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检索条件"任意字段=Conference on Parallel Processing for Imaging Applications"

共 765 条记录，以下是171-180 订阅

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Coarse-Grained and Fine-Grained parallel Optimization for Real-Time En-Face OCT imaging 20

Coarse-Grained and Fine-Grained Parallel Optimization for Re...

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conference on Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XX

作者： Kapinchev, Konstantin Bradu, Adrian Barnes, Frederick Podoleanu, Adrian Univ Kent Sch Comp Canterbury Kent England Univ Kent Sch Phys Sci Appl Opt Grp Canterbury Kent England

ISBN: (纸本)9781628419313

This paper presents parallel optimizations in the en-face (C-scan) optical coherence tomography (OCT) display. Compared with the cross-sectional (B-scan) imagery, the production of en-face images is more computationally demanding, due to the increased size of the data handled by the digital signal processing (DSP) algorithms. A sequential implementation of the DSP leads to a limited number of real-time generated en-face images. There are OCT applications, where simultaneous production of large number of en-face images from multiple depths is required, such as real-time diagnostics and monitoring of surgery and ablation. In sequential computing, this requirement leads to a significant increase of the time to process the data and to generate the images. As a result, the processing time exceeds the acquisition time and the image generation is not in real-time. In these cases, not producing en-face images in real-time makes the OCT system ineffective. parallel optimization of the DSP algorithms provides a solution to this problem. Coarse-grained central processing unit (CPU) based and fine-grained graphics processing unit (GPU) based parallel implementations of the conventional Fourier domain (CFD) OCT method and the Master-Slave Interferometry (MSI) OCT method are studied. In the coarse-grained CPU implementation, each parallel thread processes the whole OCT frame and generates a single en-face image. The corresponding fine-grained GPU implementation launches one parallel thread for every data point from the OCT frame and thus achieves maximum parallelism. The performance and scalability of the CPU-based and GPU-based parallel approaches are analyzed and compared. The quality and the resolution of the images generated by the CFD method and the MSI method are also discussed and compared.

关键词： parallel optimization multithreading en-face OCT imaging

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parallel implementation of a hyperspectral image linear SVM classifier using RVC-CAL 6

Parallel implementation of a hyperspectral image linear SVM ...

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conference on High-Performance Computing in Geoscience and Remote Sensing VI

作者： Madronal, D. Fabelo, H. Lazcano, R. Callico, G. M. Juarez, E. Sanz, C. Tech Univ Madrid UPM Ctr Software Technol & Multimedia Syst CITSEM Madrid Spain Univ Las Palmas de Gran Canaria ULPGC Res Inst Appl Microelect IUMA Las Palmas Gran Canaria Las Palmas Spain

ISBN: (纸本)9781510604186;9781510604193

Hyperspectral imaging (HI) collects high resolution spectral information consisting of hundreds of bands across the electromagnetic spectrum -from the ultraviolet to the infrared range-. Thanks to this huge amount of information, an identification of the different elements that compound the hyperspectral image is feasible. Initially, HI was developed for remote sensing applications and, nowadays, its use has been spread to research fields such as security and medicine. In all of them, new applications that demand the specific requirement of real-time processing have appear. In order to fulfill this requirement, the intrinsic parallelism of the algorithms needs to be explicitly exploited. In this paper, a Support Vector Machine (SVM) classifier with a linear kernel has been implemented using a dataflow language called RVC-CAL. Specifically, RVC-CAL allows the scheduling of functional actors onto the target platform cores. Once the parallelism of the classifier has been extracted, a comparison of the SVM classifier implementation using LibSVM -a specific library for SVM applications-and RVC-CAL has been performed. The speedup results obtained for the image classifier depends on the number of blocks in which the image is divided;concretely, when 3 image blocks are processed in parallel, an average speed up above 2.50, with regard to the RVC-CAL sequential version, is achieved.

关键词： Hyperspectral imaging Support Vector Machine RVC-CAL Real-time processing parallelism exploitation

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TERA: A Readout IC for Ultra High Rate X-ray Detection applications

TERA: A Readout IC for Ultra High Rate X-ray Detection Appli...

