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Distributed imageJ(Fiji): a framework for parallel image processing

IET image processing 2020年第12期14卷 2937-2947页

作者： Hossain, Md Amjad Khan, Preoyati Lu, Cheng Chang Clements, Robert J. Kent State Univ Dept Comp Sci Kent OH 44242 USA Kent State Univ Dept Biol Sci Kent OH 44242 USA

imageJ is an open-source application widely used for image processing. It has developer API that can be used to implement new plugins for specific image processing tasks. However, imageJ wasn't designed to work on distributed systems. Currently, it is still being used on single machines to process large medical images, which takes several hours to complete. In this article, we present the approaches to make several essential and widely used imageJ plugins to work in a cluster. As the cluster nodes parallelly run the existing plugins for image processing, they write the results on a shared drive. But one of the main challenges is, merging those results with high accuracy. Several imageJ plugins were developed to distribute tasks and generate combined results efficiently. The existing 3D-Object-Counter plugin was used for testing the designed system. The experimental results on the test images of 3D objects stored in Tagged image File Format(TIFF) show faster processing time with high accuracy and similarity compared to the single machine-based results. The image processing time depends on the number of nodes in the cluster. So, we present a mathematical model that determines the cluster size automatically for optimizing the overall image processing time.

关键词： medical image processing image processing pattern clustering application program interfaces existing 3D-Object-Counter plugin designed cluster-based system test images Tagged image File Format single machine-based results image processing time parallel image processing open-source application developer API specific image processing tasks distributed systems single machines medical images imageJ plugins existing plugins combined results

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Portable Framework for Real-Time parallel image processing on High Performance Embedded Platforms 23

Portable Framework for Real-Time Parallel Image Processing o...

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23rd Euromicro International Conference on parallel, Distributed, and Network-Based processing (PDP)

作者： Eisserer, Clemens AIT Austrian Inst Technol GmbH A-2444 Seibersdorf Austria

ISBN: (纸本)9781479984909

The trend to efficient, however more complex, multicore designs has also reached the world of Digital Signal Processors (DSP), a field where typically low-level programming has been prevalent. To overcome the additional complexity of programming multi-core and multi-chip DSP systems, we present an object-oriented framework for task-based parallel programming on the highly power efficient Texas Instruments TSMC320C6678 platform. Our framework incorporates hardware architectural details of this platform such as DMA units in a high-level manner, while maintaining portability - guiding the path for algorithmic designers from PCs to embedded DSP platforms. The whole framework has been designed and implemented with real-time requirements and low overhead in mind, which is crucial for the acceptance of higher-level solutions on embedded systems.

关键词： DSP Embedded Multicore parallel image processing Software Architecture Digital signal processing Desmoplakin Gene Software architecture Platform Frameworks embedded digital signal processor Embedded systems Micro computer Personal computers

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Accelerating parallel Magnetic Resonance image Reconstruction on Graphics processing Units Using CUDA 2

Accelerating Parallel Magnetic Resonance Image Reconstructio...

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2nd IEEE International Conference on Information and Computer Technologies (ICICT)

作者： Inam, Omair Qureshi, Mahmood Akram, Hamza Omer, Hammad Laraib, Zoia COMSATS Univ Islamabad Dept Elect & Comp Engn Islamabad Pakistan

ISBN: (纸本)9781728133232

Magnetic Resonance Imaging (MRI) is a non-invasive and powerful technique for clinical diagnosis and treatment monitoring. However, long data acquisition time in conventional MRI may cause patient discomfort and compliance. Recently, parallel magnetic resonance imaging (pMRI) techniques have been developed to speed-up the MR data acquisition time by collecting a reduced data set (k-space) using multi-channel receiver coils. However, with an increasing number of receiver coils, the handling and processing of a massive MR data limits the performance of pMRI techniques in terms of reconstruction time. Therefore, in real-time clinical settings, high speed systems have become imperative to meet the large data processing requirements of pMRI technique i.e. Generalized Auto-calibrating Partially parallel Acquisition (GRAPPA). Graphics processing units (GPUs) have recently emerged as a viable solution to adhere the rising demands of fast data processing in pMRI. This work presents the GPU accelerated GRAPPA reconstruction method using optimized CUDA kernels to obtain high-speed reconstructions, where multiple threads simultaneously communicate and cooperate to exploit the fine grained parallelism of GRAPPA reconstruction process. For a fair comparison, the performance of the proposed GPU based GRAPPA reconstruction is evaluated against CPU based GRAPPA. Several experiments against various GRAPPA configuration settings are performed using 8-channel in-vivo 1.5T human head datasets. Experimental results show that the proposed method speeds up the GRAPPA reconstruction time up to 15x without compromising the image quality.

