Gainesville FL 32611 USA Univ Florida Dept Med Div Cardiol Gainesville FL 32610 USA

In this paper, we report an active contour algorithm that is capable of using prior shapes. The energy functional of the contour is modified so that the energy depends on the image gradient as well as the prior shape. The model provides the segmentation and the transformation that maps the segmented contour to the prior shape. The active contour is able to find boundaries that are similar in shape to the prior, even when the entire boundary is not visible in the image (i.e., when the boundary has gaps). A level set formulation of the active contour is presented. The existence of the solution to the energy minimization is also established. We also report experimental results of the use of this contour on 2d synthetic images, ultrasound images and fMRI images. Classical active contours cannot be used in many of these images.

关键词： segmentation registration shape prior active contour variational method

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A self-referencing level-set method for image reconstruction from sparse Fourier samples

A self-referencing level-set method for image reconstruction...

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ieee workshop on variational and level set methods in computer vision, VLSM 2001

作者： Ye, Jong Chul Bresler, Y. Moulin, P. Coordinated Science Laboratory University of Illinois Urbana-ChampaignIL61801 United States

ISBN: (纸本)076951278X

We address image estimation from sparse Fourier samples. The problem is formulated as joint estimation of the supports of unknown sparse objects in the image, and pixel values on these supports. The domain and the pixel values are alternately estimated using the level-set method and the conjugate gradient method, respectively. Our level-set evolution shows a unique switching behavior, which stabilizes the level-set evolution. Furthermore, the trade-off between the stability and the speed of evolution can be easily controlled by the number of the conjugate gradient steps, hence removing the re-initialization steps in conventional level set approaches. © 2001 ieee.

关键词： Conjugate gradient method

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variational surface interpolation from sparse point and normal data

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ieee TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2007年第1期29卷 181-184页

作者： Solem, Jan Erik Aanaes, Henrik Heyden, Anders Malmo Univ Hosp Sch Technol & Soc SE-20506 Malmo Sweden Tech Univ Denmark Informat & Math Modelling Dept DK-2800 Lyngby Denmark

Many visual cues for surface reconstruction from known views are sparse in nature, e.g., specularities, surface silhouettes, and salient features in an otherwise textureless region. Often, these cues are the only information available to an observer. To allow these constraints to be used either in conjunction with dense constraints such as pixel-wise similarity, or alone, we formulate such constraints in a variational framework. We propose a sparse variational constraint in the level set framework, enforcing a surface to pass through a specific point, and a sparse variational constraint on the surface normal along the observed viewing direction, as is the nature of, e.g., specularities. These constraints are capable of reconstructing surfaces from extremely sparse data. The approach has been applied and validated on the shape from specularities problem.

关键词： variational methods computer vision level set method shape from specularities multiple view stereo surface interpolation

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A variational approach to multi-modal image matching

A variational approach to multi-modal image matching

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ieee workshop on variational and level set methods in computer vision, VLSM 2001

作者： Chefd'Hotel, C. Hermosillo, G. Faugeras, O. Artificial Intelligence Lab. MIT CambridgeMA United States INRIA Sophia-Antipolis France

ISBN: (纸本)076951278X

We address the problem of nonparametric multi-modal image matching. We propose a generic framework which relies on a global variational formulation and show its versatility through three different multi-modal registration methods: supervised registration by joint intensity learning, maximization of the mutual information and maximization of the correlation ratio. Regularization is performed by using a functional borrowed from linear elasticity theory. We also consider a geometry-driven regularization method. Experiments on synthetic images and preliminary results on the realignment of MRI datasets are presented. © 2001 ieee.

关键词： Image matching

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Curvature-driven PDE methods for matrix-valued images

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INTERNATIONAL JOURNAL OF computer vision 2006年第1期69卷 93-107页

作者： Feddern, Christian Weickert, Joachim Burgeth, Bernhard Welk, Martin Univ Saarland Math Image Anal Grp Fac Math & Comp Sci D-66041 Saarbrucken Germany

Matrix-valued data sets arise in a number of applications including diffusion tensor magnetic resonance imaging (DT-MRI) and physical measurements of anisotropic behaviour. Consequently. there arises the need to filter and segment such tensor fields. In order to detect edge-like structures in tensor fields, we first generalise Di Zenzo's concept of a structure tensor for vector-valued images to tensor-valued data. This structure tensor allows us to extend scalar-valued mean curvature motion and self-snakes to the tensor setting. We present both two-dimensional and three-dimensional formulations, and we prove that these filters maintain positive semidefiniteness if the initial matrix data are positive semidefinite. We give an interpretation of tensorial mean curvature motion as a process for which the Corresponding curve evolution of each generalised level line is the gradient descent of its total length. Moreover, we propose a geodesic active contour model for segmenting tensor fields and interpret it as a minimiser of a suitable energy functional with a metric induced by the tensor image. Since tensorial active contours incorporate information from all channels, they give a contour representation that is highly robust under noise. Experiments oil three-dimensional DT-MRI data and an indefinite tensor field from fluid dynamics show that the proposed methods inherit the essential properties of their scalar-valued counterparts.

关键词： DT-MRI denoising segmentation edge detection structure tensor mean curvature motion self-snakes active contours

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