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IEEE Nuclear Science Symposium

作者： Giovanni Bellotti Idham Hafizh Marco Carminati Carlo Fiorini Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano

ISBN: (纸本)9781538622834

This work presents TERA (Throughput Enhanced Readout ASIC), a multichannel low-noise readout ASIC designed for ultra-high-rate x-ray detection applications. The chip has been developed to process signals coming from Silicon Drift Detectors (SDDs) coupled to Charge Sensitive Amplifiers (CSAs), such as CUBE, with enhanced high throughput capability and good noise performance. The first prototype here presented is composed of four parallel readout channels, each one including a 7th order semi-Gaussian shaping-amplifier. The filter has been chosen to minimize series noise, ballistic deficit, and pile-up effect. The channel can accommodate four different energy ranges: 5, 10, 20, and 30 keV, and four different filter shaping times: 200, 400, 1000, and 2000 ns (pulse width at 1% of the peak amplitude). The output of the shaping amplifier is followed by a peak stretcher and an analog memory. The analog memory serves as a buffer to temporarily store the peak values, thus increasing the throughput in Poisson-distributed events with average count rate comparable to the acquisition readout frequency. An independent peak detector circuit and a novel pile-up-rejector strategy have also been developed to maximize the throughput. Readout channels are organized in two independent subsets of two channels, each one read sequentially, buffered by a single-ended-to-differential stage, and digitized by a successive-approximation-register (SAR) 12-bit ADC, providing the maximum sampling rate for each channel of 2 Mcps. The simulations suggest that in the case of Poisson-distributed events with the average input count rate of 3 Mcps, the channel throughput can reach up to 1.75 Mcps.

关键词： Throughput readout analogue storage COUNTING RATES X-ray detection noise performance Detector circuits signal processing Cube

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An Adaptive Undersampling Scheme of Wavelet-Encoded parallel MR imaging for More Efficient MR Data Acquisition

An Adaptive Undersampling Scheme of Wavelet-Encoded Parallel...

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conference on Medical imaging - Image processing

作者： Xie, Hua Bosshard, John C. Hill, Jason E. Wright, Steven M. Mitra, Sunanda Texas Tech Univ Dept Elect & Comp Engn Lubbock TX 79409 USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

ISBN: (纸本)9781510600195

Magnetic Resonance imaging (MRI) offers noninvasive high resolution, high contrast cross-sectional anatomic images through the body. The data of the conventional MRI is collected in spatial frequency (Fourier) domain, also known as k-space. Because there is still a great need to improve temporal resolution of MRI, Compressed Sensing (CS) in MR imaging is proposed to exploit the sparsity of MR images showing great potential to reduce the scan time significantly, however, it poses its own unique problems. This paper revisits wavelet-encoded MR imaging which replaces phase encoding in conventional MRI data acquisition with wavelet encoding by applying wavelet-shaped spatially selective radiofrequency (RF) excitation, and keeps the readout direction as frequency encoding. The practicality of wavelet-encoded MRI by itself is limited due to the SNR penalties and poor time resolution compared to conventional Fourier-based MRI. To compensate for those disadvantages, this paper first introduces an undersampling scheme named significance map for sparse wavelet-encoded k-space to speed up data acquisition as well as allowing for various adaptive imaging strategies. The proposed adaptive wavelet-encoded undersampling scheme does not require prior knowledge of the subject to be scanned. Multiband (MB) parallel imaging is also incorporated with wavelet-encoded MRI by exciting multiple regions simultaneously for further reduction in scan time desirable for medical applications. The simulation and experimental results are presented showing the feasibility of the proposed approach in further reduction of the redundancy of the wavelet k-space data while maintaining relatively high quality.

关键词： Magnetic Resonance imaging (MRI) wavelet-encoded MR imaging adaptive wavelet encoding significance map sparsity multiband selective excitation

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Towards a Smart Selection of Resources for Low-energy Multimedia processing

Towards a Smart Selection of Resources for Low-energy Multim...

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2nd International conference on Cloud Computing Technologies and applications (CloudTech)

作者： Mahmoudi, Sidi Ahmed Univ Mons Fac Engn Comp Sci Dept 20 Pl Parc Mons Belgium

ISBN: (纸本)9781467388948

Today, one can find images and videos everywhere, they can come from cameras, mobile phones or from other devices. These images and videos are used to illustrate different objects in a large number of situations (airports, hospitals, public areas, sport events, etc.). This makes the task of processing images and videos a necessary tool that can be used for various domains related to computer vision. The performance of these algorithms have been so reduced due to their high intensive computation and energy consumption. In this work, we propose a new framework that allows users to select in a smart and efficient way the computing units (CPU or/and GPU) in case of processing single image, multiple images, multiple videos or single video in real time. This framework enables to affect the CPU or/and GPU units for calculation depending on the type of media to process and the algorithm complexity. The framework provides several image and video functions on GPU, such as silhouette extraction, points of interest extraction, edges detection, sparse and dense optical flow estimation. These functions are exploited in different applications such as vertebra segmentation in X-ray and MR images, gaze estimation, event detection and localization in real time. Experimental results have been obtained by applying the framework for different use case applications showing a speedup ranging from 5 to 116 x, by comparison with sequential CPU implementations. In addition to these performance, the parallel and heterogeneous implementation offered lower power consumption as a result for the fast treatment.

