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Real-time range image segmentation on GPU

Real-time range image segmentation on GPU

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International conference on Control, Automation and Systems ( ICCAS)

作者： Jin Xin Hua Mun-Ho Jeong Department of Control and Instrumentation Engineering Kwangwoon University Seoul Korea

In this paper propose a GPU-based parallel processing method for real-time image segmentation with neural oscillator network. Range image segmentation methods can be divided into two categories: edge-based and region-based. Edge-base method is sensitive to noise and region-based method is hard to extracting the boundary detail between the object. However, by using LEGION (Locally Excitatory Globally Inhibitory oscillator networks) to do range image segmentation can overcome above disadvantages. The reason why LEGION is suitable for parallel processing that each oscillator calculate with its 8-neiborhood oscillators in real time when we process image segmentation by LEGION. Thus, using GPU-based parallel processing with LEGION can improve the speed to realize real-time image segmentation.

关键词： Graphics processing units Oscillators Integrated circuits image edge detection image segmentation Indexes HTML

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Correlative Analysis of Denoising methods in Spectral images Embedded with Different Noises

Correlative Analysis of Denoising Methods in Spectral Images...

引用

International conference on parallel distributed and Grid Computing (PDGC)

作者： Sangeetha Annam Anshu Singla Chitkara University Punjab India

ISBN: (数字)9781728171326

ISBN: (纸本)9781728171333

Digital image is one of the primary way of communication in the present digital world. During the acquiring process, the images may become noisy. Noise reduction is a demanding task during the image analysis process without dissimilating the important features. It is the procedure of restoring the original image by discarding unwanted noises and known as image denoising. The main intention of any noise removal technique is to completely eradicate the noise from the image, such that the resulting image is better than the original image. In this digital era, remote sensing images are widely commercial for environmental monitoring. In this study, a correlative analysis of different noise removal methods using various filters in spectral images is performed. Spectral images are introduced with different types of noise and further filters are applied to denoise the image. The performances of the methods are evaluated using benchmarks: Signal-to-Noise Ratio (SNR) and Peak Signal-to-N oise Ratio (PSNR). Experimental results demonstrate that the SNR and PSNR measures were comparatively higher for all the filters when the image is introduced with Poisson noise.

关键词： Wiener filters Nonlinear filters Maximum likelihood detection Adaptive filters Noise reduction image denoising Hyperspectral imaging

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Hidden Markov Models for Abnormal Event processing in Transportation Data Streams

Hidden Markov Models for Abnormal Event Processing in Transp...

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International conference on parallel and distributed Systems (ICPADS)

作者： John Kah-Soon Lau Chen-Khong Tham Department of Electrical and Computer Engineering National University of Singapore

Making sense of big data and big metadata remains a challenge as more and more data are churned out every day. The problem of adding value to unstructured data requires the application of computationally intensive algorithms to discover useful patterns in the data. In terms of data streams from public transport such as buses, we address the problem of performing time-consuming algorithms to model the data while still being able to process abnormal events in real-time. We propose using Hidden Markov Models (HMMs) for identifying conditions for an abnormal event in bus journeys and methods for isolating HMM computations from real-time event processing. Results show that training HMMs with even noisy metadata can generate models that can recognize an abnormal event in a parallel and distributed manner in the cloud.

关键词： Hidden Markov models Training Real-time systems Engines Computational modeling Noise measurement Computers

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Tuning the Efficiency of parallel Adaptive Integration with Synchronizers

Tuning the Efficiency of Parallel Adaptive Integration with ...

引用

Euromicro conference on parallel, distributed and Network-Based processing

作者： Janusz Borkowski Marek Tudruj Polish Japanese Institute of Information Technology Warsaw Poland Institute of Computer Science Polish Academy of Sciences Warsaw Poland

High efficiency in parallel adaptive integration is difficult to achieve. Dependencies between integration regions are dynamic and therefore dynamic region redistribution is necessary. In our approach, we employ a control infrastructure designed to support parallel application global state monitoring. It includes monitors (called synchronizers), which observe application global states and take control decisions based on predicates computed on global states. Region redistribution strategy is implemented inside the monitors. The most and least loaded processes are selected and ordered to level their loads by region exchange. The paper shows that this strategy can work well, underlining some important factors, which influence the performance. Two methods, which aim at eliminating excessive region transfers, are presented. It is also shown, that good parallel efficiency can be obtained easier if interprocessor network supports efficient transmissions of large data packets. The problem of finding an optimal communication frequency between computing processes has been much reduced - it is enough if processes report their local load reasonably often.

