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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121-130 订阅

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On the parallel classification system using hyperspectral images for remote sensing applications

On the parallel classification system using hyperspectral im...

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conference on Real-Time Image and Video processing

作者： Garcia-Salgado, Beatriz P. Ponomaryov, Volodymyr I. Robles-Gonzalez, Marco A. Sadovnychiy, Sergiy Inst Politecn Nacl ESIME Cu Mexico Mexico City DF Mexico Inst Mexicano Petr Mexico Mexico City DF Mexico

ISBN: (数字)9781510618527

ISBN: (纸本)9781510618527

This work is orientated towards time optimization of the hyperspectral images classification. This kind of images represents an immense computational cost in the course of processing, particularly in tasks such as feature extraction and classification. In fact, numerous techniques in the state-of-the-art have suggested a reduction in the dimension of the information. Nevertheless, real-time applications require a fast information shrinkage with a feature extraction included in order to conduce to an agile classification. To solve the mentioned problem, this study is composed of a time and algorithm complexity comparison between three different transformations: Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). Furthermore, three feature selection criteria are likewise analyzed: Jeffries-Matusita Distance (JMD), Spectral Angle Mapper (SAM) and the unsupervised algorithm N-FINDR. An application that takes into consideration the study previously described is developed performing the parallel programming paradigm in multicore mode via utilizing a cluster of two Raspberry Pi units and, comparing it in time and algorithm complexity with the sequential paradigm. Moreover, a Support Vector Machine (SVM) is incorporated in the application to perform the classification. The images employed to test the algorithms were acquired by the Hyperion sensor, the Airborne Visible/Infrared imaging Spectrometer (AVIRIS), and the Reflective Optics System imaging Spectrometer (ROSIS).

关键词： Hyperspectral image Feature extraction CPU multicores

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Faster image compression technique based on LZW algorithm using GPU parallel processing 7

Faster image compression technique based on LZW algorithm us...

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Joint 7th International conference on Informatics, Electronics and Vision and 2nd International conference on imaging, Vision and Pattern Recognition, ICIEV-IVPR 2018

作者： Alam, Md. Ashraful Ahsan, Fakhrul Soobhee, Ateeq-Ur-Rahman Subhani, Mahfuze Fahmid Hossain, F.M. Saiful Islam, Md. Ruma, Kamrun Nahar Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (纸本)9781538651612

As web, mobile, desktop and all other applications use image for different purposes, image compression technique has become one of the most important applications in image analysis as well as in computer science. Though image compression is an old concept, it is still a very time-consuming process which has opened a new field of research in image compression. In this paper, we showcase our extensive research works performed on LZW (Lempel-Ziv-Welch) algorithm, a lossless image compression algorithm that yields faster computation time when it is processed with parallel computation, based on the outcome of our research. The improved time efficiency obtained from our experimental result can be perceived in the context of millisecond scale rather than in the macro level of measurement along with keeping the decoded image in lossless format. © 2018 IEEE.

关键词： Image compression

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Denoising and dimensionality reduction based on PARAFAC decomposition for hyperspectral images

Denoising and dimensionality reduction based on PARAFAC deco...

引用

conference on Optical Sensing and imaging Technologies and applications / International Symposium on Optoelectronic Technology and Application (OTA) / Annual conference of the Chinese-Society-for-Optical-Engineering (CSOE)

作者： Yan Rong-hua Peng Jin-ye Wen De-sheng Ma Dong-mei Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Shaanxi Peoples R China Chinese Acad Sci Inst Opt & Precis Mech Xian 710119 Shaanxi Peoples R China Northwest Univ Sch Informat & Technol Xian 710127 Shaanxi Peoples R China Xian Janssen Pharmaceut Ltd Xian 710043 Shaanxi Peoples R China

ISBN: (纸本)9781510623354

In hyperspectral image analysis, classification requires spectral dimensionality reduction (DR). Tensor decompositions have been successfully applied to joint noise reduction in spatial and spectral dimensions of hyperspectral images, such as parallel factor analysis (PARAFAC). However, the PARAFAC method does not reduce the dimension in the spectral dimension. To improve it, a new method was proposed in this paper, that is, combine PCA and PARAFAC to reduce both the dimension in the spectral dimension and the noise in the spatial and spectral dimensions. The experiment results indicate that the new method improves the classification compared with the previous methods.

关键词： dimensionality reduction tensor processing hyperspectral image (HSI) parallel factor analysis (PARAFAC)

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Cylindrical sample space imaging and material BRDF performance measurement

Cylindrical sample space imaging and material BRDF performan...

