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6th International Conference on Digital Information, Networking, and Wireless Communications (DINWC)

作者： Atweh, Hanadi Kassem Hamandi, Lama Zekri, Ahmed Zantout, Rached Beirut Arab Univ Dept Math & Comp Sci Beirut Lebanon Amer Univ Beirut Elect & Comp Engn Dept Beirut Lebanon Rafik Hariri Univ Elect & Comp Engn Dept Beirut Lebanon

ISBN: (纸本)9781538639030

Current computers are multi-core, with more than one physical core in one microprocessor chip. Many applications in digital image processing are parallel in nature. Therefore, multi-core processors can be exploited to perform such computations in parallel. In this paper, the standard OpenMP threading library is used to speed-up the edge detection operation on multicore processors. Different partitioning methods of the input image are tested and their effect on the performance of the parallel implementation of the Sobel Edge Detection algorithm is analyzed. It is shown that the horizontal partitioning of the image leads to better performance than vertical partitioning or twodimensional block partitioning. Various numbers of blocks of the image are tested. It is shown that a number of blocks equal to 0.25 the size of the cache line and a number of threads double the number of physical core give the best performance of the parallel Sobel algorithm.

关键词： parallel image processing multithreading Sobel edge detection

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COMBimage: a modular parallel processing framework for pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies

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BMC BIOINFORMATICS 2018年第1期19卷 453-453页

作者： Chantzi, Efthymia Jarvius, Malin Niklasson, Mia Segerman, Anna Gustafsson, Mats G. Uppsala Univ Dept Med Sci Canc Pharmacol & Computat Med Uppsala Sweden Uppsala Univ In Vitro Syst Pharmacol Facil SciLifeLab Drug Discovery & Dev Uppsala Sweden Uppsala Univ Rudbeck Lab Dept Immunol Genet & Pathol Uppsala Sweden

BackgroundLarge-scale pairwise drug combination analysis has lately gained momentum in drug discovery and development projects, mainly due to the employment of advanced experimental-computational pipelines. This is fortunate as drug combinations are often required for successful treatment of complex diseases. Furthermore, most new drugs cannot totally replace the current standard-of-care medication, but rather have to enter clinical use as add-on treatment. However, there is a clear deficiency of computational tools for label-free and temporal image-based drug combination analysis that go beyond the conventional but relatively uninformative end point *** is a fast, modular and instrument independent computational framework for in vitro pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies. Jointly with automated analyses of temporal changes in cell morphology and confluence, it performs and displays conventional cell viability and synergy end point analyses. The image processing algorithms are parallelized using Google's MapReduce programming model and optimized with respect to method-specific tuning parameters. COMBimage is shown to process time-lapse microscopy movies from 384-well plates within minutes on a single quad core personal *** framework was employed in the context of an ongoing drug discovery and development project focused on glioblastoma multiforme;the most deadly form of brain cancer. Interesting add-on effects of two investigational cytotoxic compounds when combined with vorinostat were revealed on recently established clonal cultures of glioma-initiating cells from patient tumor samples. Therapeutic synergies, when normal astrocytes were used as a toxicity cell model, reinforced the pharmacological interest regarding their potential clinical *** enables, for the first time, fast and optimized pairwise drug combination analyses of temporal ch

关键词： Time-lapse video microscopy Label-free Drug combination analysis Therapeutic synergy MapReduce parallel image processing Systematic parameter optimization Glioblastoma multiforme

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Hybrid multi-GPU computing: accelerated kernels for segmentation and object detection with medical image processing applications

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JOURNAL OF REAL-TIME image processing 2017年第1期13卷 227-244页

作者： Graca, Carlos Falcao, Gabriel Figueiredo, Isabel N. Kumar, Sunil Univ Coimbra Inst Telecomunicacoes Fac Sci & Technol Dept Elect & Comp Engn P-3030290 Coimbra Portugal Univ Coimbra Dept Math CMUC P-3001501 Coimbra Portugal Natl Inst Technol Delhi Dept Appl Sci Delhi 110040 India

In the last two decades, we have seen an amazing development of image processing techniques targeted for medical applications. We propose multi-GPU-based parallel real-time algorithms for segmentation and shape-based object detection, aiming at accelerating two medical image processing methods: automated blood detection in wireless capsule endoscopy (WCE) images and automated bright lesion detection in retinal fundus images. In the former method we identified segmentation and object detection as being responsible for consuming most of the global processing time. While in the latter, as segmentation was not used, shape-based object detection was the compute-intensive task identified. Experimental results show that the accelerated method running on multi-GPU systems for blood detection in WCE images is on average 265 times faster than the original CPU version and is able to process 344 frames per second. By applying the multi-GPU framework for bright lesion detection in fundus images we are able to process 62 frames per second with a speedup average 667 times faster than the equivalent CPU version.

