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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Jeonghwan Park Joel T. Johnson Jeffrey Ouellette Department of Electrical and Computer Engineering The Ohio State University Columbus OH USA Remote Sensing Division Naval Research Laboratory Washington DC USA

A study of the polarimetric properties of near specular scattered fields from rough surfaces is reported using a circular polarization basis. Such studies are relevant for Global Navigation Satellite System- Reflectometry (GNSS-R) remote sensing of the sea surface. Particular emphasis in the study is placed on the use of field correlations for the remote sensing of wind direction over the sea surface.

关键词： Sea surface Correlation remote sensing Polarization Rough surfaces Surface roughness

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Pacific-Atlantic Ocean influence on wildfires in northeast China (1774 to 2010)

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Geophysical Research Letters 2017年第2期44卷 1025-1033页

作者： Qichao Yao Peter M. Brown Shirong Liu Monique E. Rocca Valerie Trouet Ben Zheng Haonan Chen Yinchao Li Duanyang Liu Xiaochun Wang College of Forestry Northeast Forestry University Harbin China Laboratory of Tree-Ring Research University of Arizona Tucson Arizona USA Rocky Mountain Tree-Ring Research Fort Collins Colorado USA Institute of Forest Ecology Environment and Protection Chinese Academy of Forestry Beijing China Ecosystem Science and Sustainability Natural Resource Ecology Laboratory Colorado State University Fort Collins Colorado USA Department of Statistics Colorado State University Fort Collins Colorado USA Department of Electrical and Computer Engineering Colorado State University Fort Collins Colorado USA School of Remote Sensing and Information Engineering Wuhan University Wuhan China Jiangsu Meteorological Observatory Nanjing China Harvard Forest Harvard University Petersham Massachusetts USA

Identification of effects that climate teleconnections, such as El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO), have on wildfires is difficult because of short and incomplete records in many areas of the world. We developed the first multicentury wildfire chronologies for northeast China from fire-scarred trees. Regional wildfires occurred every 7 years from the 1700s to 1947, after which fire suppression policies were implemented. Regional wildfires occurred predominately during drought years and were associated with positive phases of ENSO and PDO and negative NAO. Twentieth century meteorological records show that this contingent combination of +ENSO/+PDO/−NAO is linked to low humidity, low precipitation, and high temperature during or before late spring fire seasons. Climate and wildfires in northeast China may be predictable based on teleconnection phases, although future wildfires may be more severe due to effects of climate change and the legacy of fire suppression. Regional wildfires in northeast China reconstructed from tree ring data occurred on average every 7 years from 1774 to 1949 Regional wildfires were associated with regional droughts and large-scale climate drivers (+ENSO/+PDO/−NAO) Fire suppression policies implemented since 1949 may have contributed to more severe fire behavior and more extensive burning

关键词： fire history drought dendroecology Pacific-Atlantic Ocean China

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Nuclei segmentation of fluorescence microscopy images based on midpoint analysis and marked point process

Nuclei segmentation of fluorescence microscopy images based ...

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IEEE Southwest Symposium on image Analysis and Interpretation

作者： Neeraj J. Gadgil Paul Salama Kenneth W. Dunn Edward J. Delp Video and Image Processing Laboratory Purdue University West Lafayette Indiana Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis Indiana Division of Nephrology Indiana University Indianapolis Indiana

Microscope image analysis is challenging because of non-uniform brightness, decreasing image contrast with tissue depth, poor edge details and irregular and unknown structures. In this paper, we present a nuclei segmentation and counting method using midpoint analysis, shape-based function optimization and a MPP simulation with a spatial birth-death process. Preliminary results demonstrate efficacy of the proposed method.

关键词： image segmentation Microscopy Shape Three-dimensional displays Brightness Biology Stochastic processes

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Jelly filling segmentation of fluorescence microscopy images containing incomplete labeling

Jelly filling segmentation of fluorescence microscopy images...

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IEEE International Symposium on Biomedical Imaging

ISBN: (纸本)9781479923519

Biological images acquired using fluorescence microscopy typically suffer from poor edge details, non-uniform brightness, decreasing image contrast with tissue depth and irregular/unknown structure. Hence they are consequently challenging to segment. In this paper, we present a segmentation method we call "Jelly filling" for analyzing biological volumes that also contain incomplete labeling. Our method uses adaptive thresholding, component analysis, and three-dimensional structural consistency to iteratively refine segments that represent physical quantities. Preliminary results obtained using fluorescence microscopy images of rat kidneys demonstrate the efficacy of the proposed method.

