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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2005年 3752 LNCS卷 V页

作者： Paragios, Nikos Faugeras, Olivier Chan, Tony Schnoerr, Christoph

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Natural image statistics for natural image segmentation

Natural image statistics for natural image segmentation

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International Conference on computer vision (ICCV)

作者： Heiler Schnorr Department of Mathematics and Computer Science University of Mannheim Germany

Building on recent progress in modeling filter response statistics of natural images we integrate a statistical model into a variational framework for image segmentation. Incorporated in a sound probabilistic distance measure the model drives level sets toward meaningful segmentations of complex textures and natural scenes. Since each region comprises two model parameters only the approach is computationally efficient and enables the application of variational segmentation to a considerably larger class of real-world images. We validate the statistical basis of our approach on thousands of natural images and demonstrate that our model outperforms recent variational segmentation methods based on second-order statistics.

关键词： Image segmentation Layout computer vision Parametric statistics Mathematical model level set Bayesian methods Image databases computer graphics Pattern recognition

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variational space-time motion segmentation

Variational space-time motion segmentation

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International Conference on computer vision (ICCV)

作者： Cremers Soatto Department of Computer Science University of California Los Angeles CA USA

We propose a variational method for segmenting image sequences into spatiotemporal domains of homogeneous motion. To this end, we formulate the problem of motion estimation in the framework of Bayesian inference, using a prior which favors domain boundaries of minimal surface area. We derive a cost functional which depends on a surface in space-time separating a set of motion regions, as well as a set of vectors modeling the motion in each region. We propose a multiphase level set formulation of this functional, in which the surface and the motion regions are represented implicitly by a vector-valued level set function. Joint minimization of the proposed functional results in an eigenvalue problem for the motion model of each region and in a gradient descent evolution for the separating interface. Numerical results on real-world sequences demonstrate that minimization of a single cost functional generates a segmentation of space-time into multiple motion regions.

关键词： Motion segmentation computer vision Cost function level set Image segmentation Image sequences Spatiotemporal phenomena Motion estimation Bayesian methods Eigenvalues and eigenfunctions

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Adaptive region-based video registration

Adaptive region-based video registration

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ieee workshop on Motion and Video Computing, MOTION 2005

作者： Xiao, Jiangjian Zhang, Yunjun Shah, Mubarak Computer Vision Lab. School of Computer Science University of Central Florida Orlando FL 32816 United States

ISBN: (纸本)0769522718

Video registration without meta data (camera location, viewing angles, and reference DEMs) is still a challenging problem. With the aim of handling this kind of problem, this paper presents an adaptive region expansion approach to propagate the alignment process from high confidence areas (reliable salient features) to low confidence areas and to simultaneously remove outlier regions. Hence, we re-cast the image registration problem as a partitioning problem such that we determine the optimal supporting regions and their corresponding motion parameters for the registration. First, we determine sparse robust correspondences between mission and reference images by using our wide baseline algorithm. Next, starting from the seed regions, the aligned areas are expanded to the whole overlapping areas using the graph cut algorithm, which is controlled by the level set representation of the previous region shape. Consequently, a robust video registration is achieved if the scene can be represented by one homography. Furthermore, we extend this approach to multi-homography video registration problem for 3D scenes, which cannot be directly solved by the current alignment methods. Using our motion layer extraction algorithm, the mission video first is segmented into several layers, then each layer is respectively aligned to the reference image by employing the region expansion algorithm. Several examples are demonstrated in the experiments to show that our approach is effective and robust.

关键词： Image registration

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Threshold-based 3D Tumor Segmentation using level set (TSL)

Threshold-based 3D Tumor Segmentation using Level Set (TSL)

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ieee workshop on Applications of computer vision (WACV)

作者： Sima Taheri Sim Heng Ong Vincent Chong Department of Electrical and Computer Engineering Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Diagnostic Radiology National University of Singapore Singapore

Three-dimensional segmentation is reliable approach to achieve a proper estimation of tumor volume. Among all possible methods for this purpose, level set can be used as a powerful tool which implicitly extracts the tumor surface. In this paper, we introduce a threshold-based algorithm for 3D tumor segmentation using level set (TSL). This algorithm uses a global threshold to form the level set speed function which is updated iteratively throughout the level set growing process. An important feature of TSL is that no explicit knowledge about the tumor and non-tumor density functions is required. The proposed method can be implemented in the automatic and semi-automatic forms depending on the complexity of the tumor shape. TSL is examined on several clinical MRIs for both visual and quantitative evaluation. Experimental results demonstrate the effectiveness of our approach

关键词： Neoplasms level set Iterative algorithms Density functional theory Shape Magnetic resonance imaging Image segmentation Iterative methods Radiology Medical treatment

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A fully implicit alternating direction method of multipliers for the minimization of convex problems with an application to motion segmentation

A fully implicit alternating direction method of multipliers...

