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IEEE International Geoscience and Remote Sensing Symposium

作者： WenKang Liu MengDao Xing Guang-Cai Sun Xidian University Xi’an P. R. China

Ship oscillatory motions with long coherence processing interval (CPI) are complex and hard to accurately estimate. The traditional ship imaging methods usually avoid long-CPI focusing by dealing with a short observation time interval, which may make it hard to obtain a high-resolution and high-SNR image. In this paper, the mechanisms and challenges of long CPI imaging of oscillatory targets are investigated. The properties of wavenumber domain support (WDS) and point spreading function (PSF) of oscillatory targets are analyzed. It's found that the WDS spreads as a three-dimensional (3D) thin and curved sweep surface, with time-variant energy density, non-parallel boundaries and a complex structure. The PSF of an oscillatory target has a 3D resolution, but also multiple side-lobes with high level. We inspect the relationships between the properties of the WDS and the non-ideal PSF. Moreover, it's found that scatters distributed on a 3D oscillatory target cannot be focused uniformly on a predefined imaging plane (IP). The projection relationship of the target focusing positions on the slant-range plane (SRP) and the IP are also derived. The simulation results can well validate the proposed method.

关键词： Marine vehicles Three-dimensional displays IP networks Radar imaging Focusing

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Appraisal of Pre-processing Techniques for Automated Detection of Diabetic Retinopathy

Appraisal of Pre-processing Techniques for Automated Detecti...

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International Conference on parallel distributed and Grid Computing (PDGC)

作者： Charu Bhardwaj Shruti Jain Meenakshi Sood Department of ECE Jaypee University of Information Technology H.P India

ISBN: (纸本)9781728106489

Retinopathy is a retinal vascular disease caused by abnormal blood flow that damages the retina and it accounts for 5% of blindness worldwide. There are different causes of Retinopathy as retinopathy of prematurity, diabetic retinopathy and hypertensive retinopathy; this paper mainly focuses on pre-processing techniques employed for the detection of diabetic retinopathy. In this work, literatures reporting different methods for automated diabetic retinopathy detection in color images of retina are analyzed for their strength and shortcoming. Extensive techniques for retinal image screening have been published so far thus this paper presents the evaluation of these methods on a common database of image screening. Different image pre-processing techniques are implemented on fundus images and their performance is compared using performance metrics like mean square error, peak signal to noise ratio and root mean square error. Pre-processing steps used in this work can also be applied to other images of interest to identify and recognize the objects in an image.

关键词： Retinopathy Diabetes Histograms Retina Databases Blood vessels Biomedical imaging

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An improved distributed storage and query for remote sensing data

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Procedia Computer Science 2018年 129卷 238-247页

作者： Weipeng Jing Dongxue Tian College of information and computer engineering Northeast Forestry University Harbin 150040 China

With the rapid development of information technology, the amount of remote sensing data is increasing at an unprecedented scale. In the presence of massive remote sensing data, the traditional processing methods have the problems of low efficiency and lack of scalability, so this paper uses open source big data technology to improve it. Firstly, the storage model of remote sensing image data is designed by using the distributed storage database HBase. Then, the grid index and the Hibert curve are combined to establish the index for the image data. Finally, the method of MapReduce parallel processing is used to write and query remote sensing images. The experimental results show that the method can effectively improve the data writing and query speed, and has good scalability.

关键词： remote data distribute storage data query HBase mapreduce

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parallel and distributed methods for Constrained Nonconvex Optimization-Part ii: Applications in Communications and Machine Learning

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IEEE TRANSACTIONS ON SIGNAL processing 2017年第8期65卷 1945-1960页

作者： Scutari, Gesualdo Facchinei, Francisco Lampariello, Lorenzo Sardellitti, Stefania Song, Peiran Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA Univ Roma La Sapienza Dept Comp Control & Management Engn I-00184 Rome Italy Univ Rome Tre Dept Business Studies I-00154 Rome Italy Univ Roma La Sapienza Dept Informat Elect & Telecommun I-00184 Rome Italy Beijing Informat Sci & Technol Univ Sch Informat & Commun Engn Beijing 100101 Peoples R China

