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distributed travel-time seismic tomography in large-scale sensor networks

JOURNAL OF parallel AND distributed COMPUTING 2016年 89卷 50-64页

作者： Kamath, Goutham Shi, Lei Song, Wen-Zhan Lees, Jonathan Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Univ N Carolina Dept Geol Sci Chapel Hill NC USA

Current geophysical techniques for visualizing seismic activity employ image reconstruction methods that rely on a centralized approach for processing the raw data captured by seismic sensors. The data is either gathered manually, or relayed by expensive broadband stations, and then processed at a base station. This approach is time-consuming (weeks to months) and hazardous as the task involves manual data gathering in extreme conditions. Also, raw seismic samples are typically in the range of 16-24 bit, sampled at 50-200 Hz and transferring this high fidelity sample from large number of sensors to a centralized station results in a bottleneck due to bandwidth limitations. To avoid these issues, a new distributed method is required which processes raw seismic samples inside each node and obtains a high resolution seismic tomography in real time. In this paper, we present a component-averaged distributed multi-resolution evolving tomography algorithm for processing data and inverting volcano tomography in the network while avoiding centralized computation and costly data collection. The algorithm is first evaluated for the correctness using a synthetic model in a CORE emulator. Later, our proposed algorithm runs using the real data obtained from Mt. St. Helens, WA, USA. The results validate that our distributed algorithm is able to obtain a satisfactory image similar to centralized computation under constraints of network resources, while distributing the computational burden to sensor nodes. (C) 2015 Elsevier Inc. All rights reserved.

关键词： distributed computing Seismic tomography Sensor network Mt. St. Helens

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Location-Based parallel Tag Completion for Geo-Tagged Social image Retrieval

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2017年第3期8卷 38-38页

作者： Zhang, Jiaming Wang, Shuhui Huang, Qingming Chinese Acad Sci Key Lab Intellectual Informat Proc Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

Having benefited from tremendous growth of user-generated content, social annotated tags get higher importance in the organization and retrieval of large-scale image databases on Online Sharing Websites (OSW). To obtain high-quality tags from existing community contributed tags with missing information and noise, tag-based annotation or recommendation methods have been proposed for performance promotion of tag prediction. While images from OSW contain rich social attributes, they have not taken full advantage of rich social attributes and auxiliary information associated with social images to construct global information completion models. In this article, beyond the image-tag relation, we take full advantage of the ubiquitous GPS locations and image-user relationship to enhance the accuracy of tag prediction and improve the computational efficiency. For GPS locations, we define the popular geo-locations where people tend to take more images as Points of Interests (POI), which are discovered by mean shift approach. For image-user relationship, we integrate a localized prior constraint, expecting the completed tag sub-matrix in each POI to maintain consistency with users' tagging behaviors. Based on these two key issues, we propose a unified tag matrix completion framework, which learns the image-tag relation within each POI. To solve the optimization problem, an efficient proximal sub-gradient descent algorithm is designed. The model optimization can be easily parallelized and distributed to learn the tag sub-matrix for each POI. Extensive experimental results reveal that the learned tag sub-matrix of each POI reflects the major trend of users' tagging results with respect to different POIs and users, and the parallel learning process provides strong support for processing large-scale online image databases. To fit the response time requirement and storage limitations of Tag-based image Retrieval (TBIR) on mobile devices, we introduce Asymmetric Locality Sensitive H

关键词： Tag matrix completion geo-location information social image retrieval asymmetric locality sensitive hashing

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Stereo Matching by Using Self-distributed Segmentation and Massively parallel GPU Computing 15th

Stereo Matching by Using Self-distributed Segmentation and M...

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15th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Qiao, Wenbao Creput, Jean-Charles Univ Technol Belfort Montbeliard IRTES SET Belfort France

ISBN: (纸本)9783319393841

As an extension of using image segmentation to do stereo matching, firstly, by using self-organizing map (som) and K-means algorithms, this paper provides a self-distributed segmentation method that allocates segments according to image's texture changement where in most cases depth discontinuities appear. Then, for stereo, under the fact that the segmentation of left image is not exactly same with the segmentation of right image, we provide a matching strategy that matches segments of left image to pixels of right image as well as taking advantage of border information from these segments. Also, to help detect occluded regions, an improved aggregation cost that considers neighbor valid segments and their matching characteristics is provided. For post processing, a gradient border based median filter that considers the closest adjacent valid disparity values instead of all pixels' disparity values within a rectangle window is provided. As we focus on real-time execution, these time-consumming works for segmentation and stereo matching are executed on a massively parallel cellular matrix GPU computing model. Finaly, we provide our visual dense disparity maps before post processing and final evaluation of sparse results after post-processing to allow comparison with several ranking methods top listed on Middlebury.