关键词： Segmentation Shape-based object detection Wireless capsule endoscopy Fundus images Automated diagnosis parallel image processing Multi-GPU Systems

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Developing the Algorithm Allowing Business-Dedicated Mobile Applications to Read Texts

Developing the Algorithm Allowing Business-Dedicated Mobile ...

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International Conference on Quality Management,Transport and Information Security, Information Technologies (IT and QM and IS)

作者： Kaznin, A. A. Sushko, O. P. Babkin, A. V. Northern Arctic Fed Univ Arkhangelsk Russia Grad Sch Ind Management & Econ St Petersburg Russia

ISBN: (纸本)9781538607039

The article looks at the current state of the mobile apps market and its potential to generate solutions for B2C and B2B segments. It also explores the issue of how mobile devices can be made capable of recognizing printed text containing images. Technologies are described for mobile application implementation. The article further explains how signal rank processing algorithm can be used to improve text recognition accuracy. Experimental evidence is provided of the expediency of this algorithm for use as a text recognition means on mobile devices.

关键词： mobile application OCR image processing signal rank processing algorithm parallel image processing

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parallel Computing for Fast and Accurate Phase Analysis of Fringe Pattern by Two-Dimensional Phase-Shifting Methods

Parallel Computing for Fast and Accurate Phase Analysis of F...

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International Conference on Optical and Photonics Engineering (icOPEN)

作者： Ri, Shien Wang, Qinghua Tsuda, Hiroshi Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan

ISBN: (数字)9781510610026

ISBN: (纸本)9781510610019;9781510610026

Phase analysis techniques of fringe patterns have been widely used for noncontact three-dimensional shape and deformation measurement by the fringe projection method. Recently, we developed two novel accurate phase analysis methods. One is the two-dimensional sampling moire method to perform robust phase analysis for a single-shot fringe pattern. The other is the two-dimensional spatiotemporal phase-shifting method to analyze phase distribution accurately for multi-step phase-shifted fringe patterns. To perform accurate phase analysis under low signal-to-noise ratio conditions, both the above two methods use the two-dimensional discrete Fourier transform or fast Fourier transform. Therefore, these algorithms are computationally expensive compared with the conventional one-dimensional sampling moire and phase-shifting methods. In this study, a fast parallelization implementation for two-dimensional phase-shifting methods, including the two-dimensional sampling moire method and the spatiotemporal phase-shifting method, are presented by utilizing multi-core CPU. Simulation and experimental results demonstrate that phase analysis can reach 7.5 and 5.9 times faster by use of a 12-core CPU compared with a single CPU.

关键词： Fringe pattern phase-shifting method spatiotemporal parallel image processing multi-core CPU

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OpenCL-based optimization methods for utilizing forward DCT and quantization of image compression on a heterogeneous platform

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JOURNAL OF REAL-TIME image processing 2016年第2期12卷 219-235页

作者： Alqudami, Nasser Kim, Shin-Dug Yonsei Univ Dept Comp Sci Coll Engn 134 Sinchon Dong Seoul 120749 South Korea

Recent computer systems and handheld devices are equipped with high computing capability, such as general purpose GPUs (GPGPU) and multi-core CPUs. Utilizing such resources for computation has become a general trend, making their availability an important issue for the real-time aspect. Discrete cosine transform (DCT) and quantization are two major operations in image compression standards that require complex computations. In this paper, we develop an efficient parallel implementation of the forward DCT and quantization algorithms for JPEG image compression using Open Computing Language (OpenCL). This OpenCL-based parallel implementation utilizes a multi-core CPU and a GPGPU to perform DCT and quantization computations. We demonstrate the capability of this design via two proposed working scenarios. The proposed approach also applies certain optimization techniques to improve the kernel execution time and data movements. We developed an optimal OpenCL kernel for a particular device using device-based optimization factors, such as thread granularity, work-items mapping, workload allocation, and vector-based memory access. We evaluated the performance in a heterogeneous environment, finding that the proposed parallel implementation was able to speed up the execution time of the DCT and quantization by factors of 7.97 and 8.65, respectively, obtained from 1024 x 1024 and 2084 x 2048 image sizes in 4:4:4 format.

