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计算机系统应用 2018年第12期27卷 257-261页

作者：赵越秀高俊钗李翰山西安工业大学电子信息工程学院西安710021

海天背景图像存在大面积天空区域,且在远距离平视时目标一定出现在海天线附近,现有的去雾算法对天空区域的改善都是弱化天空区域的处理,这样势必会造成海天线附近去雾效果的减弱,不利于后续的目标检测.针对该问题,提出一种基于大气散射... 详细信息

海天背景图像存在大面积天空区域,且在远距离平视时目标一定出现在海天线附近,现有的去雾算法对天空区域的改善都是弱化天空区域的处理,这样势必会造成海天线附近去雾效果的减弱,不利于后续的目标检测.针对该问题,提出一种基于大气散射模型的图像复原去雾算法.首先,利用海天背景图像的特点,采用边缘检测算子将图像划分为天空和非天空区域,并结合大气光的物理意义,取天空区域最大的值作为大气光的估计值;其次,针对有雾图像对比度很低而无雾图像对比度较高这一先验信息设计代价函数,并通过SLIC超像素分割进行分块,通过求解每个小块内该函数的最小值,估计出粗透射率,再用引导滤波对粗透射率进行细化从而消除块效应;最后,利用大气散射模型,代入前两步求得的参数便可以得到恢复的无雾图像.实验结果与分析结果表明,本文能对海天背景的图像取得较好的去雾效果.

关键词：海天背景去雾大气散射模型大气光超像素透射率

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A NON-ITERATIVE BLIND image DEBLURRING ALGORITHM BASED ON OTF ESTIMATION

A NON-ITERATIVE BLIND IMAGE DEBLURRING ALGORITHM BASED ON OT...

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International Symposium on Intelligent Signal processing and Communication systems

作者： Weizhe Gao Jianhua Zou Xuebin Xu Zhiguang Zhang Systems Engineering Institute Xi'an Jiaotong University Xi'an China Guangdong Shunde Xi'an Jiaotong University Academy Foshan City China

This paper proposes a non-iterative algorithm on blind image deblurring. This algorithm can restore the degraded images which are blurred by class G. This algorithm is based on that most images spectral amplitude have the similar power law distribution. In accordance with the power law distribution of natural image spectrum, a curve model is proposed to approximate the spectrum of the true image. The OTF(optical transfer function) is estimated by comparing the spectrum of the degraded image with the reconstructed one. Then, the image is restored by employing the estimated OTF and the Wiener filtering. The experiments show that this algorithm obtains a more accurate OTF, and this algorithm can reduce ringing artifacts as compared with some existing algorithms. The quality of the restored images has been enhanced significantly.

关键词： Blind image deblurring OTF estimation Levy processes Atmospheric turbulence

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Shape classification of wear particles by image boundary analysis using machine learning algorithms

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MECHANICAL systems AND SIGNAL processing 2016年 72-73卷 346-358页

作者： Yuan, Wei Chin, K. S. Hua, Meng Dong, Guangneng Wang, Chunhui Xi An Jiao Tong Univ Key Lab Modern Design & Rotor Bearing Syst Educ Minist Xian 710049 Peoples R China City Univ Hong Kong Dept Syst Engn & Engn Management 83 Tat Chee Ave Kowloon Tong Hong Kong Peoples R China City Univ Hong Kong Dept Mech & Biomed Engn 83 Tat Chee Ave Kowloon Tong Hong Kong Peoples R China