In Part I of this paper, we proposed and analyzed a novel algorithmic framework for the minimization of a nonconvex objective function, subject to nonconvex constraints, based on inner convex approximations. This Part ii is devoted to the (nontrivial) application of the framework to the following relevant large-scale problems ranging from communications to machine learning: 1) (generalizations of) the rate profile maximization in MIMO interference broadcast networks;2) the max-min fair multicast multigroup beamforming problem in a multicell environment;and 3) a general nonconvex constrained bi-criteria formulation for k-sparse variable selection in statistical learning;the two criteria are a nonconvex loss objective function, measuring the fitness of the model to data, and the latter is a nonconvex sparsity-inducing constraint in the general form of difference-ofconvex (DC) functions, which allows to accomodate in a unified fashion convex and nonconvex surrogates of the lo function. The proposed algorithms outperform current state-of-the-art schemes for 1)-3) both theoretically and numerically. For instance, they are the first distributed schemes for the class of problems 1) and 2);and they also lead to subproblems enjoying closed form solutions.

关键词： Interference networks multicast beamforming nonconvex optimization statistical learning successive convex approximation

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Content-based image Retrieval in Medical Applications

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methods of Information in Medicine 2018年第4期43卷 354-361页

作者： T. M. Lehmann M. O. Güld C. Thies B. Fischer K. Spitzer D. Keysers H. Ney M. Kohnen H. Schubert B. B. Wein

Objectives: To develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its efficient implementation. methods: Stepwise content analysis of medical images results in six layers of information modeling incorporating medical expert knowledge (raw data layer, registered data layer, feature layer, scheme layer, object layer, knowledge layer). A reference database with 10,000 images categorized according to the image modality, orientation, body region, and biological system is used. By means of prototypes in each category, identification of objects and their geometrical or temporal relationships are handled in the object and the knowledge layer, respectively. A distributed system designed with only three core elements is implemented: (i) the central database holds program sources, processing scheme descriptions, images, features, and administrative information about the workstation cluster; (ii) the scheduler balances distributed computing; and (iii) the web server provides graphical user interfaces for data entry and retrieval, which can be easily adapted to a variety of applications for content-based image retrieval in medicine. Results: Leaving-one-out experiments were distributed by the scheduler and controlled via corresponding job lists offering transparency regarding the viewpoints of a distributed system and the user. The proposed architecture is suitable for content-based image retrieval in medical applications. It improves current picture archiving and communication systems that still rely on alphanumerical descriptions, which are insufficient for image retrieval of high recall and precision.

关键词： Information storage and retrieval information management pattern recognition computer assisted image processing

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An OpenCL framework for high performance extraction of image features

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JOURNAL OF parallel AND distributed COMPUTING 2017年 109卷 75-88页

作者： de Andrade, Douglas Coimbra Trabasso, Luis Gonzaga Petroleo Brasileiro SA Sao Paulo Brazil Aeronaut Inst Technol Mech Engn Div Sao Jose Dos Campos Brazil

image features are widely used for object identification in many situations, including interpretation of data containing natural scenes captured by unmanned aerial vehicles. This paper presents a parallel framework to extract additive features (such as color features and histogram of oriented gradients) using the processing power of GPUs and multicore CPUs to accelerate the algorithms with the OpenCL language. The resulting features are available in device memory and then can be fed into classifiers such as SVM, logistic regression and boosting methods for object recognition. It is possible to extract multiple features with better performance. The GPU accelerated image integral algorithm speeds up computations up to 35x when compared to the single-thread CPU implementation in a test bed hardware. The proposed framework allows real-time extraction of a very large number of image features from full-HD images (better than 30 fps) and makes them available for access in coalesced order by GPU classification algorithms. (C) 2017 Elsevier Inc. All rights reserved.

关键词： OpenCL Heterogeneous programming image descriptors Additive features Haar features Histogram of oriented gradients parallel processing

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GPU Implementation of Bitplane Coding with parallel Coefficient processing for High Performance image Compression

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2017年第8期28卷 2272-2284页

作者： Enfedaque, Pablo Auli-Llinas, Francesc Moure, Juan Carlos Univ Autonoma Barcelona Dept Informat & Commun Engn E-08193 Barcelona Spain Univ Autonoma Barcelona Dept Comp Architecture & Operating Syst E-08193 Barcelona Spain

