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ieee workshop on variational and level set methods in computer vision

作者： N. Paragios Siemens AG Corporate Research and Development Princeton NJ USA

ISBN: (纸本)076951278X

This paper proposes a new front propagation method to segment MR cardiac images. This framework is based on the geodesic active region model, refers to a coupled propagation of two curves (inner and outer cardiac contours) and integrates boundary and region-based segmentation modules. The boundary information is introduced to the objective function using the gradient vector flow framework while the region information using continuous probability density functions. The defined objective function is minimised using a gradient descent method and the obtained motion equations are implemented using a level set approach. A recently introduced numerical approximation scheme with fast convergence rate and stable behavior is used to implement the level set motion equations. Finally, according to the application the propagations of the level set contours are coupled using their relative distances. Encouraging experimental results are provided using real data.

关键词： Image segmentation level set Equations Application software Surface fitting Shape Noise shaping Educational institutions Probability density function Convergence of numerical methods

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variational problems and PDEs on implicit surfaces

Variational problems and PDEs on implicit surfaces

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ieee workshop on variational and level set methods in computer vision

作者： M. Bertalmio G. Sapiro Li-Tien Cheng S. Osher Electrical and Computer Engineering University of Minnesota USA Mathematics Department University of California Los Angeles USA

ISBN: (纸本)076951278X

A novel framework for solving variational problems and partial differential equations for scalar and vector-valued data defined on surfaces is introduced. The key idea is to implicitly represent the surface as the level set of a higher dimensional function, and solve the surface equations in a fixed Cartesian coordinate system using this new embedding function. The equations are then both intrinsic to the surface and defined in the embedding space. This approach thereby eliminates the need for performing complicated and inaccurate computations on triangulated surfaces, as is commonly done in the literature. We describe the framework and present examples in computer graphics and image processing applications, including texture synthesis, flow field visualization, as well as image and vector field intrinsic regularization for data defined on 3D surfaces.

关键词： computer graphics Image processing Partial differential equations Application software Data visualization Differential equations Surface texture Physics Laplace equations Embedded computing

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PERFORMANCE BOUNDS FOR ESTIMATING 3-DIMENSIONAL MOTION PARAMETERS FROM A SEQUENCE OF NOISY IMAGES

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND vision 1989年第6期6卷 879-889页

作者： BROIDA, TJ CHELLAPPA, R []Signal Image Processing Institute University of Southern California University Park PHE 304 Los Angeles California 90089-0272

The problem considered here involves the use of a sequence of monocular images of a three-dimensional moving object to estimate both its structure and kinematics. The object is assumed to be rigid, and its motion is assumed to be smooth. A set of object match points is assumed to be available, consisting of fixed features on the object, the image plane coordinates of which have been extracted from successive images in the sequence. The measured data are the noisy image plane coordinates of this set of object match points, taken from each image in the sequence. In previous papers [ieee Trans. Pattern Anal. Mach. Intell. PAMI-8, 90 (1986); in Proceedings of the ieee workshop on Motion: Representation and Analysis (Institute of Electrical and Electronics Engineers, New York, 1986), p. 95; in Proceedings of the ieee Conference on computer vision and Pattern Recognition (Institute of Electrical and Electronics Engineers, New York, 1986), p. 176] we discussed model-based approaches for motion and structure estimation from a long sequence of images. We examine here the performance of such techniques by using a Cramér–Rao lower bounds on the estimation error variance. This method permits a priori prediction of estimation accuracy as a function of a number of factors, including the number of images in the sequence, the time at which each image is made, the number of feature points used, the image-plane noise level, and the type of motion that is involved. Theoretical performance predictions are compared with the statistics of Monte Carlo simulation, and it is shown that the actual estimation accuracy is close to the Cramér–Rao bounds in most cases. These results also show that noisy sequences with fewer than four images often do not contain enough information to permit accurate estimation of motion and structure parameters. This conclusion is consistent with the observed instability of so-called two-frame estimation methods in the presence of noise.

关键词： Electronics Imaging noise Machine vision Motion estimation Pattern recognition Three dimensional imaging

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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

作者： Jong Chul Ye Y. Bresler P. Moulin Coordinated Science Laboratory University of Illinois Urbana-Champaign IL USA

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.

关键词： Image reconstruction Paramagnetic resonance Magnetic resonance imaging Image sampling Fourier transforms Passive radar Spaceborne radar Radar imaging Computed tomography Positron emission tomography

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Regularization of orthonormal vector sets using coupled PDE's

Regularization of orthonormal vector sets using coupled PDE'...