关键词： image segmentation Biology Three-dimensional displays Microscopy Labeling image edge detection Filling

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Combined active and passive microwave remote sensing of soil moisture for vegetated surfaces at L-band

Combined active and passive microwave remote sensing of soil...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Huanting Huang Tien-Hao Liao Leung Tsang Eni G. Njoku Andreas Colliander Thomas Jackson Simon Yueh Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA USDA Agricultural Research Service Hydrology and Remote Sensing Laboratory MD USA

The distorted Born approximation (DBA) combined with the numerical solutions of Maxwell equations (NMM3D) has been used for the radar backscattering model for NASA's Soil Moisture Active Passive (SMAP) mission. The models for vegetated surfaces such as wheat, grass, soybean and corn have been validated with the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) data. In this paper we report progress on development of a consistent model for combined active and passive microwave remote sensing of vegetated surfaces by using the same approach to obtain backscatter and emissivity. The active model DBA/NMM3D is extended to calculate bistatic scattering for each of the three scattering mechanisms: volume, double bounce and surface scattering. Then emissivity is obtained by integration of the bistatic scattering. An advantage of this combined active and passive model is that the same physical parameters of vegetation and soil surfaces are used in both the active model and the passive model. The β parameter that relates backscattering to emissivity is also derived for various vegetated surfaces.

关键词： Scattering Mathematical model Soil moisture Vegetation mapping Rough surfaces Surface roughness Biological system modeling

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Variable-delay polarization modulators for the CLASS telescopes

arXiv

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arXiv 2018年

作者： Harrington, Kathleen Eimer, Joseph Chuss, David T. Petroff, Matthew Cleary, Joseph DeGeorge, Martin Grunberg, Theodore W. Ali, Aamir Appel, John W. Bennett, Charles L. Brewer, Michael Bustos, Ricardo Chan, Manwei Couto, Jullianna Dahal, Sumit Denis, Kevin Dünner, Rolando Essinger-Hileman, Thomas Fluxa, Pedro Halpern, Mark Hilton, Gene Hinshaw, Gary F. Hubmayr, Johannes Iuliano, Jeffrey Karakla, John Marriage, Tobias McMahon, Jeffrey Miller, Nathan J. Nuñez, Carolina Padilla, Ivan L. Palma, Gonzalo Parker, Lucas Marquez, Bastian Pradenas Reeves, Rodrigo Reintsema, Carl Rostem, Karwan Valle, Deniz Augusto Nunes van Engelhoven, Trevor Wang, Bingjie Wang, Qinan Watts, Duncan Weiland, Janet Wollack, Edward Xu, Zhilei Yan, Ziang Zeng, Lingzhen Department of Physics and Astronomy Johns Hopkins University BaltimoreMD21218 United States Department of Physics Villanova University VillanovaPA19085 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics University of California BerkeleyCA94720 United States Facultad de Ingeniería Universidad Católica de la Santísima Concepción Alonso de Ribera 2850 Concepción Chile NASA Goddard Space Flight Center GreenbeltMD20771 United States Instituto de Astrofísica and Centro de Astro-Ingeniería Facultad de Física Pontificia Universidad Católica de Chile Macul Santiago7820436 Chile Department of Physics and Astronomy University of British Columbia VancouverBCV6T 1z4 Canada National Institute of Standards and Technology BoulderCO80305 United States Department of Physics University of Michigan Ann ArborMI48109 United States Departmento de Física FCFM Universidad de Chile Blanco Encalada 2008 Santiago Chile Space and Remote Sensing MS D436 Los Alamos National Laboratory Los AlamosNM87544 United States Departamento de Astronomía Universidad de Concepción Casilla 160 C Concepción Chile Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Harvard-Smithsonian Center for Astrophysics CambridgeMA02138 United States

