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22nd IEEE International conference on parallel and distributed Systems (ICPADS)

作者： Yao, Zhijun Zhou, Quan Lai, Zhongyuan Ren, Zhiming Liu, Liming China Shipbldg Ind Corp Inst 723 Yangzhou Peoples R China Nanjing Univ Posts & Telecommun Key Lab Minist Educ Broad Band Commun & Sensor Network Te Nanjing Jiangsu Peoples R China Jianghan Univ Inst Interdisciplinary Res Wuhan Peoples R China

ISBN: (纸本)9781509044573

This paper presents a new image enhancement method using histogram equalization called Bi-Histogram Equalization with Non-parametric Modified Technology (BHENMT). Our proposed method consists of three steps: (i) The input original histogram is divided into two parts using the Otsu method. (ii) Then the histogram modification technique is used to control over enhancement and maximize entropy. (iii) Two sub images are enhanced by the tradtional histogram equalization method using the corresponding modified histogram respectively and finally are merged into one output enhanced image. The experimental results show that BHENMT is better than other contrast enhancement methods according to subjective evaluation and various image objective evaluation measures, i.e. Entropy, AMBE and PSNR.

关键词： Histogram modification Histogram equalization image objective evaluation measure Brightness preserving

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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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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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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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Improving Hash distributed A* for Shared Memory Architectures Using Abstraction 16th

Improving Hash Distributed A* for Shared Memory Architecture...

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16th International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Sanz, Victoria De Giusti, Armando Naiouf, Marcelo Natl Univ La Plata Sch Comp Sci III LIDI La Plata Argentina Minist Sci Technol & Product Innovat CONICET Buenos Aires DF Argentina

ISBN: (纸本)9783319495835;9783319495828

The A* algorithm is generally used to solve combinatorial optimization problems, but it requires high computing power and a large amount of memory, hence, efficient parallel A* algorithms are needed. In this sense, Hash distributed A* (HDA*) parallelizes A* by applying a decentralized strategy and a hash-based node distribution scheme. However, this distribution scheme results in frequent node transfers among processors. In this paper, we present Optimized AHDA*, a version of HDA* for shared memory architectures, that uses an abstraction-based node distribution scheme and a technique to group several nodes before transferring them to the corresponding thread. Both methods reduce the amount of node transfers and mitigate communication and contention. We assess the effect of each technique on algorithm performance. Finally, we evaluate the scalability of the proposed algorithm, when it is run on a multicore machine, using the 15-puzzle as a case study.

关键词： HDA* for shared memory architectures Abstraction-based node distribution scheme Hash-based node distribution scheme Scalability Combinatorial optimization problems

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Automatic Detection and Classification of Tuberculosis Bacilli from Camera-enabled Smartphone Microscopic images 4

Automatic Detection and Classification of Tuberculosis Bacil...

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4th IEEE International conference on parallel, distributed and Grid Computing (IEEE PDGC)

作者： Shah, Mi Mishra, S. Sarkar, Malay Sudarshan, S. K. Jaypee Univ Informat Technol Dept Biotechnol & Bioinformat Solon 173215 Himachal Prades India Indira Gandhi Med Coll Shimla 171001 Himachal Prades India

ISBN: (纸本)9781509036691

Sputum smear conventional microscopy (CM) is used as primary bacteriological test for detection of TB. This technique is the most preferred technique in low and middle income countries due to its availability as well as accessibility. Manual screening of bacilli using CM is time consuming and labor intensive. As a result, the sensitivity of TB detection is compromised leading to misdiagnosis 33-50% of active cases. Automated methods can increase the sensitivity and specificity of TB detection. Currently, the remote areas of TB-endemic developing countries have easy accessibility to portable and camera-enabled Smartphone microscope for capturing images from ZN-stained smear slide. In this paper, the performance of watershed segmentation method for detection and classification of bacilli from camera-enable Smartphone microscopic images is presented. Several preprocessing techniques have been implemented prior to watershed segmentation. Current method has achieved the sensitivity and specificity of 93.3% and 87% respectively for classifying an image as TB positive or negative.

关键词： TB diagnosis Bacilli detection Watershed segmentation ZN-stainin Mobile Microscopy image processing

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RPL: A Domain-Specific Language for Designing and Implementing parallel C++ Applications 24

RPL: A Domain-Specific Language for Designing and Implementi...

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24th Euromicro International conference on parallel, distributed, and Network-Based processing, PDP 2016

作者： Janjic, V. Brown, C. Mackenzie, K. Hammond, K. Danelutto, M. Aldinucci, M. Garcia, J. Daniel Uni. of St. Andrews United Kingdom Uni. of Pisa Italy Uni. of Torino Italy Uni. Carlos III Madrid Spain

ISBN: (纸本)9781467387750

parallelising sequential applications is usually a very hard job, due to many different ways in which an application can be parallelised and a large number of programming models (each with its own advantages and disadvantages) that can be used. In this paper, we describe a method to semi-automatically generate and evaluate different parallelisations of the same application, allowing programmers to find the best parallelisation without significant manual reengineering of the code. We describe a novel, high-level domain-specific language, Refactoring Pattern Language (RPL), that is used to represent the parallel structure of an application and to capture its extra-functional properties (such as service time). We then describe a set of RPL rewrite rules that can be used to generate alternative, but semantically equivalent, parallel structures (parallelisations) of the same application. We also describe the RPL Shell that can be used to evaluate these parallelisations, in terms of the desired extra-functional properties. Finally, we describe a set of C++ refactorings, targeting OpenMP, Intel TBB and FastFlow parallel programming models, that semi-automatically apply the desired parallelisation to the application's source code, therefore giving a parallel version of the code. We demonstrate how the RPL and the refactoring rules can be used to derive efficient parallelisations of two realistic C++ use cases (image Convolution and Ant Colony Optimisation). © 2016 IEEE.

