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11th International Conference on computer Graphics Theory and Application, GRAPP 2016;Part of the 11th Joint Conference on computer vision, Imaging and computer Graphics Theory and Applications, VISIGRAPP 2016

作者： Cao, Van-Toan Tran, Trung-Thien Laurendeau, Denis Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Laval University 1065 Avenue of Medicine QuebecQCG1V 0A6 Canada

ISBN: (纸本)9789897581755

In this paper we present a method for non-rigid registration of meshes. The method aligns two surfaces of deformable objects by automatically separating the deformation into a single global transformation and other local deformations. The local deformations are found by applying the deformation model proposed by Sumner (Sumner et al., 2007) that previous methods have used to register two surfaces. However, we specify a rigid transformation for each node of the deformation graph using a rotation matrix and a translation vector. With this model, unit quaternions of rotation matrices are paramterized using an homeomorphic relation between the 4D unit sphere and the 3D projective space. Therefore, the number of unknowns is reduced by half compared to the original models based on affine transformations and the optimization process is less complex. We demonstrate the efficiency of the proposed method by aligning the surfaces of data sets without any prior knowledge and assumptions about the deformation between the two surfaces. © Copyright 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： Deformation

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Micro-Nano Fabrication: Fabrication of Sub-Micrometer-Sized MoS2Thin-Film Transistor by Phase Mode AFM Lithography (Small 49/2018)

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Small 2018年第49期14卷

作者： Lianqing Liu Jialin Shi Meng Li Peng Yu Tie Yang Guangyong Li State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang 110016 China University of Chinese Academy of Science Beijing 100049 China Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA 15213 USA

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Optoacoustic tomographic imaging of myelinated structures in rodent brains

Optoacoustic tomographic imaging of myelinated structures in...

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Optics and the Brain, BRAIN 2017

作者： Lockwood, Joshua Ringuette, Dene Zhou, Xun Yucel, Yeni Levi, Ofer The Institute of Biomaterials and Biomedical Engineering University of Toronto 164 College Street TorontoONM5S 3G9 Canada Department of Ophthalmology and Vision Sciences Laboratory Medicine and Pathobiology University of Toronto Keenan Research Centre for Biomedical Science 209 Victoria St TorontoONM5B 1T8 Canada The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto 164 College Street TorontoONM5S 3G9 Canada

Myelinated structures in rodent brains were identified using multispectral optoacoustic tomography;exploiting the 930 nm lipid absorbance maximum. Results support optoacoustic imaging as an inventigative tool for rode... 详细信息

ISBN: (纸本)9781943580255

关键词： Photoacoustic effect

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Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions

arXiv

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arXiv 2018年

作者： Maier-Hein, Lena Eisenmann, Matthias Reinke, Annika Onogur, Sinan Stankovic, Marko Scholz, Patrick Arbel, Tal Bogunovic, Hrvoje Bradley, Andrew P. Carass, Aaron Carolin Feldmann Frangi, Alejandro F. Ful, Peter M. van Ginneken, Bram Hanbury, Allan Honauer, Katrin Kozubek, Michal Landman, Bennett A. Marz, Keno Maier, Oskar Maier-Hein, Klaus Menze, Bjoern H. Müller, Henning Neher, Peter F. Niessen, Wiro Rajpoot, Nasir Sharp, Gregory C. Sirinukunwattana, Korsuk Speidel, Stefanie Stock, Christian Stoyanov, Danail Taha, Abdel Aziz van der Sommen, Fons Wang, Ching-Wei Weber, Marc-André Zheng, Guoyan Jannin, Pierre Kopp-Schneider, Annette Heidelberg Germany Centre for Intelligent Machines McGill University MontrealQC Canada Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna Vienna Austria Science and Engineering Faculty Queensland University of Technology BrisbaneQLD Australia Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore United States CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine University of Sheffield Sheffield United Kingdom Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center Nijmegen Netherlands Institute of Information Systems Engineering TU Wien Vienna Austria Heidelberg University Heidelberg Germany Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Electrical Engineering Vanderbilt University NashvilleTN United States Institute of Medical Informatics Universität zu Lübeck Lübeck Germany Heidelberg Germany Institute for Advanced Studies Department of Informatics Technical University of Munich Munich Germany Information System Institute HES-SO Sierre Switzerland Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC Rotterdam Netherlands Department of Computer Science University of Warwick Coventry United Kingdom Department of Radiation Oncology Massachusetts General Hospital BostonMA United States Institute of Biomedical Engineering University of Oxford Oxford United Kingdom National Center for Tumor Diseases Dresden Dresden Germany Heidelberg Germany Department of Computer Science University College London London United Kingdom Data Science Studio Research Studios Austria FG Vienna Austria Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands AIExplore NTUST Center of Computer Vision and Medical Imaging Graduate Institute of Biomedical Engineering National Ta

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future. Copyright © 2018, The Authors. All rights reserved.

