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ieee workshop on Applications of computer vision (WACV)

作者： Xiwen Chen Huayu Li Peijie Qiu Wenhui Zhu Rahul Amin Abolfazl Razi Clemson University University of Arizona Washington University in St. Louis Arizona State University MIT Lincoln Laboratory

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Selecting representative samples plays an indispensable role in many machine learning and computer vision applications under limited resources (e.g., limited communication bandwidth and computational power). Determinantal Point Process (DPP) is a widely used method for selecting the most diverse representative samples that can summarize a dataset. However, its adaptability to different tasks remains an open challenge, as it is challenging for DPP to perform task-specific tuning. In contrast, Rate-Distortion (RD) theory provides a way to measure task-specific diversity. However, optimizing RD for a data selection problem remains challenging because the quantity that needs to be optimized is the index set of the selected samples. To tackle these challenges, we first draw an inherent relationship between DPP and RD theory. Our theoretical derivation paves the way to take advantage of both RD and DPP for a task-specific data selection. To this end, we propose a novel method for task-specific data selection for multi-level classification tasks, named RD-DPP. Empirical studies on seven different datasets using five benchmark models demonstrate the effectiveness of the proposed RD-DPP method. Our method also outperforms recent strong competing methods, while exhibiting high generalizability to a variety of learning tasks. The source code is available on https://***/xiwencl/RD-DPP 1 .

关键词： Training computer vision Source coding Rate-distortion Data visualization Machine learning Bandwidth Benchmark testing Indexes Tuning

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A variational approach for the segmentation of the left ventricle in cardiac image analysis

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INTERNATIONAL JOURNAL OF computer vision 2002年第3期50卷 345-362页

作者： Paragios, N Siemens Corp Res Real Time Vis & Modeling Dept Princeton NJ 08540 USA

In this paper we propose a level set method to segment MR cardiac images. Our approach is based on a coupled propagation of two cardiac contours and integrates visual information with anatomical constraints. The visual information is expressed through a gradient vector flow-based boundary component and a region term that aims at best separating the cardiac contours/regions according to their global intensity properties. In order to deal with misleading visual support, an anatomical constraint is considered that couples the propagation of the cardiac contours according to their relative distance. The resulting motion equations are implemented using a level set approach and a fast and stable numerical approximation scheme, the Additive Operator Splitting. Encouraging experimental results are provided using real data.

关键词： left ventricle medical image segmentation gradient vector flow geodesic active regions level set methods

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Diffusion snakes:: Introducing statistical shape knowledge into the Mumford-Shah functional

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INTERNATIONAL JOURNAL OF computer vision 2002年第3期50卷 295-313页

作者： Cremers, D Tischhäuser, F Weickert, J Schnörr, C Univ Mannheim Dept Math & Comp Sci Comp Vis Graph & Pattern Recognit Grp D-68131 Mannheim Germany

We present a modification of the Mumford-Shah functional and its cartoon limit which facilitates the incorporation of a statistical prior on the shape of the segmenting contour. By minimizing a single energy functional, we obtain a segmentation process which maximizes both the grey value homogeneity in the separated regions and the similarity of the contour with respect to a set of training shapes. We propose a closed-form, parameter-free solution for incorporating invariance with respect to similarity transformations in the variational framework. We show segmentation results on artificial and real-world images with and without prior shape information. In the cases of noise, occlusion or strongly cluttered background the shape prior significantly improves segmentation. Finally we compare our results to those obtained by a level set implementation of geodesic active contours.

关键词： image segmentation shape recognition statistical learning variational methods diffusion snake geodesic active contours

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On affine invariance in the Beltrami framework for vision

On affine invariance in the Beltrami framework for vision

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

作者： Sochen, NA Tel Aviv Univ Dept Appl Math IL-69978 Tel Aviv Israel

ISBN: (纸本)076951278X

We use the geometric Beltrami framework to incorporate and explain some of the known invariant flows e.g. the equi-affine invariant flow. It is also demonstrated that the tint, concepts put forward in this framework enable us to construct new invariant flows for the case where the codimension is greater than one e.g. for color images and video.