关键词： Forward DCT Quantization image compression parallel image processing Heterogeneous computing OpenCL

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Fully automatized parallel segmentation of the optic disc in retinal fundus images

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PATTERN RECOGNITION LETTERS 2016年第Part1期83卷 99-107页

作者： Diaz-Pernil, Daniel Fondon, Irene Pena-Cantillana, Francisco Gutierrez-Naranjo, Miguel A. Univ Seville Dept Appl Math 1 CATAM Res Grp Seville Spain Univ Seville Dept Signal Theory & Commun Seville Spain Univ Seville Dept Comp Sci & AI Res Grp Nat Comp Seville Spain

This paper presents a fully automatic parallel software for the localization of the optic disc (OD) in retinal fundus color images. A new method has been implemented with the Graphics processing Units (GPU) technology. image edges are extracted using a new operator, called AGP-color segmentator. The resulting image is binarized with Hamadani's technique and, finally, a new algorithm called Hough circle cloud is applied for the detection of the OD. The reliability of the tool has been tested with 129 images from the public databases DRIVE and DIARETDB1 obtaining an average accuracy of 99.6% and a mean consumed time per image of 7.6 and 16.3 s respectively. A comparison with several state-of-the-art algorithms shows that our algorithm represents a significant improvement in terms of accuracy and efficiency. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Optic disc Hough transform parallel image processing GPU

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Feasibility Study of a Distributed and parallel Environment for Implementing the Standard Version of AAM Model

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JOURNAL OF INFORMATION processing SYSTEMS 2016年第1期12卷 149-168页

作者： Naoui, Moulkheir Mahmoudi, Said Belalem, Ghalem Univ Oran Dept Comp Sci Fac Exact Sci & Appl Oran 31000 Algeria Univ Mons Dept Comp Sci Fac Engn B-7000 Mons Belgium

The Active Appearance Model (AAM) is a class of deformable models, which, in the segmentation process, integrates the priori knowledge on the shape and the texture and deformation of the structures studied. This model in its sequential form is computationally intensive and operates on large data sets. This paper presents another framework to implement the standard version of the AAM model. We suggest a distributed and parallel approach justified by the characteristics of the model and their potentialities. We introduce a schema for the representation of the overall model and we study of operations that can be parallelized. This approach is intended to exploit the benefits build in the area of advanced image processing.

关键词： Active Appearance Model Data parallelism Deformable Model Distributed image processing parallel image processing Segmentation

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Distributed image processing Using Hadoop and HIPI 5

Distributed Image Processing Using Hadoop and HIPI

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Arsh, Swapnil Bhatt, Abhishek Kumar, Praveen LNM Inst Informat Technol Jaipur 302031 Rajasthan India VNIT Comp Sci & Engn Dept Nagpur Maharashtra India

ISBN: (纸本)9781509020287

In the present era, huge amount of data is being produced every single day. A significant portion of this massive data or big data is contributed by images. Besides the amount of data, the size and resolution of individual images is also increasing at a very fast pace, leading to more and more complex image processing algorithms which in turn pose great demand to computation power. This paper provides a solution to one such image processing application which analyzes the image-processing kernels from an industrial application: Organic-Light-Emitting-Diode (OLED) Printing for OLED center detection. The application uses Hadoop and Hadoop image processing Interface(HIPI) for parallelizing the processing. Hadoop provides the parallel processing paradigm, which when used along with HIPI can provide significant performance improvements for processing images.

关键词： image processing OLED Printing Hadoop HIPI parallel image processing

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Distributed image processing Using Hadoop and HIPI

Distributed Image Processing Using Hadoop and HIPI

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International Conference on Advances in Computing, Communications and Informatics

作者： Swapnil Arsh Abhishek Bhatt Praveen Kumar The LNM Institute of Information Technology Jaipur India 302031 Computer Science and Engineering Dept. VNIT Nagpur India

ISBN: (纸本)9781509020300

In the present era, huge amount of data is being produced every single day. A significant portion of this massive data or big data is contributed by images. Besides the amount of data, the size and resolution of individual images is also increasing at a very fast pace, leading to more and more complex image processing algorithms which in turn pose great demand to computation power. This paper provides a solution to one such image processing application which analyzes the image-processing kernels from an industrial application: Organic-Light-Emitting-Diode (OLED) Printing for OLED center detection. The application uses Hadoop and Hadoop image processing Interface (HIPI) for parallelizing the processing. Hadoop provides the parallel processing paradigm, which when used along with HIPI can provide significant performance improvements for processing images.

关键词： image processing OLED Printing Hadoop HIPI parallel image processing

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