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ieee workshop on Applications of computer vision (WACV)

作者： Karin Tichmann Oliver Junge Center for Mathematics Technische Universität München Garching Germany

ISBN: (纸本)9781479949847

Motivated by a variational formulation of the motion segmentation problem, we propose a fully implicit variant of the (linearized) alternating direction method of multipliers for the minimization of convex functionals over a convex set. The new scheme does not require a step size restriction for stability and thus approaches the minimum using considerably fewer iterates. In numerical experiments on standard image sequences, the scheme often significantly outperforms other state of the art methods.

关键词： Minimization Motion segmentation computer vision Vectors Image sequences Linear systems Standards

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Nonlinear Dynamical Shape Priors for level set Segmentation

Nonlinear Dynamical Shape Priors for Level Set Segmentation

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Daniel Cremers Department of Computer Science University of Bonn Germany

The introduction of statistical shape knowledge into level set based segmentation methods was shown to improve the segmentation of familiar structures in the presence of noise, clutter or partial occlusions. While most work has been focused on shape priors which are constant in time, it is clear that when tracking deformable shapes certain silhouettes may become more or less likely over time. In fact, the deformations of familiar objects such as the silhouettes of a walking person are often characterized by pronounced temporal correlations. In this paper, we propose a nonlinear dynamical shape prior for level set based image segmentation. Specifically, we propose to approximate the temporal evolution of the eigenmodes of the level set function by means of a mixture of autoregressive models. We detail how such shape priors "with memory" can be integrated into a variational framework for level set segmentation. As an application, we experimentally validate that the nonlinear dynamical prior drastically improves the tracking of a person walking in different directions, despite large amounts of clutter and noise.

关键词： level set Image segmentation Legged locomotion Topology Shape measurement Noise shaping Bayesian methods Humans Embedded computing Principal component analysis

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Mutual Segmentation with level sets

Mutual Segmentation with Level Sets

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ieee computer Society Conference on computer vision and Pattern Recognition workshops (CVPRW)

作者： T. Riklin-Raviv N. Sochen N. Kiryati Tel-Aviv University Tel-Aviv Israel

We suggest a novel variational approach for mutual segmentation of two images of the same object. The images are taken from different views, related by projective transformation. Each of the two images may not provide sufficient information for correct object-background delineation. The emerging segmentation of the object in each view provides a dynamic prior for the segmentation of the other image. The foundation of the proposed method is a unified level-set framework for region and edge based segmentation, associated with a shape similarity term. The dissimilarity between the two shape representations accounts for excess or deficient parts and is invariant to planar projective transformation. The suggested algorithm extracts the object in both images, correctly recovers its boundaries, and determines the homography between the two object views.

关键词： Image segmentation Noise shaping Shape measurement Data mining Object segmentation Shadow mapping level set Cost function Blood vessels Biomedical imaging

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Visibility constrained surface evolution

Visibility constrained surface evolution

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Conference on computer vision and Pattern Recognition (CVPR)

作者： J.E. Solem F. Kahl A. Heyden Applied Mathematics Group School of Technology and Society Malmö University Sweden Department of Computer Science & Engineering University of California La Jolla CA USA

The problem of feature-based surface reconstruction is considered in this paper. Our main contribution is the ability to handle visibility constraints, obtained from the projections of points, curves and silhouettes, in the surface fitting process. While traditional methods often ignore such information, we show that visibility constraints not only give better initial surface estimates and faster convergence, but also provide an important cue for determining surface topology. The problem is cast as a variational problem with constraints within the level set framework. It is shown how to evolve the surface without violating the visibility constraints using methods from variational calculus. Applications of the theory are detailed for a number of important cases of geometric primitives: points, curves and visual hulls. Several experiments on real image sequences are given to demonstrate the performance of the approach.

关键词： Surface fitting Surface reconstruction Layout level set Image reconstruction Topology Cameras Clouds Mathematics computer science

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Multiview normal field integration using level set methods

Multiview normal field integration using level set methods

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Ju Yong Chang Kyoung Mu Lee Sang Uk Lee School of Electrical Engineering Seoul National University Seoul South Korea

In this paper, we propose a new method to integrate multiview normal fields using level sets. In contrast with conventional normal integration algorithms used in shape from shading and photometric stereo that reconstruct a 2.5D surface using a single-view normal field, our algorithm can combine multiview normal fields simultaneously and recover the full 3D shape of a target object. We formulate this multiview normal integration problem by an energy minimization framework and find an optimal solution in a least square sense using a variational technique. A level set method is applied to solve the resultant geometric PDE that minimizes the proposed error functional. It is shown that the resultant flow is composed of the well known mean curvature and flux maximizing flows. In particular, we apply the proposed algorithm to the problem of 3D shape modelling in a multiview photometric stereo setting. Experimental results for various synthetic data show the validity of our approach.

关键词： level set Shape Photometry Noise shaping Stereo vision Surface reconstruction computer vision Poisson equations computer errors Least squares methods

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