关键词： Stereo image segmentation SOM Self-distributed segments

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Bilateral Histogram Equalization with Pre-processing for Contrast Enhancement 17

Bilateral Histogram Equalization with Pre-processing for Con...

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17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD)

作者： Amil, Feroz Mahmud Rahman, Md Mostafijur Rahman, Shanto Dey, Emon Kumar Shoyaib, Mohammad Univ Dhaka Inst Informat Technol Dhaka Bangladesh

ISBN: (纸本)9781509022397

Various methods have been proposed for enhancing the images. Some of those perform well in some specific application areas but most of the techniques suffer from artifacts due to over enhancement. To overcome this problem, we have introduced a new image enhancement technique namely Bilateral Histogram Equalization with Pre-processing (BHEP) which uses Harmonic mean to divide the histogram of the image. We have performed both qualitative and quantitative measurements for experiments and the results show that BHEP creates less artifacts in several standard images than the existing state-of-the-art image enhancement techniques.

关键词： Contrast enhancement Histogram equalization Harmonic mean

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Denoising model for parallel magnetic resonance imaging images using higher-order Markov random fields

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IET image processing 2016年第12期10卷 962-970页

作者： Xu, Zhihuo Shi, Quan Nantong Univ Sch Transportat Image Res Grp Nantong 226001 Peoples R China

This study presents a novel robust method for Bayesian denoising of parallel magnetic resonance imaging (pMRI) images. For the first time, the authors' proposal applies fields of experts (FoE), a filter-based higher-order Markov random field (MRF), to model the prior of the pMRI image statistics. The noise in pMRI data behaves to be non-central Chi (nc-chi) distributed. In practice, correlation between coils exists, resulting in that nc-. distribution does not hold anymore and the spatially varying noise problem. Thus, preservation of fine textures requires to adapt locally the estimation. Therefore, more precisely, the noise is reduced by using a sliding window scheme. In each window, the likelihood probability function is accurately modelled from corrupted data by using an innovative Gaussian mixture model (GMM). The parameters of GMM are calculated by applying an iterative expectation maximisation approach. With the priors via the learned FoE model and the likelihood function via GMM, a maximum a posteriori (MAP) estimator is formulated. Then, the noise in the each window is filtered by applying an efficient nonlinear quasi-Newton method to explore an optimal solution for the MAP estimator. Finally, experiments have been conducted on both the simulated and real data to compare the proposed model with some state-of-the-art denoising methods. The experimental results demonstrate the robustness and effectiveness of the proposed denoising model.

关键词： Likelihood Functions corrupted data sliding windows Noise reduction Maximum a posteriori estimation Markov random fields optimal solution Gm(m)

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Learning non-local image diffusion for image denoising

arXiv

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arXiv 2017年

作者： Qiao, Peng Dou, Yong Feng, Wensen Chen, Yunjin National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha410073 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing

image diffusion plays a fundamental role for the task of image denoising. Recently proposed trainable nonlinear reaction diffusion (TNRD) model defines a simple but very effective framework for image denoising. However, as the TNRD model is a local model, the diffusion behavior of which is purely controlled by information of local patches, it is prone to create artifacts in the homogenous regions and over-smooth highly textured regions, especially in the case of strong noise levels. Meanwhile, it is widely known that the non-local selfsimilarity (NSS) prior stands as an effective image prior for image denoising, which has been widely exploited in many nonlocal methods. In this work, we are highly motivated to embed the NSS prior into the TNRD model to tackle its weaknesses. In order to preserve the expected property that end-to-end training is available, we exploit the NSS prior by a set of non-local filters, and derive our proposed trainable non-local reaction diffusion (TNLRD) model for image denoising. Together with the local filters and influence functions, the non-local filters are learned by employing loss-specific training. The experimental results show that the trained TNLRD model produces visually plausible recovered images with more textures and less artifacts, compared to its local versions. Moreover, the trained TNLRD model can achieve strongly competitive performance to recent state-of-theart image denoising methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Copyright © 2017, The Authors. All rights reserved.

