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检索条件"任意字段=MIPPR 2007: Pattern Recognition and Computer Vision"

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Fourier Methods for Nonparametric Image Registration

Fourier Methods for Nonparametric Image Registration

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Nathan D. Cahill J. Alison Noble David J. Hawkes Wolfson Medical Vision Laboratory University of Oxford Oxford UK Research and Innovation Carestream Health Inc. Rochester NY USA Centre for Medical Image Computing University College London London UK

Nonparametric image registration algorithms use deformation fields to define nonrigid transformations relating two images. Typically, these algorithms operate by successively solving linear systems of partial differential equations. These PDE systems arise by linearizing the Euler-Lagrange equations associated with the minimization of a functional defined to contain an image similarity term and a regularizer. Iterative linear system solvers can be used to solve the linear PDE systems, but they can be extremely slow. Some faster techniques based on Fourier methods, multigrid methods, and additive operator splitting, exist for solving the linear PDE systems for specific combinations of regularizers and boundary conditions. In this paper, we show that Fourier methods can be employed to quickly solve the linear PDE systems for every combination of standard regularizers (diffusion, curvature, elastic, and fluid) and boundary conditions (Dirichlet, Neumann, and periodic).

关键词： Image registration Boundary conditions Poisson equations Partial differential equations Biomedical imaging Iterative algorithms Linear systems Differential equations Force measurement Standards development

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Spatio-Temporal Markov Random Field for Video Denoising

Spatio-Temporal Markov Random Field for Video Denoising

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Jia Chen Chi-Keung Tang Vision and Graphics Group Hong Kong University of Science and Technology Hong Kong China

This paper presents a novel spatio-temporal Markov random field (MRF) for video denoising. Two main issues are addressed in this paper, namely, the estimation of noise model and the proper use of motion estimation in the denoising process. Unlike previous algorithms which estimate the level of noise, our method learns the full noise distribution nonparametrically which serves as the likelihood model in the MRF. Instead of using deterministic motion estimation to align pixels, we set up a temporal likelihood by combining a probabilistic motion field with the learned noise model. The prior of this MRF is modeled by piece-wise smoothness. The main advantage of the proposed spatio-temporal MRF is that it integrates spatial and temporal information adaptively into a statistical inference framework, where the posteriori is optimized using graph cuts with alpha expansion. We demonstrate the performance of the proposed approach on benchmark data sets and real videos to show the advantages of our algorithm compared with previous single frame and multi-frame algorithms.

关键词： Markov random fields Noise reduction Noise level Motion estimation Inference algorithms Filtering Motion compensation Image restoration Uncertainty Gaussian noise

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lambda-Connectedness Determination for Image Segmentation

lambda-Connectedness Determination for Image Segmentation

引用

Applied Imagery pattern recognition Workshop (AIPR)

作者： Li Chen Department of Computer Science and Information Technology University of District of Columbia Washington D.C. DC USA

ISBN: (纸本)9781424431625

Image segmentation is to separate an image into distinct homogeneous regions belonging to different objects. It is an essential step in image analysis and computer vision. This paper compares some segmentation technologies and attempts to find an automated way to better determine the parameters for image segmentation, especially the connectivity value of lambda in lambda-connected segmentation. Based on the theories on the maximum entropy method and Otsu's minimum variance method, we propose:(1)maximum entropy connectedness determination: a method that uses maximum entropy to determine the best lambda value in lambda-connected segmentation, and (2) minimum variance connectedness determination: a method that uses the principle of minimum variance to determine lambda value. Applying these optimization techniques in real images, the experimental results have shown great promise in the development of the new methods. In the end, we extend the above method to more general case in order to compare it with the famous Mumford-Shah method that uses variational principle and geometric measure.