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作者： Wu Houping Gan Haiyong Feng Guojin He Yingwei Liu Zilong Liu Sen Natl Inst Metrol Beijing 100029 Peoples R China Beijing Technol & Business Univ Beijing 100048 Peoples R China

ISBN: (纸本)9781510623330

In the field of space exploration, it is often necessary to detect and identify the target under specific light source illumination and different observation angle conditions. Due to different shapes of the sample at different wavelengths of illumination source and geometry measurement conditions, the appearance of the obtained sample images are very different, and there are great differences between the apparent scale of the image and the inherent dimensions of the actual object. Using a standard reflector to calibrate the imaging system, the image or data can be corrected. In this paper, cylindrical sample is selected as the research object, a beam of 680nm parallel monochromatic light source is used for illumination, and the digital camera is used to photograph at a certain angular interval in the range of observation angle (0-75 degrees), and the geometric dimension of the image pixel is calibrated. Through the calibration of image pixel geometry and the calculation of image gray value, the relationship between the gray value changes and the geometrical dimensions of the images captured under different imaging geometry conditions is studied. With the same wavelength light source lighting conditions, a bi-directional reflection distribution function (BRDF) calibration device was used to measure BRDF under multiple geometric conditions for the same material flat material. The BRDFs of material was used to study the appearance of the target sample, and the relationship between the image of the target sample and the corresponding BRDF of the material under different geometric conditions was analyzed and compared. A series of image processing data and methods for cylindrical samples are given in this paper, which has certain practical significance for the study of material properties and target imaging.

关键词： BRDF measurement Geometric conditions image processing gray value

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The Challenge of a Strong Speed-Up of a Bio-Medical Big Data Application

The Challenge of a Strong Speed-Up of a Bio-Medical Big Data...

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IEEE International conference on Big Data (Big Data)

作者： Strutz, Marco Lindequist, Bjoern Hessling, Hermann Streit, Achim Univ Appl Sci HTW Berlin Wilhelminenhofstr 75A D-12459 Berlin Germany KIT SCC Hermann von Helmholtz Pl 1 D-76344 Eggenstein Leopoldshafen Germany

ISBN: (纸本)9781538650356

Digital data of patients can aid a pathologist along the diagnostic process In Medical devices generate data sets that are processed by specialized computing applications, which often run on a single computer. The resolution power of the devices is increasing steadily and, consequently, the volumes of the data sets are also growing and can no longer be analyzed in a reasonable amount of time. Big Data tools like Apache Spark [2] provide methods for analyzing data, however, they are not directly applicable and need considerable implementation efforts, in general. Usually, well established analysis tools for medical data are designed to run on single workstations. These tools are not designed to meet current and future challenges. Migrating processing tools from single nodes to distributed environments is nontrivial. Moreover, partitioning data sets for a parallel processing is a further challenge [3]. In this work, we continue our efforts for improving the speedup of a bio-medical big data application further by partitioning the images of a Whole Slide Image (WSI) [4] into sub tiles and by analyzing these sub tiles on a cluster of computer nodes. The idea is to benefit from the divide and conquer strategy. However, it is shown that the score parameter is determined incorrectly, when the software package is applied to each sub tile and the score parameters of all sub tiles are combined in an apparently natural manner. The cause of this anomaly is determined and a solution suggested. The original software is based on implicit assumptions. For example, the size of the tiles is assumed to be 1024 x 1024 px(2). The anomaly shows up when this constraint is reduced.

关键词： Runtime Tumors Tools Big Data Medical diagnostic imaging parallel processing

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Edge detection in planet image using an improved partial area effect algorithms

Edge detection in planet image using an improved partial are...

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作者： Zhang, Yong Jiang, Jie Zhang, Guangjun Beihang Univ Sch Instrumentat Sci & Optoelect Engn Beijing 100191 Peoples R China Beihang Univ Key Lab Precis Optomech Technol Minist Educ Beijing 100191 Peoples R China

ISBN: (纸本)9781510623354

The images of a planet captured by the optical sensor system can provide a lot of serviceable navigation information for space exploration missions. Accurate measurement of a planet centroid is one of the important tasks for deep-space autonomous optical navigation. In order to improve the accuracy of centroid localization, a novel sub-pixel edge detection algorithm is proposed. First, image pre-processing technology is adopted to eliminate the effect of stars and stray light. Second, the edge of a planet at the pixel level is extracted using Sobel's operator. Next, a new sub-pixel edge extraction algorithm of adaptive rotating template is proposed based on an improved partial area effect algorithm. Partial area effect algorithm is an effective edge detector based on an edge and acquisition model, which using the vertical or parallel template. We construct a new rotatable adaptive template, which is to align the direction of the template with the gradient direction of the edge pixel points. Finally, we estimated the planet center using least squares ellipse fitting. The result indicates that the algorithm can achieve higher location accuracy and robustness to noise.

关键词： Navigation Image processing Sub-pixel edge detection Centroid extracting

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Hardware Acceleration in Genode OS Using Dynamic Partial Reconfiguration 31st

Hardware Acceleration in Genode OS Using Dynamic Partial Rec...

