检索结果-内蒙古大学图书馆

An Effective Method for Modeling Two-dimensional Sky Background of LAMOST

Proceedings of the International Astronomical Union 2017年第S325期12卷 63-66页

作者： Hasitieer Haerken Fuqing Duan Jiannan Zhang Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University 100875 Beijing China email: fqduan@bnu.edu.cnpguo@*** National Astronomical Observatories & Chinese Academy of Sciences 100012 Beijing China email: hastear@***jnzhang@***

Each CCD of LAMOST accommodates 250 spectra, while about 40 are used to observe sky background during real observations. How to estimate the unknown sky background information hidden in the observed 210 celestial spectra by using the known 40 sky spectra is the problem we solve. In order to model the sky background, usually a pre-observation is performed with all fibers observing sky background. We use the observed 250 skylight spectra as training data, where those observed by the 40 fibers are considered as a base vector set. The Locality-constrained Linear Coding (LLC) technique is utilized to represent the skylight spectra observed by the 210 fibers with the base vector set. We also segment each spectrum into small parts, and establish the local sky background model for each part. Experimental results validate the proposed method, and show the local model is better than the global model.

关键词： LAMOST celestial spectra sky background modeling

来源：评论

学校读者我要写书评

暂无评论

Shape classification according to LBP persistence of critical points 19th

Shape classification according to LBP persistence of critica...

引用

19th IAPR International Conference on Discrete Geometry for Computer imagery, DGCI 2016

作者： Janusch, Ines Kropatsch, Walter G. Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms TU Wien Vienna Austria

ISBN: (纸本)9783319323596

This paper introduces a shape descriptor based on a combination of topological image analysis and texture information. Critical points of a shape’s skeleton are determined first. The shape is described according to persistence of the local topology at these critical points over a range of scales. The local topology over scale-space is derived using the local binary pattern texture operator with varying radii. To visualise the descriptor, a new type of persistence graph is defined which captures the evolution, respectively persistence, of the local topology. The presented shape descriptor may be used in shape classification or the grouping of shapes into equivalence classes. Classification experiments were conducted for a binary image dataset and the promising results are presented. Because of the use of persistence, the influence of noise or irregular shape boundaries (e.g. due to segmentation artefacts) on the result of such a classification or grouping is bounded. © Springer International Publishing Switzerland 2016.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

<i>L</i>(2,1)-Labeling of the Brick Product Graphs

引用

Journal of Applied Mathematics and Physics 2017年第8期5卷 1529-1536页

作者： Xiujun Zhang Hong Yang Hong Li School of Information Science and Engineering Chengdu University Chengdu China Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu University Chengdu China

A k-L(2,1)-labeling for a graph G is a function such that whenever and whenever u and v are at distance two apart. The λ-number for G, denoted by λ(G), is the minimum k over all k-L(2,1)-labelings of G. In this paper, we show that for or 11, which confirms Conjecture 6.1 stated in [X. Li, V. Mak-Hau, S. Zhou, The L(2,1)-labelling problem for cubic Cayley graphs on dihedral groups, J. Comb. Optim. (2013) 25: 716-736] in the case when or 11. Moreover, we show that? if 1) either (mod 6), m is odd, r = 3, or 2) (mod 3), m is even (mod 2), r = 0.

关键词： Graph Labeling Brick Product Graph L((2 1)-Labeling Frequency Assignment Problem

来源：评论

学校读者我要写书评

暂无评论

Semantic channels for fast pedestrian detection

Semantic channels for fast pedestrian detection

引用

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

作者： Costea, Arthur Daniel Nedevschi, Sergiu Image Processing and Pattern Recognition Research Center Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781467388511

