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Dynamical statistical shape priors for level set based sequence segmentation

3rd International workshop on variational, Geometric, and level set methods in computer vision

作者： Cremers, D Funka-Lea, G Siemens Corp Res Dept Imaging & Visualizat Princeton NJ USA

ISBN: (纸本)3540293485

In recent years, researchers have proposed to introduce statistical shape knowledge into the level set method in order to cope with insufficient low-level information. While these priors were shown to drastically improve the segmentation of images or image sequences, so far the focus has been on statistical shape priors that are time-invariant. Yet, in the context of tracking deformable objects, it is clear that certain silhouettes may become more or less likely over time. In this paper, we tackle the challenge of learning dynamical statistical models for implicitly represented shapes. We show how these can be integrated into a segmentation process in a Bayesian framework for image sequence segmentation. Experiments demonstrate that such shape priors with memory can drastically improve the segmentation of image sequences.

关键词： Image segmentation

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Advances in variational image segmentation using AM-FM models: Regularized demodulation and probabilistic cue integration

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3rd International workshop on variational, Geometric, and level set methods in computer vision

作者： Evangelopoulos, G Kokkinos, I Maragos, P Natl Tech Univ Athens Comp Vis Speech Commun & Signal Proc Grp Athens Greece

ISBN: (纸本)3540293485

Current state-of-the-art methods in variational image segmentation using level set methods are able to robustly segment complex textured images in an unsupervised manner. In recent work, [18,19] we have explored the potential of AM-FM features for driving the unsupervised segmentation of a wide variety of textured images. Our first contribution in this work is at the feature extraction level, where we introduce a regularized approach to the demodulation of the AM-FM-modelled signals. By replacing the cascade of multiband filtering and subsequent differentiation with analytically derived equivalent filtering operations, increased noise-robustness can be achieved, while discretization problems in the implementation of the demodulation algorithm are alleviated. Our second contribution is based on a generative model we have recently proposed [18,20] that offers a measure related to the local prominence of a specific class of features, like edges and textures. The introduction of these measures as weighting terms in the evolution equations facilitates the fusion of different cues in a simple and efficient manner. Our systematic evaluation on the Berkeley segmentation benchmark demonstrates that this fusion method offers improved results when compared to our previous work as well as current state-of-the-art methods.

关键词： Image segmentation

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A variational approach for object contour tracking

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3rd International workshop on variational, Geometric, and level set methods in computer vision

作者： Papadakis, N Mémin, E Cao, F IRISA INRIA F-35042 Rennes France

ISBN: (纸本)3540293485

In this paper we describe a new framework for the tracking of closed curves described through implicit surface modeling. The approach proposed here enables a continuous tracking along an image sequence of deformable object contours. Such an approach is formalized through the minimization of a global spatio-temporal continuous cost functional stemming from a Bayesian Maximum a posteriori estimation of a Gaussian probability distribution. The resulting minimization sequence consists in a forward integration of an evolution law followed by a backward integration of an adjoint evolution model, This latter pde include also a term related to the discrepancy between the curve evolution law and a noisy observation of the curve. The efficiency of the approach is demonstrated on image sequences showing deformable objects of different natures.

关键词： Contour measurement

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Heuristically driven front propagation for geodesic paths extraction

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3rd International workshop on variational, Geometric, and level set methods in computer vision

作者： Peyré, G Cohen, L CNRS CMAP UMR 7641 Ecole Polytech F-91128 Palaiseau France Univ Paris 09 CEREMADE CNRS UMR 7534 F-75775 Paris France

ISBN: (纸本)3540293485

In this paper we present a simple modification of the Fast Marching algorithm to speed up the computation using a heuristic. This modification leads to an algorithm that is similar in spirit to the A* algorithm used in artificial intelligence. Using a heuristic allows to extract geodesics from a single source to a single goal very quickly and with a low memory requirement. Any application that needs to compute a lot of geodesic paths can gain benefits from our algorithm. The computational saving is even more important for 3D medical images with tubular structures and for higher dimensional data.

