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检索条件"任意字段=Image Processing: Algorithms and Systems X;and Parallel Processing for Imaging Applications II"

共 1177 条记录，以下是171-180 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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Making Halide Efficient for Multicore systems 4

Making Halide Efficient for Multicore Systems

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4th International Conference on Big Data Computing and Communications (BIGCOM)

作者： Zhang, Yu Zhang, Yuxiang Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China

ISBN: (纸本)9781538680216

Graphics and imaging applications, especially emerging medical imaging or self-driving cars, demand orders of magnitude more computation, and require efficient image processing pipeline implementations. Halide is domain-specific language and compiler designed to make it easier write high-performance image processing code. It separates what is being computed from how to compute, enabling programmers to tune to find a high-performance schedule. However, Halide applications suffer poor scalability on large number of CPU cores. In this paper, we first perform a detailed parallelism and performance analysis of Halide applications on a 4x 8-physical-core Linux system. Our analysis shows that page faults and contention on shared address space as well as unaligned packed data movements are part of reasons for poor scalability. To improve the performance of Halide, we propose a novel thread model-STHREAD-and adopt it to reimplement Halide runtime, i.e. SHALIDE, without modifying the Halide language, compiler and the runtime interface. The STHREAD model allows each thread has its own memory management structure, and directly shares heap objects, globals and stack data with other threads. On six image processing benchmarks, SHALIDE shows better speedup and gives up to 1.53 x faster than the original Halide.

关键词： Benchmark testing Schedules Task analysis image processing parallel processing Instruction sets Scalability

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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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A method for automatic analysis of scanned 3D models of human hands and feet 5

A method for automatic analysis of scanned 3D models of huma...

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Conference on Optics, Photonics, and Digital Technologies for imaging applications V

作者： Kovalovs, M. Riga Tech Univ Dept Image Proc & Comp Graph 1 Setas St LV-1048 Riga Latvia

ISBN: (数字)9781510618855

ISBN: (纸本)9781510618855

The automatic analysis of 3D models of hands and feet could be useful in many areas. The main idea of the proposed method is to create cross section 2D images of the scanned 3D model that could be later analysed using any image processing algorithms. These cross section 2D images are created by placing any number of parallel planes inside the 3D model and calculating the intersections between the 3D model and the plane. The cross section images could then be used to create a "bone structure" of the scanned 3D model by using any existing image processing algorithms in order to find the central points of the 3D model in each cross section image and connecting these points between the consecutive cross section images. This "bone structure" could then be used to determine the orientation of the scanned model in 3D space in order to create more cross section images at specific locations as well as acquiring basic information about the shape of the hand or foot or which fingers are missing that could be used when creating the 3D model of the prosthesis. However, acquiring accurate dimensional information about the scanned 3D model would require additional input from the user to define the scale of the 3D model.

关键词： 3D analysis 3D models 3D scanning cross section image

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Multi-sensor array for polarimetric light-field imaging 5

Multi-sensor array for polarimetric light-field imaging

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Conference on Optics, Photonics, and Digital Technologies for imaging applications V

作者： Nie, Yunsong Zi, Chongde Li, Yunqian Yue, Tao Cao, xun Beijing Inst Space Mech & Elect Beijing Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (数字)9781510618855

ISBN: (纸本)9781510618855

Both light-field and polarization information contain lots of clues about scenes, and they can be widely used in variety of computer vision tasks. However, existing imaging systems cannot simultaneously capture the light-field and polarization information. In this paper, we present a low-cost and high-performance miniaturized polarimetric light-field camera, which is based on the six heterogeneous sensors array. The main challenge for the proposed strategy is to align the multi-view images with different polarization characteristics, especially for regions with high degree of polarization -- in which the intensity correlations are commonly weak. To solve this problem, we propose to use Convolutional Neural Network (CNN) based stereo matching method for aligning the heterogeneously polarized images accurately. After stereo matching, both the light field and the Stokes vectors of scene are estimated, and the polarimetry conventions, e.g., the polarization angle, the linear polarization degree and the circular polarization degree, are given. We implement the prototype of the multi-sensor polarization light-field camera and perform extensive experiments on it. The polarimetric light-field camera achieves six live streaming on time and the heterogeneous processor of NVIDIA Jetson Tx2 is exploited for image processing. Benefiting from the multi-sensor parallel polarization imaging and efficient parallel processing, the proposed system achieves promising performance on time resolution, signal-to-noise ratio. Besides, we develop the object recognition applications to show the superiorities of proposed system.

关键词： polarimetric imaging multi-sensor light-field convolutional neural network Stokes parameters image registration

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Towards a smart selection of resources in the cloud for low-energy multimedia processing

Towards a smart selection of resources in the cloud for low-...

