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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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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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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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A distributed parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral images

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SENSORS 2018年第11期18卷 3627-3627页

作者： Zhang, Yi Wu, Zebin Sun, Jin Zhang, Yan Zhu, Yaoqin Liu, Jun Zang, Qitao Plaza, Antonio Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Lianyungang E Port Informat Dev Co Ltd Lianyungang 222042 Peoples R China Univ Extremadura Dept Technol Computers & Commun Hyperspectral Comp Lab Caceres 10003 Spain

Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributed parallel algorithm (DPA) by redesigning key operators of LRASR in terms of MapReduce model to accelerate LRASR on cloud computing architectures. Independent computation operators are explored and executed in parallel on Spark. Specifically, we reconstitute the hyperspectral images in an appropriate format for efficient DPA processing, design the optimized storage strategy, and develop a pre-merge mechanism to reduce data transmission. Besides, a repartitioning policy is also proposed to improve DPA's efficiency. Our experimental results demonstrate that the newly developed DPA achieves very high speedups when accelerating LRASR, in addition to maintaining similar accuracies. Moreover, our proposed DPA is shown to be scalable with the number of computing nodes and capable of processing big hyperspectral images involving massive amounts of data.

关键词： hyperspectral images anomaly detection distributed and parallel computing apache spark clouds

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Investigation of image processing Techniques for Glaucoma Detection in Human Eyes

Investigation of Image Processing Techniques for Glaucoma De...

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International Conference on parallel distributed and Grid Computing (PDGC)

作者： Shilpa Kaushal Sunil Datt Sharma Shruti Jain Dept. of Electronics & Comm. Engg. Jaypee University of Information Technology Himachal Pradesh India

ISBN: (纸本)9781728106489

Glaucoma is a disease associated with retina of eye. Presently, millions of human being is suffering from this disease. Early detection of these diseases can save the people from blindness. Therefore, various methods have been developed for its detection. In this paper, we have studied the reported methods and summarized their performance in terms of accuracy of detection.

关键词： Feature extraction Databases Optical imaging Support vector machines Transforms Retina Biomedical imaging

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Unsupervised Learning-based Depth Estimation aided Visual SLAM Approach

arXiv

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arXiv 2019年

作者： Geng, Mingyang Shang, Suning Ding, Bo Wang, Huaimin Zhang, Pengfei Zhang, Lei National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology China National Key Laboratory of Integrated Automation of Process Industry Northeastern University China

Existing visual-based SLAM systems mainly utilize the three-dimensional environmental depth information from RGB-D cameras to complete the robotic synchronization localization and map construction task. However, the RGB-D camera maintains a limited range for working and is hard to accurately measure the depth information in a far distance. Besides, the RGB-D camera will easily be influenced by strong lighting and other external factors, which will lead to a poor accuracy on the acquired environmental depth information. Recently, deep learning technologies have achieved great success in the visual SLAM area, which can directly learn high-level features from the visual inputs and improve the estimation accuracy of the depth information. Therefore, deep learning technologies maintain the potential to extend the source of the depth information and improve the performance of the SLAM system. However, the existing deep learning-based methods are mainly supervised and require a large amount of ground-truth depth data, which is hard to acquire because of the realistic constraints. In this paper, we first present an unsupervised learning framework, which not only uses image reconstruction for supervising but also exploits the pose estimation method to enhance the supervised signal and add training constraints for the task of monocular depth and camera motion estimation. Furthermore, we successfully exploit our unsupervised learning framework to assist the traditional ORB-SLAM system when the initialization module of ORB-SLAM method could not match enough features. Qualitative and quantitative experiments have shown that our unsupervised learning framework performs the depth estimation task comparably to the supervised methods and outperforms the previous state-of-the-art approach by 13.5% on KITTI dataset. Besides, our unsupervised learning framework could significantly accelerate the initialization process of ORB-SLAM system and effectively improve the accuracy on environme

关键词： Unsupervised learning

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distributed Particle Metropolis-Hastings Schemes

Distributed Particle Metropolis-Hastings Schemes

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IEEE/SP Workshop on Statistical Signal processing (SSP)

作者： Luca Martino Víctor Elvira Gustau Camps-Valls Image Processing Laboratory Universitat de València Spain IMT Lille Douai CRISTAL (UMR 9189) Villeneuve d’Ascq France

We introduce a Particle Metropolis-Hastings algorithm driven by several parallel particle filters. The communication with the central node requires the transmission of only a set of weighted samples, one per filter. Furthermore, the marginal version of the previous scheme, called distributed Particle Marginal Metropolis-Hastings (DPMMH) method, is also presented. DPMMH can be used for making inference on both a dynamical and static variable of interest. The ergodicity is guaranteed, and numerical simulations show the advantages of the novel schemes.

关键词： Proposals Signal processing algorithms Probability density function State-space methods Signal processing Monte Carlo methods Conferences

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