关键词： GPU Heterogeneous architectures Image and video processing Medical imaging Motion tracking

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Real Time Thermogram Enhancement by FPGA-Based Contrast Stretching 8th

Real Time Thermogram Enhancement by FPGA-Based Contrast Stre...

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8th Asian conference on Intelligent Information and Database Systems (ACIIDS)

作者： Kwiatkowski, Jan Daniec, Krzysztof Jedrasiak, Karol Sobel, Dawid Domzal, Mariusz Nawrat, Aleksander Silesian Tech Univ Inst Automat Control Gliwice Poland

ISBN: (纸本)9783662493908;9783662493892

Image enhancement is a challenging problem in the world of digital technology. Currently, producers of video cameras, are providing devices working with increasingly higher frequencies and resolutions of acquired video frames. However, that creates a need of development of methods used in image enhancement. Furthermore, real time processing of a significant amount of data require utilization of parallel processing. The paper presents the results of research on the problem of real time histogram stretching. Proposed solution utilizes parallel processing, supported by FPGA (Field Programmable Gate Array). The problem of histogram stretching is closely associated with thermal imaging, in which, in most of the cases, the range of measured infrared radiation, in the observed scene, is generally narrow, in relation to total possible range of radiation measured by IRFPA (Infrared Focal Plane Array), which results in narrow histogram and thus low contrast of the acquired image. The proposed solution can be used in every branch of contemporary industry, wherever video cameras are used. Real time operating hardware implementation, eliminates a need of further post-processing, and may be considered as useful and eligible in many modern applications.

关键词： Thermal imaging Real time video processing Histogram stretching Normalization FPGA Hardware acceleration

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HIGH CONTRAST imaging THROUGH ADAPTIVE TRANSMITTANCE CONTROL IN THE FOCAL PLANE 5

HIGH CONTRAST IMAGING THROUGH ADAPTIVE TRANSMITTANCE CONTROL...

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conference on Dimensional Optical Metrology and Inspection for Practical applications V

作者： Dhadwal, Harbans S. Rastegar, Jahangir Feng, Dake Omnitek Partners LLC Unit 3 Ronkonkoma NY 11779 USA

ISBN: (纸本)9781510601093

High contrast imaging, in the presence of a bright background, is a challenging problem encountered in diverse applications ranging from the daily chore of driving into a sun-drenched scene to in vivo use of biomedical imaging in various types of keyhole surgeries. imaging in the presence of bright sources saturates the vision system, resulting in loss of scene fidelity, corresponding to low image contrast and reduced resolution. The problem is exacerbated in retro-reflective imaging systems where the light sources illuminating the object are unavoidably strong, typically masking the object features. This manuscript presents a novel theoretical framework, based on nonlinear analysis and adaptive focal plane transmittance, to selectively remove object domain sources of background light from the image plane, resulting in local and global increases in image contrast. The background signal can either be of a global specular nature, giving rise to parallel illumination from the entire object surface or can be represented by a mosaic of randomly orientated, small specular surfaces. The latter is more representative of real world practical imaging systems. Thus, the background signal comprises of groups of oblique rays corresponding to distributions of the mosaic surfaces. Through the imaging system, light from group of like surfaces, converges to a localized spot in the focal plane of the lens and then diverges to cast a localized bright spot in the image plane. Thus, transmittance of a spatial light modulator, positioned in the focal plane, can be adaptively controlled to block a particular source of background light. Consequently, the image plane intensity is entirely due to the object features. Experimental image data is presented to verify the efficacy of the methodology.

关键词： Fourier optics optical signal processing image contrast bright field imaging

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parallel Implementation of a Hyperspectral Data Geometry-Based Estimation of Number of Endmembers Algorithm

Parallel Implementation of a Hyperspectral Data Geometry-Bas...

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conference on Real-Time Image and Video processing

作者： Bernabe, Sergio Martin, Gabriel Botella, Guillermo Prieto-Matias, Manuel Plaza, Antonio Univ Complutense Madrid Spain Inst Super Tecn Inst Telecomunicacoes Lisbon Portugal Univ Extremadura Hyperspectral Comp Lab Caceres Spain

ISBN: (纸本)9781510601420

In the last years, hyperspectral analysis have been applied in many remote sensing applications. In fact, hyper spectral unmixing has been a challenging task in hyperspectral data exploitation. This process consists of three stages: (i) estimation of the number of pure spectral signatures or endmembers, (ii) automatic identification of the estimated endmembers, and (iii) estimation of the fractional abundance of each endmember in each pixel of the scene. However, unmixing algorithms can be computationally very expensive, a fact that compromises their use in applications under real-time constraints. In recent years, several techniques have been proposed to solve the aforementioned problem but until now, most works have focused on the second and third stages. The execution cost of the first stage is usually lower than the other stages. Indeed, it can be optional if we known a priori this estimation. However, its acceleration on parallel architectures is still an interesting and open problem. In this paper we have addressed this issue focusing on the GENE algorithm, a promising geometry-based proposal introduced in.(1) We have evaluated our parallel implementation in terms of both accuracy and computational performance through Monte Carlo simulations for real and synthetic data experiments. Performance results on a modern GPU shows satisfactory 16x speedup factors, which allow us to expect that this method could meet real-time requirements on a fully operational unmixing chain.

