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IEEE Conference on computer vision and Pattern Recognition

作者： Amos Sironi Vincent Lepetit Pascal Fua Computer Vision Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) Institute for Computer Graphics and Vision Graz University of Technology

ISBN: (纸本)9781479951192

We propose a robust and accurate method to extract the centerlines and scale of tubular structures in 2D images and 3D volumes. Existing techniques rely either on filters designed to respond to ideal cylindrical structures, which lose accuracy when the linear structures become very irregular, or on classification, which is inaccurate because locations on centerlines and locations immediately next to them are extremely difficult to distinguish. We solve this problem by reformulating centerline detection in terms of a regression problem. We first train regressors to return the distances to the closest centerline in scale-space, and we apply them to the input images or volumes. The centerlines and the corresponding scale then correspond to the regressors local maxima, which can be easily identified. We show that our method outperforms state-of-the-art techniques for various 2D and 3D datasets.

关键词： Three-dimensional displays Training Transforms Image segmentation Vectors Feature extraction Robustness

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Robust 3D Tracking with Descriptor Fields

Robust 3D Tracking with Descriptor Fields

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IEEE Conference on computer vision and Pattern Recognition

作者： Alberto Crivellaro Vincent Lepetit Computer Vision Laboratory Ecole Polytechnique Fédérale de Lausanne (EPFL) Institute for Computer Graphics Vision Graz University of Technology

We introduce a method that can register challenging images from specular and poorly textured 3D environments, on which previous approaches fail. We assume that a small set of reference images of the environment and a partial 3D model are available. Like previous approaches, we register the input images by aligning them with one of the reference images using the 3D information. However, these approaches typically rely on the pixel intensities for the alignment, which is prone to fail in presence of specularities or in absence of texture. Our main contribution is an efficient novel local descriptor that we use to describe each image location. We show that we can rely on this descriptor in place of the intensities to significantly improve the alignment robustness at a minor increase of the computational cost, and we analyze the reasons behind the success of our descriptor.

关键词： Three-dimensional displays Robustness Cameras Solid modeling Optimization Registers Lighting

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Single breathhold, three-dimensional measurement of left atrial volume and function using sparse CINE CMR imaging with iterative reconstruction

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Journal of Cardiovascular Magnetic Resonance 2015年第SUPPL 1期17卷 Q35-Q35页

作者： Pierre Monney Orestis Vardoulis Davide Piccini Amit Bermano Amir Vaxman Craig Gotsman Janine Schwitter Michael O Zenge Michaela Schmidt Mariappan S Nadar Matthias Stuber Nikolaos Stergiopulos Juerg Schwitter Center for Cardiac Magnetic Resonance Cardiology University Hospital Lausanne (CHUV) Lausanne Switzerland Laboratory of Hemodynamics and Cardiovascular Technology Swiss Federal Institute of Technology Lausanne Switzerland Center for Biomedical Imaging (CIBM) University of Lausanne Lausanne Switzerland Advanced Clinical Imaging Technology Siemens Healthcare Lausanne Switzerland Center for Graphics and Geometric Computing Technion Haifa Israel Geometric Modelling and Industrial Geometry Vienna University of Technology Vienna Austria University of Fribourg Fribourg Switzerland Healthcare sector Siemens AG Erlangen Germany Imaging and Computer Vision Siemens Corporation Princeton NJ USA Radiology University Hospital Lausanne Lausanne Switzerland

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A Probabilistic Framework for Multitarget Tracking with Mutual Occlusions

A Probabilistic Framework for Multitarget Tracking with Mutu...