关键词： Image segmentation Entropy Fuzzy sets computer vision Optimization methods Data mining Image edge detection pattern recognition computer science Information technology

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Joint Priors for Variational Shape and Appearance Modeling

Joint Priors for Variational Shape and Appearance Modeling

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Jeremy D. Jackson Anthony J. Yezzi Stefano Soatto School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Computer Science Department University of California Los Angeles Los Angeles CA USA

We are interested in modeling the variability of different images of the same scene, or class of objects, obtained by changing the imaging conditions, for instance the viewpoint or the illumination. Understanding of such a variability is key to reconstruction of objects despite changes in their appearance (e.g. due to non-Lambertian reflection), or to recognizing classes of objects (e.g. cars), or individual objects seen from different vantage points. We propose a model that can account for changes in shape or viewpoint, appearance, and also occlusions of line of sight. We learn a prior model of each factor (shape, motion and appearance) from a collection of samples using principal component analysis, akin a generalization of "active appearance models" to dense domains affected by occlusions. The ultimate goal of this work is stereo reconstruction in 3D, but first we have developed the first stage in this approach by addressing the simpler case of 2D shape/radiance detection in single images. We illustrate our model on a collection of images of different cars and show how the learned prior can be used to improve segmentation and 3D stereo reconstruction.

关键词： Layout Lighting Principal component analysis Image reconstruction Deformable models Active shape model Stereo image processing computer vision computer science Optical reflection

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Improved Video Registration using Non-Distinctive Local Image Features

Improved Video Registration using Non-Distinctive Local Imag...

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Conference on computer vision and pattern recognition (CVPR)

作者： Robin Hess Alan Fern School of Electrical Engineering and Computer Science Oregon State University Corvallis OR USA

The task of registering video frames with a static model is a common problem in many computer vision domains. The standard approach to registration involves finding point correspondences between the video and the model and using those correspondences to numerically determine registration transforms. Current methods locate video-to-model point correspondences by assembling a set of reference images to represent the model and then detecting and matching invariant local image features between the video frames and the set of reference images. These methods work well when all video frames can be guaranteed to contain a sufficient number of distinctive visual features. However, as we demonstrate, these methods are prone to severe misregistration errors in domains where many video frames lack distinctive image features. To overcome these errors, we introduce a concept of local distinctiveness which allows us to find model matches for nearly all video features, regardless of their distinctiveness on a global scale. We present results from the American football domain-where many video frames lack distinctive image features-which show a drastic improvement in registration accuracy over current methods. In addition, we introduce a simple, empirical stability test that allows our method to be fully automated. Finally, we present a registration dataset from the American football domain we hope can be used as a benchmarking tool for registration methods.

关键词： computer vision Robustness Assembly computer science Stability Automatic testing Benchmark testing Cameras Video sequences Robot localization

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A Fuzzy-interval based approach for explicit graph embedding

A Fuzzy-interval based approach for explicit graph embedding

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20th International Conference on pattern recognition, ICPR 2010

作者： Luqman, Muhammad Muzzamil Lladós, Josep Ramel, Jean-Yves Brouard, Thierry Laboratoire d’Informatique Université François Rabelais de Tours France Computer Vision Center Universitat Autónoma de Barcelona Spain

ISBN: (纸本)9783642177101

We present a new method for explicit graph embedding. Our algorithm extracts a feature vector for an undirected attributed graph. The proposed feature vector encodes details about the number of nodes, number of edges, node degrees, the attributes of nodes and the attributes of edges in the graph. The first two features are for the number of nodes and the number of edges. These are followed by w features for node degrees, m features for k node attributes and n features for l edge attributes — which represent the distribution of node degrees, node attribute values and edge attribute values, and are obtained by defining (in an unsupervised fashion), fuzzy-intervals over the list of node degrees, node attributes and edge attributes. Experimental results are provided for sample data of ICPR2010 contest GEPR. © 2010, Springer-Verlag Berlin Heidelberg.

关键词： Artificial intelligence

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Trace Ratio vs. Ratio Trace for Dimensionality Reduction

Trace Ratio vs. Ratio Trace for Dimensionality Reduction

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Conference on computer vision and pattern recognition (CVPR)

作者： Huan Wang Shuicheng Yan Dong Xu Xiaoou Tang Thomas Huang IE Chinese University of Hong Kong Hong Kong China University of Illinois Urbana-Champaign USA EE Columbia University New York NY USA Microsoft Research Asia Beijing China

