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arXiv 2018年

作者： He, Lei Wang, Guanghui Hu, Zhanyi University of Chinese Academy of Sciences Beijing100049 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS66045 United States

Learning depth from a single image, as an important issue in scene understanding, has attracted a lot of attention in the past decade. The accuracy of the depth estimation has been improved from conditional Markov random fields, non-parametric methods, to deep convolutional neural networks most recently. However, there exist inherent ambiguities in recovering 3D from a single 2D image. In this paper, we first prove the ambiguity between the focal length and monocular depth learning, and verify the result using experiments, showing that the focal length has a great influence on accurate depth recovery. In order to learn monocular depth by embedding the focal length, we propose a method to generate synthetic varying-focal-length dataset from fixed-focal-length datasets, and a simple and effective method is implemented to fill the holes in the newly generated images. For the sake of accurate depth recovery, we propose a novel deep neural network to infer depth through effectively fusing the middle-level information on the fixed-focal-length dataset, which outperforms the state-of-the-art methods built on pre-trained VGG. Furthermore, the newly generated varying-focal-length dataset is taken as input to the proposed network in both learning and inference phases. Extensive experiments on the fixed- and varying-focal-length datasets demonstrate that the learned monocular depth with embedded focal length is significantly improved compared to that without embedding the focal length information. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Epoxy-inspired nonlinear interface integrating monolayer transition-metal dichalcogenides with linear plasmonic nanosieves

arXiv

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arXiv 2019年

作者： Hong, Xuanmiao Hu, Guangwei Zhao, Wenchao Wang, Kai Sun, Shang Zhu, Rui Wu, Jing Liu, Weiwei Ping, Loh Kian Wee, Andrew Thye Shen Wang, Bing Alù, Andrea Qiu, Cheng-Wei Lu, Peixiang Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore Advanced Science Research Center City University of New York New York10031 United States 2 Fusionopolis Way Innovis #08-03 138634 Singapore Department of Physics National University of Singapore 2 Science Drive 3 Singapore117551 Singapore Department of Chemistry National University of Singapore 3 Science Drive 3 Singapore17543 Singapore Centre for Advanced 2D Materials National University of Singapore Block S14 6 Science Drive 2 Singapore117546 Singapore Hubei Key Laboratory of Optical information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China

Although TMDC monolayers offer giant optical nonlinearity within few-angstrom thickness, it is still elusive to modulate and engineer the wavefront of nonlinear emissions. The grain size of high-quality monolayers also restricts possibilities of imparting inhomogeneous or gradient profiles of phases and amplitudes by classical light sources. Plasmonic nanosieves can support huge field enhancement and precise nonlinear phase control with hundred-nanometer pixel-level resolution, however they suffer from intrinsically weak nonlinear susceptibility. In nature, epoxy represents a strong glue whose magic adhesion comes from the bonding of two intrinsic loose and nonsticky resins. Inspired by the nature of epoxy, we report a multifunctional and powerful nonlinear interfaces via integrating transition-metal dichalcogenide (TMDC) monolayers with linear plasmonic nanosieves, which cannot be accessed by either constituents. We experimentally demonstrate orbital angular momentum (OAM) generation, beam steering, versatile polarization control and holograms and of SH emission, with the proposed nonlinear interfaces. In addition, an effective second-harmonic (SH) nonlinearity χ(2) of ~25 nm/V is obtained for such systems. This designer platform synergizes the complementary advantages of TMDC monolayer, plasmonic nanosieves, geometric phases, and field enhancement, paving a distinct avenue toward designer, multi-functional, and ultra-compact nonlinear optical devices. Copyright © 2019, The Authors. All rights reserved.

关键词： Monolayers

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Edgy salient local binary patterns in inter-plane relationship for image retrieval in Diabetic Retinopathy

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Procedia computer Science 2017年 115卷 440-447页

作者： Gajanan M. Galshetwar Laxman M. Waghmare Anil B. Gonde Subrahmanyam Murala Center of Excellence in Signal and Image Processing (COESIP) Department of ECE SGGSIET Nanded Maharashtra 431606 India Computer Vision and Pattern Recognition Laboratory Department of Electrical Engineering IIT Ropar Rupnagar 140001 India

In this paper, a novel approach for content based image retrieval (CBIR) in diabetic retinopathy (DR) is proposed. The concept of salient point selection and inter-plane relationship technique is used. Salient points are selected from edgy image and later using inter-planer relationship, Local Binary patterns (LBPs) are calculated using the salient point as a center pixel. Our approach enhanced the results as we used color features in combination with LBP features. Experimentation is carried out on MESSIDOR database of 1200 retinal images, proposed approach has average precision of 57.82% as compared to the earlier approach whose average precision is 53.70%.