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

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INTERNATIONAL JOURNAL OF computer vision 2002年第3期50卷 253-270页

作者： Ye, JC Philips Res USA Briarcliff Manor NY 10510 USA Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA

We address an ill-posed inverse problem of image estimation from sparse samples of its Fourier transform. 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, thus avoiding the re-initialization steps in conventional level set approaches.

关键词： inverse problems Fourier imaging medical imaging partial-data level-set geometry-driven diffusion

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Cortex segmentation - A fast variational geometric approach

Cortex segmentation - A fast variational geometric approach

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

作者： Goldenberg, R Kimmel, R Rivlin, E Rudzsky, M Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

ISBN: (纸本)076951278X

An automatic cortical gray matter segmentation from a three-dimensional brain images (MR or CT) is a well known problem in medical image processing. In this paper we formulate it as geometric variational problem for propagation of two coupled bounding surfaces. An efficient numerical scheme is used to implement the geodesic active surface model. Experimental results of cortex segmentation on real three-dimensional MR data are provided.

关键词： Image segmentation

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Total Variation minimization by the Fast level sets Transform

Total Variation minimization by the Fast Level Sets Transfor...

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

作者： Dibos, F Koepfler, G Univ Paris 09 CEREMADE CNRS 7534 UMR F-75775 Paris France

ISBN: (纸本)076951278X

The minimization of the Total Variation is an important toot of image processing. A lot of authors have addressed the problem and developed algorithms for image denoising. In a previous paper we gave an alternative approach of the Total Variation minimization problem based on the Coarea formula. The aim of this paper is to present a new efficient algorithm for the Coarea formula approach, based on the Fast level sets Transform.

关键词： Image denoising

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On smoothness measures of active contours and surfaces

On smoothness measures of active contours and surfaces

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

作者： Delingette, H INRIA Projet Epidaure F-06902 Sophia Antipolis France

ISBN: (纸本)076951278X

In this paper we propose to study different smoothness measures of planar contours or surfaces. We first define a smoothness measure as a functional that follows three types of invariance : invariance to changes of contour parameterization, invariance to contour rotations and translations and invariance to the contour sizes. We then introduce different smoothness measures that can be classified into local or global functionals but also that can be of geometric or algebraic nature. We finally discuss their implementation by observing the advantages and disadvantages of explicit and implicit Contour representations.

关键词： Numerical methods

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Diffusion-snakes:: Combining statistical shape knowledge and image information in a variational framework

Diffusion-snakes:: Combining statistical shape knowledge and...

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

作者： Cremers, D Schnörr, C Weickert, J Univ Mannheim Dept Math & Comp Sci Comp Vis Graph & Pattern Recognit Grp D-68131 Mannheim Germany

ISBN: (纸本)076951278X

We present a modification of the Mumford-Shah Junctional and its cartoon limit which allows the incorporation of statistical shape knowledge in a single energy functional. We show segmentation results on artificial and real-world images with and without prior shape information. In the case of occlusion and strongly cluttered background the shape prior significantly improves segmentation. Finally we compare our results to those obtained by, a level-set implementation of geodesic active contours.

关键词： image segmentation shape recognition statistical learning variational methods diffusion geodesic active contours diffusion-snake

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Fast surface reconstruction using the level set method

Fast surface reconstruction using the level set method

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

作者： Zhao, HK Osher, S Fedkiw, R Univ Calif Irvine Dept Math Irvine CA 92697 USA

ISBN: (纸本)076951278X

In this paper we describe new formulations and develop fast algorithms for implicit surface reconstruction based on variational and partial differential equation (PDE) methods. In particular we use the level set method and fast sweeping and tagging methods to reconstruct surfaces from scattered data set. The data set might consist of points, curves and/or surface patches. A weighted minimal surface-like model is constructed and its variational level set formulation is implemented with optimal efficiency. The reconstructed surface is smoother than piecewise linear and has a natural scaling in the regularization that allows varying flexibility according to the local sampling density. As is usual with the level set method we can handle complicated topology and deformations, as well as noisy or highly non-uniform data sets easily. The method is based on a simple rectangular arid, although adaptive and triangular grids are also possible. Some consequences, such as hole filling capability, are demonstrated, as well as the viability and convergence of our new fast tagging algorithm.

关键词： implicit surface partial differential equations variational formulation convection minimal surface

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