The shape features of wear particles generated from wear track usually contain plenty of information about the wear states of a machinery operational condition. Techniques to quickly identify types of wear particles quickly to respond to the machine operation and prolong the machine's life appear to be lacking and are yet to be established. To bridge rapid off-line feature recognition with on-line wear mode identification, this paper presents a new radial concave deviation (RCD) method that mainly involves the use of the particle boundary signal to analyze wear particle features. Signal output from the RCDs subsequently facilitates the determination of several other feature parameters, typically relevant to the shape and size of the wear particle. Debris feature and type are identified through the use of various classification methods, such as linear discriminant analysis, quadratic discriminant analysis, naive Bayesian method, and classification and regression tree method (CART). The average errors of the training and test via ten-fold cross validation suggest CART is a highly suitable approach for classifying and analyzing particle features. Furthermore, the results of the wear debris analysis enable the maintenance team to diagnose faults appropriately. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Wear particles image processing Radial concave deviation Particle classification Machine learning

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image processing pipeline model integrating steganographic algorithms for mobile cameras

Image processing pipeline model integrating steganographic a...

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2016 ACM International Workshop on Traffic Measurements for Cybersecurity, WTMC 2016

作者： Dahal, Prabhat Peng, Dongming Sharif, Hamid Department of Electrical and Computer Engineering University of Nebraska Lincoln United States

ISBN: (纸本)9781450342841

Mobile phones these days are extensively used to capture and share multimedia right from the device without the basic security processing like watermarking. This necessitates the need for multimedia to be watermarked right within the mobile hardware for copyright protection and authentication. In this paper, we propose an image processing pipelining model for mobile cameras that integrates watermarking algorithm with the image capturing processes in cameras. Differently from existing works reported in literature, the unique contribution in this paper is to have image watermarked while in the early capturing process. In other words, the processes for digital image capturing and watermarking have become as a single non-separable procedure in the camera hardware systems. The paper proposes watermarking raw image data before it becomes display ready within the image capturing process. Also, the watermarking algorithm used to develop the model, without losing generality, caters to the computational efficiency requirement for mobile devices while being robust against image coding at the same time. Simulations results demonstrate that the proposed approach is robust against JPEG compression. © 2016 ACM.

关键词： Computational efficiency

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Improving the Performance of Vehicle Detection System in Bad Weathers

Improving the Performance of Vehicle Detection System in Bad...

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Xuerui Dai Xue Yuan Jing Zhang Liping Zhang School of electronic and Information Beijing Jiaotong University Naval Aeronautical Engineering Institute Beijing Huahang Radio Measurement Institute

ISBN: (纸本)9781509001668

Vehicle detection has been applied in many fields,such as intelligent transportation,video surveillance,driving assistance system and so *** the case of fine weather,the state-of-the-art vehicle-detection systems may achieve good ***,the performance has a substantial decline in bad weathers,such as fog,night and so ***,improving the performance of vehicle-detection systems in different weather conditions becomes an important issue in vehicle-detection *** the fog or night,the quality of the images is *** this paper,we propose some algorithms of image defogging and color enhancement in order to improve the performance of vehicle *** result of vehicle detection get much better after image processing in bad weathers.

关键词： Vehicle detection bad weathers image defogging color enhancement

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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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A high-speed image super-resolution algorithm based on sparse representation for MEMS defect detection

A high-speed image super-resolution algorithm based on spars...

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IEEE International Conference on Nano/Micro Engineered and Molecular systems (NEMS)

作者： xiuyuan Li Yulong Zhao Tengjiang Hu Qi Zhang Yingxue Li StateKey Laboratory for Mechanical Manufacturing System Xi'an Jiaotong University China Collaborative Innovation Center of High-End Manufacturing Equipment Xi'an Jiaotong University China

ISBN: (纸本)9781509019489

A novel high- speed image super-resolution algorithm based on sparse representation for MEMS defect detection is proposed in this paper. Traditional super-resolution algorithms adopt a single dictionary to represent images, which cannot differentiate varieties of image blocks and leads to slow processing speed. Aiming at overcoming this shortage of traditional super-resolution algorithms, image blocks are divided into different categories by local features and each of these categories possesses the corresponding high and low resolution dictionary pairs. Experimental results of different MEMS defects show that the improved algorithm can obtain images of little lower quality with much less processing time, indicating that the proposed algorithm is more suitable for MEMS defect detection.