The fast compression of images is a requisite in many applications like TV production, teleconferencing, or digital cinema. Many of the algorithms employed in current image compression standards are inherently sequential. High performance implementations of such algorithms often require specialized hardware like field integrated gate arrays. Graphics processing Units (GPUs) do not commonly achieve high performance on these algorithms because they do not exhibit fine-grain parallelism. Our previous work introduced a new core algorithm for wavelet-based image coding systems. It is tailored for massive parallel architectures. It is called bitplane coding with parallel coefficient processing (BPC-PaCo). This paper introduces the first high performance, GPU-based implementation of BPC-PaCo. A detailed analysis of the algorithm aids its implementation in the GPU. The main insights behind the proposed codec are an efficient thread-to-data mapping, a smart memory management, and the use of efficient cooperation mechanisms to enable inter-thread communication. Experimental results indicate that the proposed implementation matches the requirements for high resolution (4 K) digital cinema in real time, yielding speedups of 30x with respect to the fastest implementations of current compression standards. Also, a power consumption evaluation shows that our implementation consumes 40x less energy for equivalent performance than state-of-the-art methods.

关键词： image coding SIMD computing graphics processing unit (GPU) compute unified device architecture (CUDA)

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EIP-VIO: Edge-Induced Points Based Monocular Visual-Inertial Odometry

EIP-VIO: Edge-Induced Points Based Monocular Visual-Inertial...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Luoying Hao Hongjian Li Jun Cheng Qieshi Zhang Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China The Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)9781728152103

Edge and line features boost the performance of simultaneous localization and mapping (SLAM) systems while bring more additional computational cost. In this paper, we propose an improved and practical monocular visual-inertial odometry method based on selective edge points. Focus on solving the issues of mismatching and tracking loss, the auxiliary edge is utilized to supervise the selection of features instead of using rough features directly. Features along edges are employed as stronger keypoints to steer point-selection away from less distinctive points. By combining matched edge-guided feature based method, selective sampling keypoints along edges based direct method and inertial measurement unit (IMU), the performance of absolute pose estimation is more accurate and efficient. In addition, a weight adjustment factor scheme before IMU error term is introduced in our optimization model to further reduce the influence of motion blur and less texture. Experimental results on public benchmark dataset demonstrate that the proposed method is more efficient and effective compared with the state-of-the-art methods.

关键词： distance measurement edge detection feature extraction image matching pose estimation SLAM (robots) pose estimation distance measurement image matching Edge detection Feature extraction motion blur odometry MISMATCH simultaneous localization and mapping edges Edge of a figure or solid

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Universal enzymatic numerical P systems with small number of enzymatic variables

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Science China(Information Sciences) 2018年第9期61卷 38-49页

作者： Zhiqiang ZHANG Tingfang WU ANDrei P■UN Linqiang PAN Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Department of Computer Science Faculty of Mathematics and Computer ScienceUniversity of Bucharest Bioinformatics Department National Institute of Research and Development for Biological Sciences School of Electric and Information Engineering Zhengzhou University of Light Industry

Numerical P systems(for short, NP systems) are distributed and parallel computing models inspired from the structure of living cells and economics. Enzymatic numerical P systems(for short, ENP systems) are a variant of NP systems, which have been successfully applied in designing and implementing controllers for mobile robots. Since ENP systems were proved to be Turing universal, there has been much work to simplify the universal systems, where the complexity parameters considered are the number of membranes, the degrees of polynomial production functions or the number of variables used in the *** the number of enzymatic variables, which is essential for ENP systems to reach universality, has not been investigated. Here we consider the problem of searching for the smallest number of enzymatic variables needed for universal ENP systems. We prove that for ENP systems as number acceptors working in the all-parallel or one-parallel mode, one enzymatic variable is sufficient to reach universality; while for the one-parallel ENP systems as number generators, two enzymatic variables are sufficient to reach *** results improve the best known results that the numbers of enzymatic variables are 13 and 52 for the all-parallel and one-parallel systems, respectively.

关键词： bio-inspired computing numerical P system mobile robot membrane controller universality

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A Composite Model of Wound Segmentation Based on Traditional methods and Deep Neural Networks

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018年第1期2018卷 4149103-4149103页

作者： Li, Fangzhao Wang, Changjian Liu, Xiaohui Peng, Yuxing Jin, Shiyao Sci & Technol Parallel & Distributed Lab Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data arc required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment.

关键词： WOUND care TISSUE analysis ELECTRONIC health records image processing NEURAL networks (Computer science)

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