A large family of algorithms for dimensionality reduction end with solving a Trace Ratio problem in the form of arg max W Tr(W T S P W)/Tr(WT S I W) 1 , which is generally transformed into the corresponding Ratio Trace form arg max W Tr[ (W T S I W) -1 (WTS P W) ] for obtaining a closed-form but inexact solution. In this work, an efficient iterative procedure is presented to directly solve the Trace Ratio problem. In each step, a Trace Difference problem arg max W Tr [W T (S P - lambdaS I ) W] is solved with lambda being the trace ratio value computed from the previous step. Convergence of the projection matrix W, as well as the global optimum of the trace ratio value lambda, are proven based on point-to-set map theories. In addition, this procedure is further extended for solving trace ratio problems with more general constraint W T CW=I and providing exact solutions for kernel-based subspace learning problems. Extensive experiments on faces and UCI data demonstrate the high convergence speed of the proposed solution, as well as its superiority in classification capability over corresponding solutions to the ratio trace problem.

关键词： Eigenvalues and eigenfunctions Asia Subspace constraints Closed-form solution Uncertainty Matrix decomposition computer vision pattern recognition

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Medical Imaging and Informatics 1

引用

丛书名： Lecture Notes in computer Science

1000年

ISBN: (数字)9783540794905

ISBN: (纸本)9783540794899

This series constitutes a collection of selected papers presented at the International Conference on Medical Imaging and Informatics (MIMI2007), held during August 14–16, in Beijing, China. The conference, the second of its kind, was funded by the European Commission (EC) under the Asia IT&C programme and was co-organized by Middlesex University, UK and Capital University of Medical Sciences, China. The aim of the conference was to initiate links between Asia and Europe and to exchange research results and ideas in the field of medical imaging. A wide range of topics were covered during the conference that attracted an audience from 18 countries/regions (Canada, China, Finland, Greece, Hong Kong, Italy, Japan, Korea, Libya, Macao, Malaysia, Norway, Pakistan, Singapore, Switzerland, Taiwan, the United Kingdom, and the USA). From about 110 submitted papers, 50 papers were selected for oral presentations, and 20 for posters. Six key-note speeches were delivered during the conference presenting the state of the art of medical informatics. Two workshops were also organized covering the topics of “Legal, Ethical and Social Issues in Medical Imaging” and “Informatics” and “computer-Aided Diagnosis (CAD),” respectively.

关键词： Image Processing and computer vision pattern recognition Artificial Intelligence computer Graphics Health Informatics Imaging / Radiology

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The Tower of Knowledge Scheme for Learning in computer vision

The Tower of Knowledge Scheme for Learning in Computer Visio...

引用

Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Maria Petrou Mai Xu Communications and Signal Processing Group Electrical and Electronic Engineering Department Imperial College London London UK

A scheme, named tower of knowledge (ToK), is proposed for interpreting 3D scenes. The ToK encapsulates causal dependencies between object appearance and functionality. We demonstrate it by labelling the components of ... 详细信息

ISBN: (纸本)9781424431618;9780769530673

关键词： Poles and towers computer vision Layout Humans Ovens Bayesian methods Utility theory Fires Object recognition Digital images

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Learning Generative Models via Discriminative Approaches

Learning Generative Models via Discriminative Approaches

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Zhuowen Tu Lab of Neuro Imaging UCLA USA

Generative model learning is one of the key problems in machine learning and computer vision. Currently the use of generative models is limited due to the difficulty in effectively learning them. A new learning framework is proposed in this paper which progressively learns a target generative distribution through discriminative approaches. This framework provides many interesting aspects to the literature. From the generative model side: (1) A reference distribution is used to assist the learning process, which removes the need for a sampling processes in the early stages. (2) The classification power of discriminative approaches, e.g. boosting, is directly utilized. (3) The ability to select/explore features from a large candidate pool allows us to make nearly no assumptions about the training data. From the discriminative model side: (1) This framework improves the modeling capability of discriminative models. (2) It can start with source training data only and gradually "invent" negative samples. (3) We show how sampling schemes can be introduced to discriminative models. (4) The learning procedure helps to tighten the decision boundaries for classification, and therefore, improves robustness. In this paper, we show a variety of applications including texture modeling and classification, non-photorealistic rendering, learning image statistics/denoising, and face modeling. The framework handles both homogeneous patterns, e.g. textures, and inhomogeneous patterns, e.g. faces, with nearly an identical parameter setting for all the tasks in the learning stage.

关键词： Machine learning Training data computer vision Power generation Sampling methods Boosting Image sampling Robustness Rendering (computer graphics) Statistics

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