The search for inflationary primordial gravitational waves and the measurement of the optical depth to reionization, both through their imprint on the large angular scale correlations in the polarization of the cosmic microwave background (CMB), has created the need for high sensitivity measurements of polarization across large fractions of the sky at millimeter wavelengths. These measurements are subject to instrumental and atmospheric 1/f noise, which has motivated the development of polarization modulators to facilitate the rejection of these large systematic effects. Variable-delay polarization modulators (VPMs) are used in the Cosmology Large Angular Scale Surveyor (CLASS) telescopes as the first element in the optical chain to rapidly modulate the incoming polarization. VPMs consist of a linearly polarizing wire grid in front of a movable flat mirror. Varying the distance between the grid and the mirror produces a changing phase shift between polarization states parallel and perpendicular to the grid which modulates Stokes U (linear polarization at 45◦) and Stokes V (circular polarization). The CLASS telescopes have VPMs as the first optical element from the sky;this simultaneously allows a lock-in style polarization measurement and the separation of sky polarization from any instrumental polarization further along in the optical path. The CLASS VPM wire grids use 50 µm copper-plated tungsten wire with a 160 µm spacing across a 60 cm clear aperture. The mirror is mounted on a flexure system with one degree of translational freedom, enabling the required mirror motion while maintaining excellent parallelism with respect to the wire grid. The wire grids and mirrors are held parallel to each other to better than 80 µm, and the wire grids have RMS flatness errors below 50 µm across the 60 cm aperture. The Q-band CLASS VPM was the first VPM to begin observing the CMB full time, starting in the Spring of 2016. The first W-band CLASS VPM was installed in the Spring o

关键词： Cosmology

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The 5th International Conference on Biomedical engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Face recognition under pose variation with active shape model to adjust Gabor filter kernels and to correct feature extraction location

Face recognition under pose variation with active shape mode...

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International Conference on Automatic Face and Gesture Recognition

作者： Leonardo A. Cament Francisco J. Galdames Kevin W. Bowyer Claudio A. Perez Department of Electrical Engineering and the Advanced Mining Technology Center Image Processing Laboratory Universidad de Chile Chile Department of Computer Science and Engineering University of Notre Dame IN USA

Gabor features have been used widely in face identification because of their good results and robustness. However, face identification is strongly affected when the test images are very different from those of the gallery, as is the case in varying face pose. In this paper, a new 2D Gabor-based method is proposed that modifies the grid from which the Gabor features are extracted using a mesh to model face deformations produced by varying pose. Also the Gabor filter kernels are modified by the deformation field computed using the active shape model adjusted to the face. Therefore, the position of the Gabor jets with respect to the face features (eyes, nose, mouth, etc.) is closer to the original position in the frontal face and the modified Gabor filter kernel adjusts to the varying pose. Our method incorporates blocks for illumination compensation by a Local Normalization method, and entropy-weighted Gabor features to emphasize those features that yield proper identification. The method was assessed on the FERET database including pose variations for ±60°, ±40°, ±25°, ±15°, and 0°. We compared our results to those previously published for 2D methods. Our proposed method achieved the highest classification accuracy on the FERET database.

关键词： Face Shape Databases Gabor filters Feature extraction Face recognition Robustness

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AN INTERCOMPARISON OF MODELS FOR PREDICTING BISTATIC SCATTERING FROM ROUGH SURFACES

AN INTERCOMPARISON OF MODELS FOR PREDICTING BISTATIC SCATTER...

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IEEE International Geoscience and remote sensing Symposium

作者： Caglar Yardim Joel T. Johnson Robert J. Burkholder Fernando L. Teixeira Jeffrey D. Ouellette Kun-Shan Chen Marco Brogioni Nazzareno Pierdicca ElectroScience Laboratory Department of Electrical and Computer Engineering Tho Ohio State University Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences IFAC-CNR Sesto Fiorentino Sapienza University of Rome

ISBN: (纸本)9781479979301

This paper investigates the algorithms that are used to predict the full polarimetric bistatic normalized radar cross-section of rough surfaces. These include small perturbation method (SPM), the physical optics (PO) approach, the small slope approximation (SSA) and the integration equation method (IEM) and its derivatives improved IEM and advanced IEM. The methods are then compared to ground truth values obtained from multiple Monte Carlo runs a numerical Method of Moments (MOM) code using the same surface statistics. Effects of using band-limited exponential instead of a true exponential correlation function for surface statistics are also explored. Mean L_1-norm error values integrated over the hemisphere are given between AIEM and MOM and SSA and MOM.

关键词： Surface roughness Radar cross-section Bistatic radar

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