关键词： parallel image processing graphics processing unit(GPU) medical image processing high performance parallel computing

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Initial Results on the Effectiveness of Distributed Edge Detection for Large image Data Sets

Initial Results on the Effectiveness of Distributed Edge Det...

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SoutheastCon Conference

作者： Renato, Ivanescu Constantin Mihai, Mocanu Univ Craiova Dept Comp & Informat Technol Craiova Romania

ISBN: (纸本)9781665406529

image data is expanding rapidly, along with technology development, so efficient solutions must be considered to achieve high, real-time performance in the case of processing large image datasets. parallel processing is increasingly used as an attractive alternative to improve the performance, when using existing distributed architectures but also for sequential commodity computers. It can provide speedup, efficiency, reliability, incremental growth, and flexibility. We present such an alternative and stress the effectiveness of the methods to accelerate computations on a small cluster of PCs compared to a single CPU. Our paper is focused on applying edge detection on large image data sets, as a fundamental and challenging task in image processing and computer vision. Five different techniques, mainly Sobel, Prewitt, LoG, Canny, and Roberts, are compared in a simple experimental setup that includes the OpenCV library functions for image pixels manipulation. Gaussian blur is used to reduce high-frequency components to manage the noise that edge detection is impacted by. Overall, this work is part of a more extensive investigation of image segmentation methods on large image datasets, but the results presented are relevant and show the effectiveness of our approach.

关键词： parallel image processing edge detection OpenCV MPI

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GPU-accelerated uncapacitated facility location and semi-dense SymStereo pipelines for piecewise-planar-based 3D reconstruction

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JOURNAL OF REAL-TIME image processing 2021年第3期18卷 445-461页

作者： Graca, Carlos Raposo, Carolina Barreto, Joao P. Nunes, Urbano Falcao, Gabriel Univ Coimbra Inst Telecomunicaeoes Dept Elect & Comp Engn Coimbra 3030290 Portugal Univ Coimbra Inst Syst & Robot Dept Elect & Comp Engn Coimbra 3030290 Portugal

Planar 3D reconstruction presents advantages over point cloud representations. This work focuses on the acceleration of piecewise-planar-based 3D reconstruction, a StereoScan method. We identify the SymStereo (logN) and uncapacitated facility location (UFL) algorithms as the most computationally expensive tasks, consuming nearly 80 x of total runtime, when detecting planes in a single stereo pair on a sequential CPU pipeline. Consequently, these algorithms have been parallelized using single- and multi-GPU architectures to perform significantly faster than previous sequential approaches. Experimental results show that accelerated parallel implementations of SymStereo (logN) can process up to 56 frames per second, achieving a speedup of 38 x against the sequential C implementation (Intel Core i7-4790k). The parallel version of the message-passing algorithm (max-sum) for the UFL problem processes up to five matrices per second and outperforms the sequential C baseline for computing UFL by 38 x.

关键词： Piecewise-planar reconstruction SymStereo Uncapacitated facility location parallel image processing 3D reconstruction High-resolution images Multi-GPU systems

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FPGA implementation of HOOFR bucketing extractor-based real-time embedded SLAM applications

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JOURNAL OF REAL-TIME image processing 2021年第3期18卷 525-538页

作者： Dai Duong Nguyen El Ouardi, Abdelhafid Rodriguez, Sergio Bouaziz, Samir Hanoi Univ Sci & Technol Sch Elect Engn Hanoi Vietnam Univ Paris Saclay ENS Paris Saclay CNRS Syst & Applicat Technol Informat & Energie F-91405 Orsay France

Feature extraction is an important vision task in many applications like simultaneous localization and mapping (SLAM). In the recent computing systems, FPGA-based acceleration have presented a strong competition to GPU-based acceleration due to its high computation capabilities and lower energy consumption. In this paper, we present a high-level synthesis implementation on a SoC-FPGA of a feature extraction algorithm dedicated for SLAM applications. We choose HOOFR extraction algorithm which provides a robust performance but requires a significant computation on embedded CPU. Our system is dedicated for SLAM applications so that we also integrated bucketing detection method in order to have a homogeneous distribution of keypoints in the image. Moreover, instead of optimizing performance by simplifying the original algorithm as in many other researches, we respected the complexity of HOOFR extractor and have parallelized the processing operations. The design has been validated on an Intel Arria 10 SoC-FPGA with a throughput of 54 fps at 1226 x 370 pixels (handling 1750 features) or 14 fps at 1920 x 1080 pixels (handling 6929 features).