关键词： Heuristic methods

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Nonlinear dynamical shape priors for level set segmentation

Nonlinear dynamical shape priors for level set segmentation

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ieee Conference on computer vision and Pattern Recognition

作者： Cremers, Daniel Univ Bonn Dept Comp Sci D-5300 Bonn Germany

ISBN: (纸本)9781424411795

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： Image segmentation

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A gradient descent procedure for variational dynamic surface problems with constraints

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3rd International workshop on variational, Geometric, and level set methods in computer vision, VLSM 2005

作者： Solem, Jan Erik Overgaard, Niels Chr Applied Mathematics Group School of Technology and Society Malmö University Sweden

ISBN: (纸本)3540293485

Many problems in image analysis and computer vision involving boundaries and regions can be cast in a variational formulation. This means that m-surfaces, e.g. curves and surfaces, are determined as minimizers of functionals using e.g. the variational level set method. In this paper we consider such variational problems with constraints given by functionals. We use the geometric interpretation of gradients for functionals to construct gradient descent evolutions for these constrained problems. The result is a generalization of the standard gradient projection method to an infinite-dimensional level set framework. The method is illustrated with examples and the results are valid for surfaces of any dimension. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Image analysis

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Nonlinear inverse scale space methods for image restoration

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3rd International workshop on variational, Geometric, and level set methods in computer vision

作者： Burger, M Osher, S Xu, JJ Gilboa, G Johannes Kepler Univ Linz Ind Math Inst A-4040 Linz Austria Univ Calif Los Angeles Dept Math Los Angeles CA 90095 USA

ISBN: (纸本)3540293485

In this paper we generalize the iterated refinement method, introduced by the authors in [8], to a time-continuous inverse scale-space formulation. The iterated refinement procedure yields a sequence of convex variational problems, evolving toward the noisy image. The inverse scale space method arises as a limit for a penalization parameter tending to zero, while the number of iteration steps tends to infinity. For the limiting flow, similar properties as for the iterated refinement procedure hold. Specifically, when a discrepancy principle is used as the stopping criterion, the error between the reconstruction and the noise-free image decreases until termination, even if only the noisy image is available and a bound on the variance of the noise is known. The inverse flow is computed directly for one-dimensional signals, yielding high quality restorations. In higher spatial dimensions, we introduce a relaxation technique using two evolution equations. These equations allow accurate, efficient and straightforward implementation.

关键词： Image reconstruction

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Radar-Based Shape and Reflectivity Reconstruction Using Active Surfaces and the level set Method

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ieee TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第3期45卷 3617-3631页

作者： Bignardi, Samuel Sandhu, Romeil Yezzi, Anthony Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

We investigate a multiview shape reconstruction problem based on an active surface model whose geometric evolution is driven by radar measurements acquired at sparse locations. Building on our previous work in the context of variational methods for the reconstruction of a scene conceptualized as the graph of a function, we generalize this inversion approach for a general geometry, now described by an active surface, strongly motivated by prior variational computer vision approaches to multiview stereo reconstruction from camera images. While conceptually similar, use of radar echoes within a variational scheme to drive the active surface evolution requires significant changes in regularization strategies compared to prior image based methodologies for the active surface evolution to work effectively. We describe all of these aspects and how we addressed them. While our long term objective is to develop a framework capable of fusing radar as well as other image based information, in which the active surface becomes an explicit shared reference for data fusion. In this paper, we explore the reconstruction using radar as a single modality, demonstrating that the presented approach can provide reconstructions of quality comparable to those from image based methods showing great potential for further development toward data fusion.

关键词： Image reconstruction Radar Surface reconstruction Radar imaging Shape Three-dimensional displays Radar antennas Active surfaces high frequency inversion level set method noncoherent radar reflectivity estimation shape reconstruction

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Spatio-temporal prior shape constraint for level set segmentation

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5th International workshop on Energy Minimization methods in computer vision and Pattern Recognition

作者： Bailloeul, T Prinet, V Serra, B Marthon, P Chinese Acad Sci Inst Automat LIAMA Beijing 100080 Peoples R China Alcatel Space F-06156 Cannes La Bocca France IRIT LIMA F-31071 Toulouse France

ISBN: (纸本)3540302875

This paper exposes a novel formulation of prior shape constraint incorporation for the level set segmentation of objects from corrupted images. Applicable to variational frameworks, the proposed scheme consists in weighting the prior shape constraint by a function of time and space to overcome local minima issues of the energy functional. Pose paxameters which make the prior shape constraint invariant from global transformations are estimated by the downhill simplex algorithm, which is more tractable and robust than the traditional gradient descent. The proposed scheme is simple, easy to implement and can be generalized to any variational approach incorporating a single prior shape. Results illustrated with different kinds of images demonstrate the efficiency of the method.

关键词： Image segmentation

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Multiview normal field integration using level set methods

Multiview normal field integration using level set methods

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ieee Conference on computer vision and Pattern Recognition

作者： Chang, Ju Yong Lee, Kyoung Mu Lee, Sang Uk Seoul Natl Univ Sch Elect Eng Seoul 151600 South Korea

ISBN: (纸本)9781424411795

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： computer vision

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