关键词： image resolution Micromechanical devices Classification algorithms Signal resolution image reconstruction Dictionaries image edge detection

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Collaborative Annotation for Reliable Natural Language processing 1

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丛书名： Focus,,Focus series (London, England)

2016年

作者： Fort Karën

ISBN: (数字)9781119307648

ISBN: (纸本)9781848219045

This book presents a unique opportunity for constructing a consistent image of collaborative manual annotation for Natural Language processing (NLP). NLP has witnessed two major evolutions in the past 25 years: firstly, the extraordinary success of machine learning, which is now, for better or for worse, overwhelmingly dominant in the field, and secondly, the multiplication of evaluation campaigns or shared tasks. Both involve manually annotated corpora, for the training and evaluation of the systems. These corpora have progressively become the hidden pillars of our domain, providing food for our hungry machine learning algorithms and reference for evaluation. Annotation is now the place where linguistics hides in NLP. However, manual annotation has largely been ignored for some time, and it has taken a while even for annotation guidelines to be recognized as essential. Although some efforts have been made lately to address some of the issues presented by manual annotation, there has still been little research done on the subject. This book aims to provide some useful insights into the subject. Manual corpus annotation is now at the heart of NLP, and is still largely unexplored. There is a need for manual annotation engineering (in the sense of a precisely formalized process), and this book aims to provide a first step towards a holistic methodology, with a global view on annotation.

关键词： acronyms xi introduction list ix annotation process weight context tasks annotation prototypes annotation challenges new metrics crowdsourcing recent phenomenon nonexperts crowd

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基于指数律分布的图像快速盲复原算法

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光子学报 2016年第12期45卷 128-135页

作者：高伟哲邹建华张荣之王家松西安交通大学电信学院系统工程研究所西安710049 广东西安交通大学研究院广东佛山528300 西安卫星测控中心西安710043

针对现有图像盲复原迭代算法多存在耗时较长和难以保证收敛性等问题,提出一种改进的快速算法.首先根据指数律重建原始图像的频谱,然后利用原始图像和降质图像的频谱关系,采用多方向综合估计方法得到点扩散函数.多方向综合估计方法可降... 详细信息

针对现有图像盲复原迭代算法多存在耗时较长和难以保证收敛性等问题,提出一种改进的快速算法.首先根据指数律重建原始图像的频谱,然后利用原始图像和降质图像的频谱关系,采用多方向综合估计方法得到点扩散函数.多方向综合估计方法可降低估计误差,增加算法的稳定性.最后利用得到的点扩散函数和维纳滤波法进行图像重建.与现有算法的对比实验结果表明,针对适合大量成像系统的G类点扩散函数,本算法可以得到更准确的点扩散函数估计,且降低了振铃效应的影响,取得更好的图像复原效果.

关键词：图像处理图像盲复原指数律点扩散函数估计 APEX算法振铃效应

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Predicting Transmembrane β-barrel Segments with Chain Learning and Sparse Coding

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IFAC-PapersOnLine 2015年第28期48卷 245-250页

作者： xi, Yin Hong-Bin, Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China

We developed a novel method named MemBrain-TMB to predict the spanning segments of transmembrane Â-barrel from amino acid sequence. MemBrain-beta is a statistical machine learningbased model, which is constructed using a new chain learning algorithm with the input features are encoded by the image sparse representation approach. To deal with the diverse loop length problem, we applied a dynamic threshold method, which is particularly useful for enhancing the recognition of short loops and tight turns. MemBrain-TMB achieves a Q2 accuracy of 93% and SOV of 97% on the benchmark dataset, which is 5%~10% higher than other existing predictors. © 2015

关键词： Learning algorithms Forecasting image processing Learning systems Machine learning Pattern recognition Amino acid sequence Benchmark datasets Dynamic threshold Evaluation Input features Prediction methods Sparse representation Transmembranes

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