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31st International conference on Computer Architecture (ARCS)

作者： Doerflinger, Alexander Albers, Mark Fiethe, Bjoern Michalik, Harald TU Braunschweig Inst Comp & Network Engn IDA Braunschweig Germany

ISBN: (纸本)9783319776101;9783319776095

Algorithms with operations on large regular data structures such as image processing can be highly accelerated when executed as hardware tasks in an FPGA fabric. The Dynamic Partial Reconfiguration (DPR) feature of new SRAM-based FPGA families allows a dynamic swapping and replacement of hardware tasks during runtime. Particularly embedded systems with processing chains that change over time or that are too large to be implemented in an FPGA fabric in parallel, benefit from DPR. In this paper we present a complete framework for hardware acceleration using DPR in the microkernel based Genode OS. This makes the DPR feature available not only for the high-performance computing field, but also for safety-critical applications. The new framework is evaluated for an exemplary imaging application running on a Xilinx Zynq-7000 SoC.

关键词： Image processing

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GPU Assisted Towards Real-Time Reconstruction for Dual-Camera Compressive Hyperspectral imaging 19th

GPU Assisted Towards Real-Time Reconstruction for Dual-Camer...

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19th Pacific-Rim conference on Multimedia (PCM)

作者： Zhang, Shipeng Wang, Lizhi Fu, Ying Huang, Hua Xi An Jiao Tong Univ Xian 710049 Shaanxi Peoples R China Beijing Inst Technol Beijing 100081 Peoples R China

ISBN: (纸本)9783030007768;9783030007751

The dual-camera compressive hyperspectral imager (DCCHI) can capture 3D hyperspectral image (HSI) with a single snapshot. However, due to the high computation complexity of reconstruction methods, DCCHI cannot apply to the time-crucial applications. In this paper, we propose a GPU assisted towards real-time reconstruction framework for DCCHI. First, leveraging the fast convergence rate of the alternative direction multiplier method, we propose a reformative reconstruction algorithm which can achieve a fast convergence rate. Then, using the interpolation results of a low resolution reconstructed HSI as the warm start, we propose a fast reconstruction strategy to further reduce the computation burden. Last, a GPU parallel implementation is presented to achieve nearly real-time reconstruction. Evaluation experiments indicate our framework can obtain a significant promotion in reconstruction efficiency with a slight accuracy loss.

关键词： parallel optimization Alternative direction multiplier method Real-time reconstruction Compressive hyperspectral imaging

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Simplifying the multi-GPU programming of a hyperspectral image registration algorithm

Simplifying the multi-GPU programming of a hyperspectral ima...

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International conference on High Performance Computing & Simulation (HPCS)

作者： Jorge Fernàndez-Fabeiro Arturo Gonzalez-Escribano Diego R. Llanos Departamento de Informaticá Universidad de Valladolid Valladolid Spain

ISBN: (数字)9781728144849

ISBN: (纸本)9781728144856

Hyperspectral image registration is a relevant task for real-time applications like environmental disasters management or search and rescue scenarios. Traditional algorithms for this problem were not really devoted to real-time performance. The HYFMGPU algorithm arose as a high-performance GPU-based solution to solve such a lack. Nevertheless, a single-GPU solution is not enough, as sensors are evolving and then generating images with finer resolutions and wider wavelength ranges. An MPI+CUDA multi-GPU implementation of HYFMGPU was previously presented. However, this solution shows the programming complexity of combining MPI with an accelerator programming model. In this paper we present a new and more abstract programming approach for this type of applications, which provides a high efficiency while simplifying the programming of the multi-device parts of the code. The solution uses Hitmap, a library to ease the programming of parallel applications based on distributed arrays. It uses a more algorithm-oriented approach than MPI, including abstractions for the automatic partition and mapping of arrays at runtime with arbitrary granularity, as well as techniques to build flexible communication patterns that transparently adapt to the data partitions. We show how these abstractions apply to this application class. We present a comparison of development effort metrics between the original MPI implementation and the one based on Hitmap, with reductions of up to 95% for the Halstead score in specific work redistribution steps. We finally present experimental results showing that these abstractions are internally implemented in a high efficient way that can reduce the overall performance time in up to 37% comparing with the original MPI implementation.

关键词： Programming Graphics processing units Hyperspectral imaging Real-time systems Libraries Principal component analysis

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Performance Comparison of GPU-Accelerated Fast Motion Estimation Method

Performance Comparison of GPU-Accelerated Fast Motion Estima...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Pengcheng Chen Bo Peng Anxin Zou Luwen Xu School of Computer Science Southwest Petroleum University Chengdu China Chongqing Electric Power Company Electric Power Research Institute Chongqing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Motion estimation is an important part of ultrasound strain elastography. The sum-table based motion estimation method, which can effectively reduce computation time and redundant calculation, is a fast motion estimation approach. Except for NCC, it can also be extended to other motion estimators. The main work and contribution of this paper is to implement different motion estimators (CC, NCC, SSD, SAD) on GPU platform based on the sum-table method and to compare the performance of these motion estimators with the classical method on GPU. The difference of computational cost of each estimator based on the sum-table method mainly lies in the construction of the sum-table. The computation burden per thread is small, so data addressing is the dominant work per thread. It has been reported that the sum-table method does not have particular advantage over the classical method with a large search range on GPU platform. However, it possesses good performance with a huge tracking kernel window.

关键词： Microsoft Windows Graphics processing units Motion estimation Ultrasonic imaging Estimation Elastography Computational efficiency

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