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ieee workshop on variational and level set methods in computer vision

作者： D. Tschumperle R. Deriche I.N.R.I.A. Sophia-Antipolis France

ISBN: (纸本)076951278X

We address the problem of restoring, while presenting possible discontinuities, fields of noisy orthonormal vector sets, taking the orthonormal constraints explicity into account. We develop a variational solution for the general case where each image feature may correspond to multiple n-D orthogonal vectors of unit norms. We first formulate the problem in a new variational framework, where discontinuities and orthonormal constraints are preserved by means of constrained minimization and /spl Phi/-function regularization, leading to a set of coupled anisotropic diffusion PDE. A geometric interpretation of the resulting equations, coming from the field of solid mechanics, is proposed for the 3D case. Two interesting restrictions of our framework are also tackled: the regularization of 30 rotation matrices and the direction diffusion (the parallel with previous works is made). Finally, we present a number of denoising results and applications.

关键词： Image restoration Color Anisotropic magnetoresistance Equations Solids Noise reduction Application software computer vision Image motion analysis Functional analysis

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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

作者： C. Chefd'Hotel G. Hermosillo O. Faugeras MIT Artificial Intelligence Laboratory Cambridge MA USA 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.

关键词： Image matching Magnetic resonance imaging Optical computing Mutual information Elasticity Biomedical optical imaging Optical distortion Optical sensors Biomedical imaging Artificial intelligence

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Multiple contour finding and perceptual grouping using minimal paths

Multiple contour finding and perceptual grouping using minim...

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ieee workshop on variational and level set methods in computer vision

作者： L.D. Cohen CEREMADE UMR 7534 Université Paris-Dauphine Paris France

ISBN: (纸本)076951278X

We present a new approach for finding a set of contour curves in an image. We consider the problem of perceptual grouping and contour completion, where the data is a set of points in the image. A new method to find complete curves from a set of contours or edge points is presented. Our approach is based on a previous work on finding contours as minimal paths between two end points using the fast marching algorithm (Cohen et al., 1997). Given a set of key points, we find the pairs of points that have to be linked. The paths that join them complete the initial set of contours and allow us to close them. In a second part, we propose a scheme that does not need key points for initialization. Key points are automatically selected from a larger set of admissible points. We illustrate the capability of our approach to close contours with synthetic examples.

关键词： Active contours level set Topology Shape Humans computer vision Energy measurement Stochastic processes

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A level set algorithm for minimizing the Mumford-Shah functional in image processing

A level set algorithm for minimizing the Mumford-Shah functi...

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ieee workshop on variational and level set methods in computer vision

作者： T.F. Chan L.A. Vese Department of Mathematics University of California Los Angeles Los Angeles CA USA

ISBN: (纸本)076951278X

We show how the piecewise-smooth Mumford-Shah segmentation problem can be solved using the level set method of Osher and sethian (1988). The obtained algorithm can be simultaneously used to denoise, segment, detect-extract edges, and perform active contours. The proposed model is also a generalisation of a previous active contour model without edges, proposed by the authors in Chan et al., (2001), and of its extension to the case with more than two segments for piecewise-constant segmentation Chan et al., (2000). Based on the four color theorem, we can assume that in general, at most two level set functions are sufficient to detect and represent distinct objects of distinct intensities, with triple junctions, or T-junctions.

关键词： level set Image processing Image segmentation Active contours Contracts Mathematics Image edge detection Object detection Topology Phase detection

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A new variational image restoration applied to 3D angiographies

A new variational image restoration applied to 3D angiograph...

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ieee workshop on variational and level set methods in computer vision

作者： K. Krissian Departamento de Informatica y Sistemas Campus de Tafira Universidad de Las Palmas de Gran Canaria Spain

ISBN: (纸本)076951278X

We propose a new variational restoration method. We express the energy as the sum of a data attachment term. A contour smoothing term and an enhancement term. The contour smoothing is achieved by minimizing the square of the derivative of the intensity in the contour direction. The enhancement is obtained by minimizing the square of the gradient norm in each estimated region, and acts like shock filters. The minimization of the energy is then done using the conjugate gradient algorithm. We present an algorithm which allows us to compute easily the gradient of the energy in the discrete case, without calculating the Euler-Lagrange equations. Experiments have been carried out on both synthetic and real images applied to 3D angiographies.

关键词： Image restoration Image segmentation Gaussian noise Smoothing methods Equations Noise reduction Angiography Minimization methods Convolution Electric shock

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Optimal mass transport and image registration

Optimal mass transport and image registration

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ieee workshop on variational and level set methods in computer vision

作者： S. Haker A. Tannenbaum Department of Radiology Surgical Planning Laboratory Brigham and Women''s Hospital Boston MA USA Departments of Electrical and Computer and Biomedical Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)076951278X

Image registration is the process of establishing a common geometric reference frame between two or more data sets from the same or different imaging modalities possibly taken at different times. In the context of medical imaging and in particular image-guided therapy, the registration problem consists of finding automated methods that align multiple data sets with each other and with the patient. We propose a method of elastic registration based on the Monge-Kantorovich problem of optimal mass transport.

关键词： Image registration Biomedical imaging Surgery Transportation Shape measurement Medical treatment Magnetic resonance imaging Radiology Process planning Technology planning

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