Pedestrian detection and semantic segmentation are high potential tasks for many real-time applications. However most of the top performing approaches provide state of art results at high computational costs. In this work we propose a fast solution for achieving state of art results for both pedestrian detection and semantic segmentation. As baseline for pedestrian detection we use sliding windows over cost efficient multiresolution filtered LUV+HOG channels. We use the same channels for classifying pixels into eight semantic classes. Using short range and long range multiresolution channel features we achieve more robust segmentation results compared to traditional codebook based approaches at much lower computational costs. The resulting segmentations are used as additional semantic channels in order to achieve a more powerful pedestrian detector. To also achieve fast pedestrian detection we employ a multiscale detection scheme based on a single flexible pedestrian model and a single image scale. The proposed solution provides competitive results on both pedestrian detection and semantic segmentation benchmarks at 8 FPS on CPU and at 15 FPS on GPU, being the fastest top performing approach.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Nonconvex penalties with analytical solutions for one-bit compressive sensing

arXiv

引用

arXiv 2017年

作者： Huang, Xiaolin Yan, Ming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing Shanghai200240 China Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University East LansingMI48824 United States

One-bit measurements widely exist in the real world and can be used to recover sparse signals. This task is known as one-bit compressive sensing (1bit-CS). In this paper, we propose novel algorithms based on both convex and nonconvex sparsity-inducing penalties for robust 1bit-CS. We consider the dual problem, which has only one variable and provides a sufficient condition to verify whether a solution is globally optimal or not. For positive homogeneous penalties, a globally optimal solution can be obtained in two steps: a proximal operator and a normalization step. For other penalties, we solve the dual problem, and it needs to evaluate the proximal operators for many times. Then we provide fast algorithms for finding analytical solutions for three penalties: minimax concave penalty (MCP), 0norm, and sorted 1penalty. Specifically, our algorithm is more than 200 times faster than the existing algorithm for MCP. Its efficiency is comparable to the algorithm for the 1penalty in time, while its performance is much better than 1. Among these penalties, sorted 1is most robust to noise in different settings. Copyright © 2017, The Authors. All rights reserved.

关键词： Compressed sensing

来源：评论

学校读者我要写书评

暂无评论

DNA Sequence Alignment Algorithm Based on k-tuple Statistics

DNA Sequence Alignment Algorithm Based on k-tuple Statistics

引用

2017 2nd International Conference on Software, Multimedia and Communication Engineering（SMCE 2017)

作者： Jun-yan ZHANG Chen-hui YANG Hai-ying WANG College of Information Science and Engineering Chengdu UniversityChengdu 610106China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University

DNA Sequence Alignment is one of the most basic and most important operations in *** this paper,we put forward to SDk S algorithm based on k-tuple statistic,which is a kind of probability *** positive transition probability matrix,the negative transition probability matrix,and the logarithmic ration are computed based on Markov *** obtaining converting sequence,we get the results of k-tuple ***,the similarity measurement can be gained according to above *** the minimum support is given,we can identify two DNA sequences is similarity or *** experimental results show that SDk S algorithm has better effective performance in saving computing time.

关键词： Sequence alignment k-tuple statistic Markov model Similarity measurement

来源：评论

学校读者我要写书评

暂无评论

Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on Computer Vision and pattern recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Scale-space anisotropic total variation for limited angle tomography

arXiv

引用

arXiv 2017年

作者： Huang, Yixing Taubmann, Oliver Huang, Xiaolin Haase, Viktor Lauritsch, Guenter Maier, Andreas Pattern Recognition Lab Friedrich-Alexander-University Erlangen-Nuremberg Erlangen Germany Pattern Recognition Lab FriedrichAlexander-University Erlangen-Nuremberg Erlangen Germany Erlangen Germany Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Siemens Healthcare GmbH Forchheim Germany Department of Radiology University of Utah Salt Lake CityUT United States