关键词： Quality control

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A Probabilistic Framework for Color-Based Point Set Registration

A Probabilistic Framework for Color-Based Point Set Registra...

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IEEE Conference on computer vision and Pattern Recognition

作者： Martin Danelljan Giulia Meneghetti Fahad Shahbaz Khan Michael Felsberg Computer Vision Laboratory Department of Electrical Engineering Linkoping University

ISBN: (纸本)9781467388528

In recent years, sensors capable of measuring both color and depth information have become increasingly popular. Despite the abundance of colored point set data, state-of-the-art probabilistic registration techniques ignore the available color information. In this paper, we propose a probabilistic point set registration framework that exploits available color information associated with the points. Our method is based on a model of the joint distribution of 3D-point observations and their color information. The proposed model captures discriminative color information, while being computationally efficient. We derive an EM algorithm for jointly estimating the model parameters and the relative transformations. Comprehensive experiments are performed on the Stanford Lounge dataset, captured by an RGB-D camera, and two point sets captured by a Lidar sensor. Our results demonstrate a significant gain in robustness and accuracy when incorporating color information. On the Stanford Lounge dataset, our approach achieves a relative reduction of the failure rate by 78% compared to the baseline. Furthermore, our proposed model outperforms standard strategies for combining color and 3D-point information, leading to state-of-the-art results.

关键词： chromatic information Set of points expectation-maximisation algorithm failure rates REST ROOM Registration model parameters

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Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking

Adaptive Decontamination of the Training Set: A Unified Form...

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IEEE Conference on computer vision and Pattern Recognition

作者： Martin Danelljan Gustav Hager Fahad Shahbaz Khan Michael Felsberg Computer Vision Laboratory Department of Electrical Engineering Linkoping University

ISBN: (纸本)9781467388528

Tracking-by-detection methods have demonstrated competitive performance in recent years. In these approaches, the tracking model heavily relies on the quality of the training set. Due to the limited amount of labeled training data, additional samples need to be extracted and labeled by the tracker itself. This often leads to the inclusion of corrupted training samples, due to occlusions, misalignments and other perturbations. Existing tracking-by-detection methods either ignore this problem, or employ a separate component for managing the training set. We propose a novel generic approach for alleviating the problem of corrupted training samples in tracking-by-detection frameworks. Our approach dynamically manages the training set by estimating the quality of the samples. Contrary to existing approaches, we propose a unified formulation by minimizing a single loss over both the target appearance model and the sample quality weights. The joint formulation enables corrupted samples to be down-weighted while increasing the impact of correct ones. Experiments are performed on three benchmarks: OTB-2015 with 100 videos, VOT-2015 with 60 videos, and Temple-Color with 128 videos. On the OTB-2015, our unified formulation significantly improves the baseline, with a gain of 3.8% in mean overlap precision. Finally, our method achieves state-of-the-art results on all three datasets.

关键词： Functional training Training Videotapes visual tracking inspection methods Set (Psychology) decontamination

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Predicting when saliency maps are accurate and eye fixations consistent

Predicting when saliency maps are accurate and eye fixations...

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2016 IEEE Conference on computer vision and Pattern Recognition, CVPR 2016

作者： Volokitin, Anna Gygli, Michael Boix, Xavier Computer Vision Laboratory ETH Zurich Switzerland Department of Electrical and Computer Engineering National University of Singapore Singapore CBMM Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9781467388511

Many computational models of visual attention use image features and machine learning techniques to predict eye fixation locations as saliency maps. Recently, the success of Deep Convolutional Neural Networks (DCNNs) for object recognition has opened a new avenue for computational models of visual attention due to the tight link between visual attention and object recognition. In this paper, we show that using features from DCNNs for object recognition we can make predictions that enrich the information provided by saliency models. Namely, we can estimate the reliability of a saliency model from the raw image, which serves as a meta-saliency measure that may be used to select the best saliency algorithm for an image. Analogously, the consistency of the eye fixations among subjects, i.e. the agreement between the eye fixation locations of different subjects, can also be predicted and used by a designer to assess whether subjects reach a consensus about salient image locations.