关键词： Content-Based image retrieval (CBIR) Diabetic Retinopathy (DR) Edgy Salient points Local Binary patterns (LBPs)

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QuaSI: Quantile sparse image prior for spatio-temporal denoising of retinal OCT data

arXiv

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arXiv 2017年

作者： Schirrmacher, Franziska Köhler, Thomas Husvogt, Lennart Fujimoto, James G. Hornegger, Joachim Maier, Andreas K. Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Department of Electrical Engineering & Computer Science and Research Laboratory of Electronics Massachusetts Institute of Technology

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Fiber orientation estimation guided by a deep network

arXiv

引用

arXiv 2017年

作者： Ye, Chuyang Prince, Jerry L. National Laboratory of Pattern Recognition & Brainnetome Center Institute of Automation Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain’s white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for fiber tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs with a relatively small number of diffusion gradients. However, accurate FO estimation in regions with complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent the diffusion signals. To estimate the mixture fractions of the dictionary atoms (and thus coarse FOs), a deep network is designed specifically for solving the sparse reconstruction problem. Here, the smaller dictionary is used to reduce the computational cost of training. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding dense basis FOs is used and a weighted `1-norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and real dMRI data, and the results demonstrate the benefit of using a deep network for FO estimation. Copyright © 2017, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Dynamic vortical flow profile quantitatively characterizes mitral valvular-left ventricular hemodynamic coupling: In vivo analysis by dynamic 3D enstrophy mapping from 4D Flow MRI

arXiv

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arXiv 2018年

作者： Elbaz, Mohammed S.M. Le, Trung Bao Garg, Pankaj Roest, Arno A.W. Lelieveldt, Boudewijn P.F. Plein, Sven Westenberg, Jos J.M. Sotiropoulos, Fotis van der Geest, Rob J. Department of Radiology Northwestern University ChicagoIL United States Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Civil Engineering Stony Brook University Stony BrookNY United States University of Leeds Leeds United Kingdom Department of Pediatrics Division of Pediatric Cardiology Leiden University Medical Center Leiden Netherlands Pattern Recognition and Bioinformatics Group Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Computational Fluids Laboratory North Dakota State University United States

Vortical blood flow in the human left ventricular (LV) inflow initiates from the mitral valve (MV) and evolves within the LV during diastolic E-filling. Hence, vortical flow links MV and LV hemodynamics. This study sought to elucidate and quantitatively characterize the in vivo 3D dynamics of LV vortical flow over E-filling and relation to MV-LV hemodynamic coupling using 4D Flow MRI flow field. 34 healthy volunteers and 5 example patients underwent 4D Flow MRI. Vortical blood flow evolution was mapped in the LV over E-wave using enstrophy density. A new dimensionless profile PMV-LV was derived as a function of both MV vortex formation time (VFT) and LV volumetric enstrophy density. Results reveal that 3D vortical flow evolution in the healthy LV follows a bi-phasic behavior with a vortical growth phase followed by a vortical decay phase. In healthy LVs studied, the PMV-LV profile showed that the vortical growth and decay phases are characterized by a vortical growth time Tgrowth = 1.23 ± 0.25, growth rate α = 0.80 ± 0.17, decay time Tdecay = 0.96 ± 0.39 and decay rate β = -1.02 ± 0.49. Distinctly altered parameters were found in the pilot patients studied. The derived PMV-LV profile quantitatively characterizes MV-LV hemodynamic coupling by vortical flow dynamics. Results herein unravel new insights into cardiac physiology and could enable a novel standardized methodology to study MV-LV hemodynamic coupling and association to cardiac function in health and disease. Copyright © 2018, The Authors. All rights reserved.

关键词： Heart

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Face alignment across large poses: A 3D solution

Face alignment across large poses: A 3D solution

引用

2016 IEEE Conference on computer Vision and pattern recognition, CVPR 2016

作者： Zhu, Xiangyu Lei, Zhen Liu, Xiaoming Shi, Hailin Li, Stan Z. Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China Department of Computer Science and Engineering Michigan State University United States

ISBN: (纸本)9781467388511

Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community. However, most algorithms are designed for faces in small to medium poses (below 45°), lacking the ability to align faces in large poses up to 90°. The challenges are three-fold: Firstly, the commonly used landmark-based face model assumes that all the landmarks are visible and is therefore not suitable for profile views. Secondly, the face appearance varies more dramatically across large poses, ranging from frontal view to profile view. Thirdly, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose a solution to the three problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN). We also propose a method to synthesize large-scale training samples in profile views to solve the third problem of data labelling. Experiments on the challenging AFLW database show that our approach achieves significant improvements over state-of-the-art methods.