This paper addresses streak reduction in limited angle tomography. Although the iterative reweighted total variation (wTV) algorithm reduces small streaks well, it is rather inept at eliminating large ones since total variation (TV) regularization is scale-dependent and may regard these streaks as homogeneous areas. Hence, the main purpose of this paper is to reduce streak artifacts at various scales. We propose the scale-space anisotropic total variation (ssaTV) algorithm in two different implementations. The first implementation (ssaTV-1) utilizes an anisotropic gradient-like operator which uses 2 · s neighboring pixels along the streaks' normal direction at each scale s. The second implementation (ssaTV-2) makes use of anisotropic down-sampling and up-sampling operations, similarly oriented along the streaks' normal direction, to apply TV regularization at various scales. Experiments on numerical and clinical data demonstrate that both ssaTV algorithms reduce streak artifacts more effectively and efficiently than wTV, particularly when using multiple scales. Copyright © 2017, The Authors. All rights reserved.

关键词： Anisotropy

来源：评论

学校读者我要写书评

暂无评论

Varifocal-Net: A Chromosome Classification Approach Using Deep Convolutional Networks

arXiv

引用

arXiv 2018年

作者： Qin, Yulei Wen, Juan Zheng, Hao Huang, Xiaolin Yang, Jie Song, Ning Zhu, Yue-Min Wu, Lingqian Yang, Guang-Zhong Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Center for Medical Genetics School of Life Sciences Central South University Changsha410078 China Shanghai Key Laboratory of Reproductive Medicine School of Medicine Shanghai Jiao Tong University Shanghai200025 China Diagens-Hangzhou Hangzhou311121 China University Lyon INSA Lyon CNRS INSERM CREATIS UMR 5220 U1206F-69621 France Hamlyn Centre for Robotic Surgery Imperial College London SW72AZ United Kingdom

Chromosome classification is critical for karyotyping in abnormality diagnosis. To expedite the diagnosis, we present a novel method named Varifocal-Net for simultaneous classification of chromosomes type and polarity using deep convolutional networks. The approach consists of one global-scale network (G-Net) and one local-scale network (L-Net). It follows three stages. The first stage is to learn both global and local features. We extract global features and detect finer local regions via the G-Net. By proposing a varifocal mechanism, we zoom into local parts and extract local features via the L-Net. Residual learning and multi-task learning strategies are utilized to promote high-level feature extraction. The detection of discriminative local parts is fulfilled by a localization subnet of the G-Net, whose training process involves both supervised and weakly-supervised learning. The second stage is to build two multi-layer perceptron classifiers that exploit features of both two scales to boost classification performance. The third stage is to introduce a dispatch strategy of assigning each chromosome to a type within each patient case, by utilizing the domain knowledge of karyotyping. Evaluation results from 1909 karyotyping cases showed that the proposed Varifocal-Net achieved the highest accuracy per patient case (%) of 99.2 for both type and polarity tasks. It outperformed state-of-the-art methods, demonstrating the effectiveness of our varifocal mechanism, multi-scale feature ensemble, and dispatch strategy. The proposed method has been applied to assist practical karyotype diagnosis. Copyright © 2018, The Authors. All rights reserved.

关键词： Computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

image Stitching with single-hidden layer feedforward Neural Networks

Image Stitching with single-hidden layer feedforward Neural ...

引用

International Joint Conference on Neural Networks

作者： Min Yan Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University

ISBN: (纸本)9781509006212

In this paper, a novel image stitching method is proposed, which utilizes scale-invariant feature transform (SIFT) feature and single-hidden layer feedforward neural network (SLFN) to get higher precision of parameter estimation. In this method, features are extracted from the image sets by the SIFT descriptor and form into the input vector of the SLFN. The output of the SLFN is those translation, rotation and scaling parameters with respect to reference and registered image sets. We also apply a fast learning scheme, called pseudoinverse learning, to train SLFN to get higher training efficiency. Comparative experiments are performed between our proposed method and the traditional random sample consensus (RANSAC) based method. The results show that our method has the advantage not only at accuracy but also remarkably at fast speed.

关键词： SIFT descriptor Feedforward neural network image registration image stitching feedforward neural nets image stitching image registration Parameter estimation fast speed

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：