关键词： Behavioral research

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Optical coherence tomography in ophthalmology: Current applications and future directions

Optical coherence tomography in ophthalmology: Current appli...

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Frontiers in Optics, FiO 2017

作者： Sampson, Danuta M. Chew, Avenell L. Alonso-Caneiro, David Gong, Peijun Karnowski, Karol Mackey, David A. Chen, Fred K. Centre for Ophthalmology and Visual Science University of Western Australia 35 Stirling Highway Crawley PerthWA6009 Australia Lions Eye Institute 2 Verdun Street Nedlands PerthWA6009 Australia Contact Lens and Visual Optics Laboratory School of Optometry and Vision Science Queensland University of Technology Victoria Park Road Kelvin Grove BrisbaneQLD4059 Australia Optical+Biomedical Engineering Laboratory School of Electrical Electronic and Computer Engineering The University of Western Australia 35 Stirling Highway PerthWA6009 Australia Department of Ophthalmology Royal Perth Hospital 197 Wellington St PerthWA6000 Australia

Optical coherence tomography (OCT) has been the most revolutionary diagnostic tool in eye care over the past 20 years. This paper reviews the current status and future directions of OCT in ophthalmic application. ... 详细信息

ISBN: (纸本)9781943580330

关键词： Optical tomography

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Integrated mechatronics design of flexure joint and controller in dual-drive gantry: a constrained H2 optimization approach

Integrated mechatronics design of flexure joint and controll...

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2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017

作者： Ma, Jun Chen, Si-Lu Kamaldin, Nazir Teo, Chek Sing Tay, Arthur Mamun, Abdullah Al Tan, Kok Kiong Department of Electrical and Computer Engineering SIMTech-NUS Joint Lab on Precision Motion Systems National University of Singapore 117582 Singapore Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Mechatronics Group Singapore Institute of Manufacturing Technology 138634 Singapore Zhejiang Provincial Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo Zhejiang315121 China NUS Grad. School for Inte. Sci. and Eng. and Department of Electrical Computer Engineering National University of Singapore 119077 Singapore

ISBN: (纸本)9781509059980

The dual-drive H gantry is widely used in many industrial processes that require high precision Cartesian motion. The conventional rigid-link version suffers breaking down of joints if any de-synchronization between the two carriages occurs. To prevent above potential risk, a novel biaxial gantry with flexure-links is designed to allow a small rotation angle of the cross-Arm. Nevertheless, the chattering of control signals and inappropriate design of the flexure joint will possibly induce resonant modes of the end effector. Thus, in this work, the design requirements in terms of tracking accuracy, biaxial synchronization, and resonant mode suppression are achieved by integrated optimization of the stiffness of flexures and PID controller parameters for a class of point-To-point trajectories with same dynamics but different steps. From here, an H2 optimization with defined constraints is formulated, and an efficient iterative solver is proposed by hybridizing direct computation of constrained projection gradient and line search of optimal steps. Comparative experiments results obtained on the testbed are presented to verify the effectiveness of proposed method. © 2017 IEEE.

关键词： Constrained optimization

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Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets

arXiv

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arXiv 2017年

作者： Yu, Lequan Cheng, Jie-Zhi Dou, Qi Yang, Xin Chen, Hao Qin, Jing Heng, Pheng-Ann Dept. of Computer Science and Engineering Chinese University of Hong Kong Department of Electrical Engineering Chang Gung University Taoyuan Taiwan Centre for Smart Health School of Nursing Hong Kong Polytechnic University Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular disease. However, this task is very challenging due to ambiguous cardiac borders and large anatomical variations among different subjects. In this paper, we propose a novel densely-connected volumetric convolutional neural network, referred as DenseVoxNet, to automatically segment the cardiac and vascular structures from 3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional architecture for effective volume-to-volume prediction. From the learning perspective, our DenseVoxNet has three compelling advantages. First, it preserves the maximum information flow between layers by a densely-connected mechanism and hence eases the network training. Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data. Third, we add auxiliary side paths to strengthen the gradient propagation and stabilize the learning process. We demonstrate the effectiveness of DenseVoxNet by comparing it with the state-of-the-art approaches from HVSMR 2016 challenge in conjunction with MICCAI, and our network achieves the best dice coeficient. We also show that our network can achieve better performance than other 3D ConvNets but with fewer parameters. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

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