关键词： Artificial intelligence Monitoring Frequency synchronization Iterative algorithms Information technology Computer science Concurrent computing Computational efficiency Error analysis Costs

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Perspective of the Large Scale Data Facility (LSDF) Supporting Nuclear Fusion Applications

Perspective of the Large Scale Data Facility (LSDF) Supporti...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Rainer Stotzka Volker Hartmann Thomas Jejkal Michael Sutter Jos van Wezel Marcus Hardt Ariel García Rainer Kupsch Serguei Bourov Karlsruhe Institute of Technology (KIT) Institute for Data Processing and Electronics (IPE) Karlsruhe Germany Karlsruhe Institute of Technology (KIT) Steinbuch Centre for Computing (SCC) Karlsruhe Germany

To cope with the growing requirements of data intensive scientific experiments, models and simulations the Large Scale Data Facility(LSDF) at KIT aims to support many scientific disciplines. The LSDF is a distributed storage facility at Exabyte scale providing storage, archives, data bases and meta data repositories. Open interfaces and APIs support a variety of access methods to the highly available services for high throughput data applications. Tools for an easy and transparent access allow scientists to use the LSDF without bothering with the internal structures and technologies. In close cooperation with the scientific communities the LSDF provides assistance to efficiently organize data and metadata structures, and develops and deploys community specific software on the directly connected computing infrastructure.

关键词： Communities Software Protocols Standards organizations Organizations Microscopy distributed databases

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A new terrain classification algorithm based on convolutional neural network

A new terrain classification algorithm based on convolutiona...

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Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Hui Yang Wang Jie Niu Qian School of Humanities Chang’an University Xi’an China School of Construction Machinery Chang’an University Xi’an China

ISBN: (数字)9781728196688

ISBN: (纸本)9781728196695

Aiming at the problems of existing terrain classification methods, this paper proposes a new terrain classification algorithm based on convolutional neural networks. First, the SegNet and SLIC superpixel segmentation methods are combined to solve the boundary blur problem in the terrain classification of the SegNet convolutional neural network. Then, through comparison experiments of irregular and complex terrain classification based on support vector machine, SegNet and SLIC-SegNet, the superiority of the new SLIC-SegNet terrain classification method is verified.

关键词： image segmentation Information science image recognition Convolutional neural networks conferences Classification algorithms

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parallel 3D fast wavelet transform on manycore GPUs and multicore CPUs

Parallel 3D fast wavelet transform on manycore GPUs and mult...

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International conference on Computational Science (ICCS)

作者： Franco, Joaquin Bernabe, Gregorio Fernandez, Juan Ujaldon, Manuel Univ Murcia Dept Comp Engn E-30001 Murcia Spain Univ Murcia Comp Architect Dept E-30001 Murcia Spain

GPUs have recently attracted our attention as accelerators on a wide variety of algorithms, including assorted examples within the image analysis field. Among them, wavelets are gaining popularity as solid tools for data mining and video compression, though this comes at the expense of a high computational cost. After proving the effectiveness of the GPU for accelerating the 2D Fast Wavelet Transform [1], we present in this paper a novel implementation on manycore GPUs and multicore CPUs for a high performance computation of the 3D Fast Wavelet Transform (3D-FWT). This algorithm poses a challenging access pattern on matrix operators demanding high sustainable bandwidth, as well as mathematical functions with remarkable arithmetic intensity on ALUs and FPUs. On the GPU side, we focus on CUDA programming to develop methods for an efficient mapping on manycores and to fully exploit the memory hierarchy, whose management is explicit by the programmer. On multicore CPUs, OpenMP and Pthreads are used as counterparts to maximize parallelism, and renowned techniques like tiling and blocking are exploited to optimize the use of memory. Experimental results on an Nvidia Tesla C870 GPU and an Intel Core 2 Quad Q6700 CPU indicate that our implementation runs three times faster on the Tesla and up to fifteen times faster when communications are neglected, which enables the GPU for processing real-time videos in many applications where the 3D-FWT is involved. (C) 2010 Published by Elsevier Ltd.