关键词： Ant colony optimization

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Proceedings of IA3 2016 - 6th Workshop on Irregular Applications: Architectures and Algorithms, Held in conjunction with SC 2016: The International conference for High Performance Computing, Networking, Storage and Analysis

Proceedings of IA3 2016 - 6th Workshop on Irregular Applicat...

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6th Workshop on Irregular Applications: Architectures and Algorithms, IA3 2016

ISBN: (纸本)9781509038671

The proceedings contain 14 papers. The topics discussed include: highly scalable near memory processing with migrating threads on the emu system architecture;parallel interval stabbing on the automata processor;an optimized multicolor point-implicit solver for unstructured grid applications on graphics processing units;optimizing sparse tensor times matrix on multi-core and many-core architectures;compiler transformation to generate hybrid sparse computations;an OpenCL framework for distributed apps on a multidimensional network of FPGAs;fast parallel cosine K-nearest neighbor graph construction;performance evaluation of parallel sparse tensor decomposition implementations;implementation and evaluation of data-compression algorithms for irregular-grid iterative methods on the PEZY-SC processor;dynamic load balancing for high-performance graph processing on hybrid CPU-GPU platforms;a fast level-set segmentation algorithm for image processing designed for parallel architectures;HISC/R: an efficient hypersparse-matrix storage format for scalable graph processing;optimized distributed work-stealing;and fine-grained parallelism in probabilistic parsing with Habanero Java.

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Group importance sampling for particle filtering and MCMC

arXiv

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arXiv 2017年

作者： Martino, Luca Elvira, Víctor Camps-Valls, Gustau Universidad Carlos III De Madrid Spain Image Processing Laboratory Universitat De València Spain Villeneuve d'Ascq France

Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years. Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In this work, we study the assignation of a single weighted sample which compresses the information contained in a population of weighted samples. Part of the theory that we present as Group Importance Sampling (GIS) has been employed implicitly in different works in the literature. The provided analysis yields several theoretical and practical consequences. For instance, we discuss the application of GIS into the Sequential Importance Resampling framework and show that Independent Multiple Try Metropolis schemes can be interpreted as a standard MetropolisHastings algorithm, following the GIS approach. We also introduce two novel Markov Chain Monte Carlo (MCMC) techniques based on GIS. The first one, named Group Metropolis Sampling method, produces a Markov chain of sets of weighted samples. All these sets are then employed for obtaining a unique global estimator. The second one is the distributed Particle Metropolis-Hastings technique, where different parallel particle filters are jointly used to drive an MCMC algorithm. Different resampled trajectories are compared and then tested with a proper acceptance probability. The novel schemes are tested in different numerical experiments such as learning the hyperparameters of Gaussian Processes, two localization problems in a wireless sensor network (with synthetic and real data) and the tracking of vegetation parameters given satellite observations, where they are compared with several benchmark Monte Carlo techniques. Three illustrative Matlab demos are also provided. Copyright © 2017, The Authors. All rights reserved.

关键词： Importance sampling

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Exploring a distributed Iterative Reconstructor Based on Split Bregman Using PETSc 16th

Exploring a Distributed Iterative Reconstructor Based on Spl...

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16th International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Serrano, Estefania Aa, Tom Vander Wuyts, Roel Blas, Javier Garcia Carretero, Jesus Abella, Monica Univ Carlos III Madrid Spain IMEC ExaSci Life Lab Leuven Belgium IiSGM Madrid Spain

ISBN: (纸本)9783319499567;9783319499550

The proliferation in the last years of many iterative algorithms for Computed Tomography is a result of the need of finding new ways for obtaining high quality images using low dose acquisition methods. These iterative algorithms are, in many cases, computationally much more expensive than traditional analytic ones. Based on the resolution of large linear systems, they normally make use of backprojection and projections operands in an iterative way reducing the performance of the algorithms compared to traditional ones. They are also algorithms that rely on a large quantity of memory because they need of working with large coefficient matrices. As the resolution of the available detectors increase, the size of these matrices starts to be unmanageable in standard workstations. In this work we propose a distributed solution of an iterative reconstruction algorithm with the help of the PETSc library. We show in our preliminary results the good scalability of the solution in one node (close to the ideal one) and the possibilities offered with a larger number of nodes. However, when increasing the number of nodes the performance degrades due to the poor scalability of some fundamental pieces of the algorithm as well as the increase of the time spend in both MPI communication and reduction.

关键词： Computed tomography CT PETSc MPI Iterative reconstruction

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