关键词： image denoising

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A Review of Indoor-Outdoor Scene Classification

A Review of Indoor-Outdoor Scene Classification

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2017 2nd International Conference on Control, Automation and Artificial Intelligence(CAAI2017)

作者： Zhehang Tong Dianxi Shi Bingzheng Yan Jing Wei National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology

ISBN: (纸本)9781510845541

Indoor-Outdoor scene classification problem have been proposed for almost 20 years and widely applied to general scene classification, image retrieval, image processing and robot application. But there is no consensus on one particular scene classification technique that can solve the Indoor-Outdoor scene classification problem perfectly. As larger image dataset has been developed and machine learning technology especially deep learning based methods achieve remarkable performance in computer vision, we aim to provide guidance and direction for researchers to tackle the Indoor-Outdoor scene classification problem with more powerful and robust solution through concluding the Indoor-Outdoor scene classification approaches which have been proposed in last 20 years. In this paper, we review the Indoor-Outdoor scene classification including feature extraction, classifier and related dataset. Their advantages and disadvantages are discussed. At last we conclude some challenging problems remain unsolved and propose some potential solutions.

关键词： indoor-outdoor scene classification computer vision

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parallelized deblocking filtering of HEVC decoders based on complexity estimation

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JOURNAL OF REAL-TIME image processing 2016年第2期12卷 369-382页

作者： Jo, Hyunho Park, Seanae Sim, Donggyu Kwangwoon Univ Dept Comp Engn 20 Gwangun Ro Seoul South Korea

This paper proposes a parallelization method for a deblocking filter in a high-efficiency video coding (HEVC) decoder based on complexity estimation. A deblocking filter of HEVC is generally considered to be appropriate for data-level parallelism (DLP) because there are no data-level dependencies among adjacent blocks in the horizontal and vertical filtering processes. However, an imbalanced workload can increase the idle time on some of the threads, and thus, the maximum parallel performance cannot be achieved with only DLP. To alleviate this problem, the proposed method estimates the computational complexity by utilizing the coding unit (CU) segment information and the on/off flag of the deblocking filter in advance. Then, the workload is distributed equally across all threads. The experimental results indicate that the proposed method can accelerate the decoding speed by a factor of 3.97, with six threads on top of the sequential deblocking filtering. In addition, the ratio of the maximum elapsed time to ideal elapsed time is reduced to approximately 21 %, as compared to conventional DLP-based parallel deblocking filtering methods that do not implement a complexity estimation method.

关键词： HEVC Deblocking filter parallel decoding Workload imbalance

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High Performance Implementation of 3D Convolutional Neural Networks on a GPU

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017年第1期2017卷 1-8页

作者： Lan, Qiang Wang, Zelong Wen, Mei Zhang, Chunyuan Wang, Yijie Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China Natl Key Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version.

关键词： NEURAL networks (Computer science) GRAPHICS processing units (Computers) CONVOLUTION (Signal processing) HIGH performance computing FAST Fourier transforms

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Scalable splitting algorithms for big-data interferometric imaging in the SKA era

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MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2016年第4期462卷 4314-4335页

作者： Onose, Alexandru Carrillo, Rafael E. Repetti, Audrey McEwen, Jason D. Thiran, Jean-Philippe Pesquet, Jean-Christophe Wiaux, Yves Heriot Watt Univ Inst Sensors Signals & Syst Edinburgh EH14 4AS Midlothian Scotland Ecole Polytech Fed Lausanne Signal Proc Lab LTS5 CH-1015 Lausanne Switzerland Univ Coll London Mullard Space Sci Lab Surrey RH5 6NT England Univ Paris Est Lab Informat Gaspard Monge F-77454 Marne La Vallee France

In the context of next-generation radio telescopes, like the Square Kilometre Array (SKA), the efficient processing of large-scale data sets is extremely important. Convex optimization tasks under the compressive sensing framework have recently emerged and provide both enhanced image reconstruction quality and scalability to increasingly larger data sets. We focus herein mainly on scalability and propose two new convex optimization algorithmic structures able to solve the convex optimization tasks arising in radio-interferometric imaging. They rely on proximal splitting and forward-backward iterations and can be seen, by analogy, with the CLEAN major-minor cycle, as running sophisticated CLEAN-like iterations in parallel in multiple data, prior, and image spaces. Both methods support any convex regularization function, in particular, the well-studied l(1) priors promoting image sparsity in an adequate domain. Tailored for big-data, they employ parallel and distributed computations to achieve scalability, in terms of memory and computational requirements. One of them also exploits randomization, over data blocks at each iteration, offering further flexibility. We present simulation results showing the feasibility of the proposed methods as well as their advantages compared to state-of-the-art algorithmic solvers. Our MATLAB code is available online on GitHub.

关键词： techniques: image processing techniques: interferometric

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