关键词： Alignment

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Face Alignment Across Large Poses: A 3D Solution

Face Alignment Across Large Poses: A 3D Solution

引用

IEEE Conference on computer Vision and pattern recognition

作者： Xiangyu Zhu Zhen Lei Xiaoming Liu Hailin Shi Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Department of Computer Science and Engineering Michigan State University

ISBN: (纸本)9781467388528

关键词： face modeling Face Posing Face landmarks Imagery (Psychotherapy) images Alignment

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Theoretical Assessment of Different Ultrasonic Configurations for Delamination Defects Detection in Composite Components

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Procedia CIRP 2017年 59卷 29-34页

作者： V. Kappatos G. Asfis K. Salonitis V. Tzitzilonis N.P. Avdelidis E. Cheilakou P. Theodorakeas Department of Technology and Innovation University of Southern Denmark (SDU) Campusvej 55 DK-5230 Odense M Denmark TWI Ltd Granta Park Great Abington Cambridge CB21 6AL UK Manufacturing Department Cranfield University Cranfield MK430AL UK Pattern Recognition Group Wire Communication Laboratory Department of Electrical & Computer Engineering University of Patras Greece Materials Science & Engineering Department School of Chemical Engineering National Technical University of Athens Zografou Campus 157 80 Athens Greece

It is well known that structures’ safety is crucial and of great importance. Part of their maintenance procedure is structural inspection, which is currently performed with the aid of Non Destructive Testing techniques, aiming to detect structural defects in damaged or flawed components and prevent a catastrophic failure by substituting or repairing them. The objective of this work is the theoretical assessment of different ultrasonic configurations that could maximize delamination defect detection in composite components. Modeling study was performed using simulation software, where physical models representative of laminated Carbon Fiber Reinforced Polymer composites, consisting of a variety of artificial delamination defect modes (different sizes and depth), were numerically tested. Different ultrasonic configurations on both the positioning and the firing of the probe's elements including Phased Array delay timings and sampled array techniques were investigated and are presented in this paper. The potential of Full Matrix Capture data acquisition technique, modelled here, along with the post processing Total Focusing Method reconstruction approach is also assessed in terms of their ability to enhance defect detectability and visualization.

关键词： Ultrasonic Assessment Sensor Configuration Phased Array Composite.

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Multimodal Fusion of Brain Imaging Data: A Key to Finding the Missing Link(s) in Complex Mental Illness

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2016年第3期1卷 230-244页

作者： Calhoun, Vince D. Sui, Jing Mind Research Network Lovelace Biomedical and Environmental Research Institute Albuquerque NM United States Department of Electrical and Computer Engineering University of New Mexico Albuquerque NM United States Brainnetome Center National Laboratory of Pattern Recognition Beijing China Chinese Academy of Sciences Center for Excellence in Brain Science Institute of Automation Beijing China

It is becoming increasingly clear that combining multimodal brain imaging data provides more information for individual subjects by exploiting the rich multimodal information that exists. However, the number of studies that do true multimodal fusion (i.e., capitalizing on joint information among modalities) is still remarkably small given the known benefits. In part, this is because multimodal studies require broader expertise in collecting, analyzing, and interpreting the results than do unimodal studies. In this article, we start by introducing the basic reasons why multimodal data fusion is important and what it can do and, importantly, how it can help us avoid wrong conclusions and help compensate for imperfect brain imaging studies. We also discuss the challenges that need to be confronted for such approaches to be more widely applied by the community. We then provide a review of the diverse studies that have used multimodal data fusion (primarily focused on psychosis) as well as provide an introduction to some of the existing analytic approaches. Finally, we discuss some up-and-coming approaches to multimodal fusion including deep learning and multimodal classification that show considerable promise. Our conclusion is that multimodal data fusion is rapidly growing, but it is still underutilized. The complexity of the human brain coupled with the incomplete measurement provided by existing imaging technology makes multimodal fusion essential to mitigate misdirection and hopefully provide a key to finding the missing link(s) in complex mental illness. © 2016 Society of Biological Psychiatry.

关键词： Brain function Connectivity Data fusion Independent component analysis Psychosis Schizophrenia

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