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IEEE Conference on computer vision and Pattern Recognition

作者： Menglong Yang Yiguang Liu Longyin Wen Zhisheng You Stan Z. Li Key Laboratory of Fundamental Synthetic Vision Graphics and Image for National Defense School of Aeronautics and Astronautics & Computer Science Sichuan University Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781479951192

Mutual occlusions among targets can cause track loss or target position deviation, because the observation likelihood of an occluded target may vanish even when we have the estimated location of the target. This paper presents a novel probability framework for multitarget tracking with mutual occlusions. The primary contribution of this work is the introduction of a vectorial occlusion variable as part of the solution. The occlusion variable describes occlusion states of the targets. This forms the basis of the proposed probability framework, with the following further contributions: 1) Likelihood: A new observation likelihood model is presented, in which the likelihood of an occluded target is computed by referring to both of the occluded and occluding targets. 2) Priori: Markov random field (MRF) is used to model the occlusion priori such that less likely "circular" or "cascading" types of occlusions have lower priori probabilities. Both the occlusion priori and the motion priori take into consideration the state of occlusion. 3) Optimization: A realtime RJMCMC-based algorithm with a new move type called "occlusion state update" ispresented. Experimental results show that the proposed framework can handle occlusions well, even including long-duration full occlusions, which may cause tracking failures in the traditional methods.

关键词： Target tracking Probabilistic logic Approximation algorithms Cameras Proposals Computational modeling

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Head pose estimation with improved random regression forests

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2012 International Conference on Service-Oriented Computing, ICSOC 2012

作者： Zhu, Ronghang Sang, Gaoli Cai, Ying You, Jian Zhao, Qijun National Key Laboratory of Fundamental Science on Synthetic Vision Institute of Image and Graphics School of Computer Science China

ISBN: (纸本)9783319029603

Head pose estimation is an important step in many face related applications. In this paper, we propose to use random regression forests to estimate head poses in 2D face images. Given a 2D face image, Gabor filters are first applied to extract raw high-dimensional features. Linear discriminant analysis (LDA) is then used to reduce the feature dimension. Random regression forests are constructed in the low dimensional feature space. Unlike traditional random forests, when generating tree predictors in the forests we weight the features according to the eigenvalues associated with their corresponding LDA axes. The proposed method has been evaluated on a set of 2D face images synthesized from the BU-3DFE database and on the CMU-PIE database. The experimental results demonstrate the effectiveness of the proposed method. © Springer International Publishing 2013.

关键词： Regression analysis

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Comparison of appearance-based and geometry-based bubble detectors

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8671卷 610-617页

作者： Strokina, Nataliya Juránek, Roman Eerola, Tuomas Lensu, Lasse Zemčik, Pavel Kälviäinen, Heikki Tampere University of Technology Department of Signal Processing P.O. Box 527 Tampere33101 Finland Brno University of Technology Department of Computer Graphics and Multimedia Brno Czech Republic Lappeenranta University of Technology Machine Vision and Pattern Recognition Laboratory P.O. Box 20 Lappeenranta53851 Finland

Bubble detection is a complicated tasks since varying lighting conditions changes considerably the appearance of bubbles in liquid. The two common techniques to detect circular objects such as bubbles, the geometry-based and appearance-based approaches, have their advantages and weaknesses. The geometry-based methods often fail to detect small blob-like bubbles that do not match the used geometrical model, and appearance-based approaches are vulnerable to appearance changes caused by, e.g., illumination. In this paper, we compare a geometry-based concentric circular arrangements (CCA) and appearance-based sliding window methods as well as their combinations in terms of bubble detection, gas volume computation, and size distribution estimation. The best bubble detection performance was achieved with the sliding window method whereas the most precise volume estimate was produced by the CCA method. The combination of the two approaches gave only a minor advantage compared to the base methods. © Springer International Publishing Switzerland 2014.

关键词： Geometry

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Fast anatomical structure localization using top-down image patch regression

Fast anatomical structure localization using top-down image ...