关键词： Features extraction parallel image processing FPGA implementation Embedded systems

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Biologically inspired compound eye 14

Biologically inspired compound eye

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14th International Conference on Correlation Optics

作者： Borovytsky, Volodymyr Antonenko, Vitalii Natl Tech Univ Ukraine Igor Sikorsky KPI Opt Engn Dept Pr Perem 37 UA-03056 Kiev Ukraine

ISBN: (纸本)9781510635111

The paper describes the design of a biologically inspired compound eye. It contains numerous optical channels like facets in a natural compound eye. Each facet has an optical system, several photodetectors with pre-amplifiers and a microcontroller with a multi-channel analog-to-digital converter. The signals coming from the photodetectors in each channel are amplified, converted into a digital form and processed by a microcontroller. All microcontrollers are attached to a microcontroller network. These microcontrollers can be reprogrammed to perform various image processing operations including spatial filtration, temporal filtration, correlation calculation, neural network simulation and others. It makes possible investigation and performance evaluation of the compound eye when all its facets perform parallel image processing and image analysis. The working prototype of this compound eye has demonstrated ability to measure distribution of speed and direction of optical flow thought its field of view. The possible improvements and possible applications of this design are also discussed.

关键词： compound eye artificial eye bionic eye biologically-inspired vision image sensor facet correlation technique parallel image processing

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A parallel image Registration Algorithm Based on a Lattice Boltzmann Model

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INFORMATION 2020年第1期11卷 1页

作者： Chen, Yu Lu, Dongxiang Courbebaisse, Guy Yancheng Teachers Univ Sch Informat Engn Yancheng 224000 Peoples R China Inst Natl Sci Appl Lyon Res Ctr Med Imaging F-69000 Lyon France

image registration is a key pre-procedure for high level image processing. However, taking into consideration the complexity and accuracy of the algorithm, the image registration algorithm always has high time complexity. To speed up the registration algorithm, parallel computation is a relevant strategy. parallelizing the algorithm by implementing Lattice Boltzmann method (LBM) seems a good candidate. In consequence, this paper proposes a novel parallel LBM based model (LB model) for image registration. The main idea of our method consists in simulating the convection diffusion equation through a LB model with an ad hoc collision term. By applying our method on computed tomography angiography images (CTA images), Magnet Resonance images (MR images), natural scene image and artificial images, our model proves to be faster than classical methods and achieves accurate registration. In the continuity of 2D image registration model, the LB model is extended to 3D volume registration providing excellent results in domain such as medical imaging. Our method can run on massively parallel architectures, ranging from embedded field programmable gate arrays (FPGAs) and digital signal processors (DSPs) up to graphics processing units (GPUs).

关键词： image registration Lattice Boltzmann method and model parallel image processing

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NPIY : A novel partitioner for improving mapreduce performance

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JOURNAL OF VISUAL LANGUAGES AND COMPUTING 2018年 46卷 1-11页

作者： Lu, Wei Chen, Lei Wang, Liqiang Yuan, Haitao Xing, Weiwei Yang, Yong Beijing Jiaotong Univ Sch Software Engn Beijing Peoples R China Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA

MapReduce is an effective and widely-used framework for processing large datasets in parallel over a cluster of computers. Data skew, cluster heterogeneity, and network traffic are three issues that significantly affect the performance of MapReduce applications. However, the hash-based partitioner in the native Hadoop does not consider these factors. This paper proposes a new partitioner for Yarn (Hadoop 2.6.0), namely, NPIY, which adopts an innovative parallel sampling method to distribute intermediate data. The paper makes the following major contributions: (1) NPIY mitigates data skew in MapReduce applications;(2) NPIY considers the heterogeneity of computing resources to balance the loads among Reducers;(3) NPIY reduces the network traffic in the shuffle phase by trying to retain intermediate data on those nodes running both map and reduce tasks. Compared with the native Hadoop and other popular strategies, NPIY can reduce execution time by up to 41.66% and 58.68% in homogeneous and heterogeneous clusters, respectively. We further customize NPIY for parallel image processing, and the execution time has been improved by 28.8% compared with the native Hadoop. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： MapReduce Hadoop Data skew Load balance Data transmission amount Heterogeneous parallel image processing

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Face extraction and recognition from public images using HIPI 14

Face extraction and recognition from public images using HIP...

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14th International Conference on Electronics Computer and Computation (ICECCO)

作者： Bogdanchikov, Andrey Kariboz, Darmen Meraliyev, Meraryslan Suleyman Demirel Univ Dept Comp Sci Kaskelen Kazakhstan

ISBN: (纸本)9781728101323

Social networking services with public data are widely used nowadays. Billions of images uploaded to the internet each day over the world. This paper proposes the idea of a system which is currently being developed. The system collects images from public sources by some specific criteria, applies face detection and recognition algorithms on collected images, and provides the results in readable form. The Apache Hadoop library is used to increase performance of the system, and images are downloaded by the help of HIPI library. For testing purposes Labeled Faces in the Wild benchmark is used as a database for images containing over 13233 images of 5749 identities. Each image is jpg format in 250*250 resolutions. Results were more than good after testing the system with this database for face detection and recognition.

关键词： image collection face detection face recognition Hadoop HIPI parallel image processing

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