关键词： 3D Fast Wavelet Transform parallel programming GPU multicore

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parallel image thinning through topological operators on shared memory parallel machines

Parallel image thinning through topological operators on sha...

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Asilomar conference on Signals, Systems & Computers

作者： Ramzi Mahmoudi Mohamed Akil Petr Matas Laboratoire d'Informatique Gaspard-Monge Université Paris-Est Noisy-le-Grand France Department of Applied Electronics and Telecommunications University of West Bohemia Plzen Czech Republic

ISBN: (纸本)9781424458257

In this paper, we present a concurrent implementation of a powerful topological thinning operator. This operator is able to act directly over grayscale images without modifying their topology. We introduce an adapted parallelization methodology which combines split, distribute and merge (SDM) strategy and mixed parallelism techniques (data and thread parallelism). The introduced strategy allows efficient parallelization of a large class of topological operators including, mainly, ¿-leveling, skeletonization and crest restoring algorithms. To achieve a good speedup, we cared about coordination of threads. distributed work during thinning process is done by a variable number of threads. Tests on 2D grayscale image (512*512), using shared memory parallel machine (SMPM) with 8 CPU cores (2× Xeon E5405 running at frequency of 2 GHz), showed an enhancement of 6.2 with a maximum achieved cadency of 125 images/s using 8 threads.

关键词： parallel machines Gray-scale parallel processing Topology image processing Frequency synchronization Fingerprint recognition Biological cells Random access memory

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FPGA-Based parallel Implementation of SURF Algorithm

FPGA-Based Parallel Implementation of SURF Algorithm

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International conference on parallel and distributed Systems (ICPADS)

作者： Wenjie Chen Shuaishuai Ding Zhilei Chai Daojing He Weihua Zhang Guanhua Zhang Qiwei Peng Wang Luo MoE Engineering Research Center for Software/Hardware Co-design Technology and Application East China Normal University Shanghai China School of loT Engineering Jiangnan University Wuxi China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Parallel Processing Institute Fudan University Shanghai China

ISBN: (纸本)9781509053827

SURF (Speeded up robust features) detection is used extensively in object detection, tracking and matching. However, due to its high complexity, it is usually a challenge to perform such detection in real time on a general-purpose processor. This paper proposes a parallel computing algorithm for the fast computation of SURF, which is specially designed for FPGAs. By efficiently exploiting the advantages of the architecture of an FPGA, and by appropriately handling the inherent parallelism of the SURF computation, the proposed algorithm is able to significantly reduce the computation time. Our experimental results show that, for an image with a resolution of 640x480, the processing time for computing using SURF is only 0.047 seconds on an FPGA (XC6SLX150T, 66.7 MHz), which is 13 times faster than when performed on a typical i3-3240 CPU (with a 3.4 GHz main frequency) and 249 times faster than when performed on a traditional ARM system (CortexTM-A8, 1 GHz).

关键词： Feature extraction parallel processing Field programmable gate arrays Hardware Algorithm design and analysis Interpolation Real-time systems

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Multiscale data reduction with flexible saliency criterion for biological image analysis

Multiscale data reduction with flexible saliency criterion f...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： William J. Bosl Harvard-MIT Division Health Sciences and Technology Boston MA USA

Analysis of biomedical images requires attention to image features that represent a small fraction of the total image size. A rapid method for eliminating unnecessary detail, analogous to pre-attentive processing in biological vision, allows computational resources to be applied where most needed for higher-level analysis. In this report we describe a method for bottom up merging of pixels into larger units based on flexible saliency criteria using a method similar to structured adaptive grid methods used for solving differential equations on physical domains. While creating a multiscale quadtree representation of the image, a saliency test is applied to prune the tree to eliminate unneeded details, resulting in an image with adaptive resolution. This method may be used as a first step for image segmentation and analysis and is inherently parallel, enabling implementation on programmable hardware or distributed memory clusters.

关键词： image analysis Biomedical imaging Biology computing Biomedical computing Computer vision Merging Differential equations Testing image resolution image segmentation

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