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15th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2012

作者： Donner, René Menze, Bjoern H. Bischof, Horst Langs, Georg Computational Image Analysis and Radiology Lab. Department of Radiology Medical University Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Austria CSAIL MIT Cambridge MA United States Asclepios Project INRIA Sophia-Antipolis France Computer Vision Laboratory ETH Zurich Switzerland

ISBN: (纸本)9783642366192

Fully automatic localization of anatomical structures in 2D and 3D radiological data sets is important in both computer aided diagnosis, and the rapid automatic processing of large amounts of data. We present a simple, accurate and fast approach with low computational complexity to find anatomical landmarks, based on a multi-scale regression codebook of informative image patches and encoded landmark contexts. From a set of annotated training volumes the method captures the appearance of landmarks over several scales together with relative positions of neighboring landmarks and a spatial distribution model. During multi-scale search in a target volume, starting from the coarsest level, each landmark model predicts all landmark positions it has encoded, with the median of all predictions yielding the final prediction for each scale. We present results on two challenging data sets (hand radiographs and hand CTs), where our method achieves comparable accuracy to the state of the art with substantially improved run-time. © 2013 Springer-Verlag.

关键词： computer aided diagnosis

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Optimization of ultrasonic imaging using persistence and filter technology

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2013 2nd International Conference on Materials Science and Technology, ICMST 2013

作者： Guo, De Quan Yang, Hong Yu Zhang, Cong Yao Liu, Dong C. Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Sichuan Province China Department of Machinery Engineering Neijiang Vocational and Technical College Neijiang China College of Computer Science Sichuan University Sichuan Province China

ISBN: (纸本)9783037857137

B-mode ultrasonic images are often pervaded by the electronics noise and speckle artifact, which may make the interpretation of medical images difficult. In this paper, a legible method for ultrasonic image is constructed and it can be effective to suppress the noise and improve the images quality. Dynamic persistence technology based on temporal multi-frames is utilized to smooth the electronics noise and even lower speckle artifact. To reduce computing complexity, a sum table method and conjugate direction search approaches are applied to speed up estimation of motion vector. The guided image filter strategy is used to enhance the contrast and detail resolution for the processed image by persistence. Experimental results show that the proposed method is effective to smooth noise and enhance contrast of ultrasound image in vivo. © (2013) Trans Tech Publications, Switzerland.

关键词： Ultrasonic imaging

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Optimization of Ultrasonic Imaging Using Persistence and Filter Technology

Optimization of Ultrasonic Imaging Using Persistence and Fil...

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2013 2nd International Conference on Materials Science and Technology(ICMST 2013)

作者： Dequan Guo Hongyu Yang Congyao Zhang Dong C.Liu Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Department of Machinery Engineering Neijiang Vocational and Technical College College of Computer Science Sichuan University

关键词： noise contrast sum table conjugate direction search guided image filter

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Motion compensation and histogram aided methods for persistence in ultrasonic images optimization

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Journal of Computational Information Systems 2013年第13期9卷 5325-5335页

作者： Guo, Dequan Yang, Hongyu Yin, Hao Shao, Dangguo Liu, Dong C. Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu 610064 China Department of Machinery Engineering Neijiang Vocational and Technical College Neijiang 641100 China School of Computer Science and Engineering Sichuan University Chengdu 610064 China

Ultrasound images are often degraded by electronics noise from circuit system and speckle noise. In the paper, a new persistence approach is proposed. The motion vectors are calculated by maximum-fast-normalized-cross-correlation (MFNCC) with sum table and conjugate direction (CD) search approaches. These improvements can be helpful to increase accuracy and accelerate the motion estimate. The coefficient of the persistence function is determined by the correlation, which is based on the discrepancy histogram between the corresponding points of successive images quantitatively after motion estimation rectified. The signal-to-noise-ratio (SNR) and four texture features are quantitatively verified the effectiveness in kidney images. Experimental results demonstrate that the proposed method works very well in the vivo ultrasound image with noise reduction, smooth and removal shadow of tissue edge in visual appearance. © 2013 by Binary Information Press.

关键词： Motion compensation

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