关键词： Hyperspectral imaging estimation of number of endmembers hyperspectral unmixing high performance computing (HPC) commodity graphics processing units (GPUs)

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GPU-based parallel fuzzy c-mean clustering model via genetic algorithm

GPU-based parallel fuzzy c-mean clustering model via genetic...

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4th International Workshop on Embedded Multicore Computing and applications in conjunction with the 16th IEEE International conference on High Performance and Communications

作者： Hung, Che-Lun Wu, Yuan-Huai Providence Univ Dept Comp Sci & Commun Engn Taichung 43301 Taiwan Dept Comp Sci & Informat Engn Taichung Taiwan

Detection of white matter changes in brain tissue using magnetic resonance imaging has been an increasingly active and challenging research area in computational neuroscience. A genetic algorithm based on a fuzzy c-mean clustering method (GAFCM) was applied to simulated images to separate foreground spot signal information from the background, and the results were compared. The strength of this algorithm was tested by evaluating the segmentation matching factor, coefficient of determination, concordance correlation, and gene expression values. The experimental results demonstrated that the segmentation ability of GAFCM was better than that of fuzzy c-means and K-means algorithms. However, GAFCM is computationally expensive. This study presents a new GPU-based parallel GAFCM algorithm to improve the performance of GAFCM. The experimental results show that computational performance can be increased by a factor of approximately 20 over the CPU-based GAFCM algorithm while maintaining the quality of the processed images. Thus, the proposed GPU-based parallel GAFCM algorithm can achieve the same results and significantly decrease processing time. Copyright (C) 2015 John Wiley & Sons, Ltd.

关键词： Fuzzy C-Mean magnetic resonance white matter GPU CUDA genetic algorithm

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Optimizing Convergence Rates of Alternating Minimization Reconstruction Algorithms for Real-Time Explosive Detection applications

Optimizing Convergence Rates of Alternating Minimization Rec...

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SPIE Anomaly Detection and imaging with X-Rays (ADIX) conference

作者： Bosch, Carl Degirmenci, Soysal Barlow, Jason Mesika, Assaf Politte, David G. O'Sullivan, Joseph A. SureScan Corp 2507 Wayne St Endicott NY 13760 USA Washington Univ Dept Elect & Syst Engn 1 Brookings Dr St Louis MO 63130 USA Washington Univ Sch Med Mallinckrodt Inst Radiol 510 South Kingshighway Blvd St Louis MO 63110 USA

ISBN: (纸本)9781510600881

X-ray computed tomography reconstruction for medical, security and industrial applications has evolved through 40 years of experience with rotating gantry scanners using analytic reconstruction techniques such as filtered back projection (FBP). In parallel, research into statistical iterative reconstruction algorithms has evolved to apply to sparse view scanners in nuclear medicine, low data rate scanners in Positron Emission Tomography (PET) [5, 7, 10] and more recently to reduce exposure to ionizing radiation in conventional X-ray CT scanners. Multiple approaches to statistical iterative reconstruction have been developed based primarily on variations of expectation maximization (EM) algorithms. The primary benefit of EM algorithms is the guarantee of convergence that is maintained when iterative corrections are made within the limits of convergent algorithms. The primary disadvantage, however is that strict adherence to correction limits of convergent algorithms extends the number of iterations and ultimate timeline to complete a 3D volumetric reconstruction. Researchers have studied methods to accelerate convergence through more aggressive corrections [1], ordered subsets [1, 3, 4, 9] and spatially variant image updates. In this paper we describe the development of an AM reconstruction algorithm with accelerated convergence for use in a real-time explosive detection application for aviation security. By judiciously applying multiple acceleration techniques and advanced GPU processing architectures, we are able to perform 3D reconstruction of scanned passenger baggage at a rate of 75 slices per second. Analysis of the results on stream of commerce passenger bags demonstrates accelerated convergence by factors of 8 to 15, when comparing images from accelerated and strictly convergent algorithms

关键词： Reconstruction regularization computed tomography convergence explosive detection accelerated convergence

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