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Quality and complexity bounds of load balancing algorithms for parallel image processing

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2000年第4期14卷 463-476页

作者： Miguet, S Pierson, JM Univ Lyon 2 Lab ERIC F-69676 Bron France Univ Littoral Cote Opale Lab LIL F-62100 Calais France

The parallel implementation of image processing algorithms implies an important choice of data distribution strategy. In order to handle the specific constraints associated with images, data distribution must take into account not only the locality of the data and its geometrical regularity but also the possible irregular computation costs associated with different image elements. A widely studied field to tackle this problem is the family of methods related to rectilinear partitioning. We introduce two fully parallel heuristics that compute suboptimal partitions, with a better complexity than the best known algorithms that compute optimal partitions. In this paper, we compare our heuristics to an optimal partitioning, both in terms of execution time and accuracy of the partition. We give some theoretical bounds on the quality of these heuristics that are corroborated by results of random numerical experiments and real applications.

关键词： parallel image processing rectilinear partitioning dynamic load balancing heuristics complexity analysis

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

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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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A highly scalable interconnection network for parallel image processing

A highly scalable interconnection network for parallel image...

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Conference on Neural Network and Distributed processing

作者： Wang, HY Gu, WK Zhejiang Univ Dept Informat & Elect Engn Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)0819442836

In this paper, we introduce a new hierarchical interconnection network for massively parallel systems, named Fully Connected Cubic Network (FCCN). FCCN is able to emulate the popular Hypercube. FCCN has a constant nodal degree of 4 and it therefore eliminates the problem of large fanout in Hypercube. Moreover, the constant degree is an important requirement for efficiently fabricating an architecture in parallel image processing. FCCN is also a highly scalable architecture in that the existing links remain intact when new nodes are introduced. FCCN is maximally fault tolerant and it enjoys reasonably low diameter, growth of the number of links and average internodal distance. At last, FCCN is used for parallel image processing system for interconnection. The computation results show that FCCN is a high efficient interconnection network for parallel image processing.

关键词： Fully Connected Cubic Network(FCCN) interconnection network(IN) self-routing algorithm parallel image processing

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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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DARPA benchmark image processing on SIMD parallel machines

DARPA benchmark image processing on SIMD parallel machines

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1996 IEEE 2nd International Conference on Algorithms and Architectures for parallel processing (ICA3PP 96)

作者： Cucchiara, R Piccardi, M Univ of Ferrara Ferrara Italy

ISBN: (纸本)0780335295

The aim of this paper is to present a comparative analysis of the execution times of low-level vision algorithms on two different SIMD parallel machines. The set of algorithms is part of the DARPA image Understanding benchmark, a widely-accepted platform for performance comparison of parallel systems in the field of computer vision. The considered computer architectures represent two opposite solutions in terms of granularity in approaching the SIMD paradigm, one with a coarse-grain array of floating-point processors and the other with a fine-grain array of single-bit processing elements. For these reasons, the set of algorithms was implemented on both systems taking into account machine specificities. In this work some insights into implementation issues and a comparative analysis of the assessed execution times are presented.

关键词： parallel image processing SIMD arrays benchmark

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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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parallel processing of remotely sensed hyperspectral imagery: full-pixel versus mixed-pixel classification

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2008年第13期20卷 1539-1572页

作者： Plaza, Antonio J. Univ Extremadura Dept Technol Comp & Commun E-10071 Caceres Spain

The rapid development of space and computer technologies allows for the possibility to store huge amounts of remotely sensed image data, collected using airborne and satellite instruments. In particular, NASA is continuously gathering high-dimensional image data with Earth observing hyperspectral sensors such as the jet Propulsion Laboratory's airborne visible-infrared imaging spectrometer (AVIRIS), which measures reflected radiation in hundreds of narrow spectral bands at different wavelength channels for the same area on the surface of the Earth. The development of fast techniques for transforming massive amounts of hyperspectral data into scientific understanding is critical for space-based Earth science and planetary exploration. Despite the growing interest in hyperspectral imaging research, only it few efforts have been devoted to the design of parallel implementations in the literature, and detailed comparisons of standardized parallel hyperspectral algorithms are currently unavailable. This paper compares several existing and new parallel processing techniques for pure and mixed-pixel classification in hyperspectral imagery. The distinction of pure versus mixed-pixel analysis is linked to the considered application domain, and results from the very rich spectral information available from hyperspectral instruments. In some cases, such information allows image analysts to overcome the constraints imposed by limited spatial resolution. In most cases, however, the spectral bands collected by hyperspectral instruments have high statistical correlation, and efficient parallel techniques are required to reduce the dimensionality of the data while retaining, the spectral information that allows for the separation of the classes. In order to address this issue, this paper also develops a new parallel feature extraction algorithm that integrates the spatial and spectral information. The proposed technique is evaluated (from the viewpoint of both classification accura

关键词： parallel image processing hyperspectral imaging cluster computing parallel processing load balance morphological processing spatial/spectral integration hyperspectral applications

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A CMOS image sensor with programmable pixel-level analog processing

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2005年第6期16卷 1673-1684页

作者： Massari, N Gottardi, M Gonzo, L Stoppa, D Simoni, A Ctr Ric Sci & Tecnol I-38050 Povo TN Italy

A prototype of a 34 x 34 pixel image sensor, implementing real-time analog image processing, is presented. Edge detection, motion detection, image amplification, and dynamic-range boosting are executed at pixel level by means of a highly interconnected pixel architecture based on the absolute value of the difference among neighbor pixels. The analog operations are performed over a kernel of 3 x 3 pixels. The square pixel, consisting of 30 transistors, has a pitch of 35 mu m with a fill-factor of 20%. The chip was fabricated in a 0.35 mu m CMOS technology, and its power consumption is 6 mW with 3.3 V power supply. The device was fully characterized and achieves a dynamic range of 50 dB with a light power density of 150 nW/mm(2) and a frame rate of 30 frame/s. The measured fixed pattern noise corresponds to 1.1% of the saturation level. The sensor's dynamic range can be extended up to 96 dB using the double-sampling technique.

关键词： active pixel sensors analog processing circuits CMOS vision sensors parallel image processing switched capacitor circuits

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ULTRA-FAST parallel CONTOUR TRACING, WITH APPLICATION TO THINNING

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PATTERN RECOGNITION 1994年第7期27卷 867-878页

作者： FERREIRA, A UBEDA, S EPFL INFORMAT THEOR LAB CH-1015 LAUSANNE SWITZERLAND UNIV JEAN MONNET TSI ST ETIENNE FRANCE

This paper proposes a parallel algorithm for contour tracking of binary pictures. Given an object contour composed by O(N) pixels, our algorithm computes in constant time the next layer of the contour of that object, using the weakest parallel model, i.e. an Exclusive Read Exclusive Write (EREW) parallel Random Access Machine (PRAM). As an application of the technique we show a work-optimal parallel thinning algorithm for binary pictures, based on Pavlidis' characterization of a skeleton. Our algorithm improves on previous solutions by producing a list of coordinates corresponding to the skeleton contour in O(N) time with O(N) processors in an EREW PRAM, where N is the width of the picture.

关键词： parallel image processing image CONTOUR TRACKING THINNING PRAM ALGORITHMS

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