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Concurrency Computation

作者： Mahmoudi, Sidi Ahmed Belarbi, Mohammed Amin Mahmoudi, Saïd Belalem, Ghalem University of Mons Faculty of Engineering Computer science department 20 Place du Parc Mons Belgium Faculty of Exact Sciences and Computer Science Algeria University of Oran1 Computer science department Faculty of Exact and Applied Science Algeria

Nowadays, image and video processing applications have become widely used in many domains related to computer vision. Indeed, they can come from cameras, smartphones, social networks, or from medical devices. Generally, these images and videos are used for illustrating people or objects (cars, trains, planes, etc) in many situations such as airports, train stations, public areas, sport events, and hospitals. Thus, image and video processing algorithms have got increasing importance, they are required from various computer vision applications such as motion tracking, real time event detection, database (images and videos) indexation, and medical computer-aided diagnosis methods. Themain inconvenient of image and video processing applications is the high intensity of computation and the complex configuration and installation of the related materials and libraries. In this paper, we propose a new framework that allows users to select in a smart and efficient way the computing units (CPUor/andGPU) in a cloud-based platform, in case of processing one image (or one video in real time) or many images (or videos). This framework enables to affect the local or remote computing units for calculation after analyzing the type of media and the algorithm complexity. The framework disposes of a set of selected CPU and GPU-based computer vision methods, such as image denoising, histogram computation, features descriptors (SIFT, SURF), points of interest extraction, edges detection, silhouette extraction, and sparse and dense optical flow estimation. These primitive functions are exploited in various applications such as medical image segmentation, videos indexation, real timemotion analysis, and left ventricle segmentation and tracking from 2D echocardiography. Experimental results showed a global speedup ranging from 5× to 273× (compared to CPU versions) as result of the application of our framework for the above-mentioned methods. In addition to these performances, the parallel a

关键词： Medical image processing Biomedical equipment Cloud computing Complex networks Computational complexity Computer aided diagnosis Computer vision Echocardiography Extraction Graphics processing unit image denoising image segmentation Medical imaging mHealth Motion analysis Multimedia systems Video signal processing Computer vision applications Heterogeneous architectures image and video processing Lower power consumption Motion tracking real Time event detections Segmentation and tracking Silhouette extraction

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A Configuration Data Multicasting Method for Coarse-Grained Reconfigurable Architectures 28

A Configuration Data Multicasting Method for Coarse-Grained ...

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28th International Conference on Field Programmable Logic and applications (FPL)

作者： Kojima, Takuya Amano, Hideharu Keio Univ Dept Informat & Comp Sci Yokohama Kanagawa Japan

ISBN: (数字)9781538685174

ISBN: (纸本)9781538685174

This paper proposes a novel configuration data compression technique for coarse-grained reconfigurable architectures (CGRAs). The proposed technique is based on a multicast configuration technique called RoMultiC, which reduces the configuration time by multicasting the same data to multiple PEs(processing Elements) with two bit-maps. Scheduling algorithms for an optimizing the order of multicasting have been proposed. In general, configuration data for CGRAs can be divided into some fields like machine code formats. The proposed scheme confines a part of fields for multicasting so that the possibility of multicasting more PEs can be increased. This paper analyzes algorithms to find a configuration pattern which maximizes the number of multicasted PEs. We implemented the proposed scheme to CMA (Cool Mega Array), a straight forward CGRA as a case study. Experimental results show that the proposed method achieves 40.0% smaller configuration for an image processing application at maximum. Furthermore, it achieves 35.6% reduction of the power consumption for the configuration with a negligible area overhead.

关键词： Multicast communication Multicast algorithms Arrays Power demand parallel processing Hardware

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Multi-GPU Acceleration of Iterative x-ray CT image Reconstruction

Multi-GPU Acceleration of Iterative X-ray CT Image Reconstru...

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作者： Mitra Ayan Washington University in St. Louis Electrical Engineering

学位级别：Ph.D.

x-ray computed tomography is a widely used medical imaging modality for screening and diagnosing diseases and for image-guided radiation therapy treatment planning. Statistical iterative reconstruction SIR) algorithms have the potential to significantly reduce image artifacts by minimizing a cost function that models the physics and statistics of the data acquisition process in x-ray CT. SIR algorithms have superior performance compared to traditional analytical reconstructions for a wide range of applications including nonstandard geometries arising from irregular sampling, limited angular range, missing data, and low-dose CT. The main hurdle for the widespread adoption of SIR algorithms in multislice x-ray CT reconstruction problems is their slow convergence rate and associated computational *** seek to design and develop fast parallel SIR algorithms for clinical x-ray CT scanners. Each of the following approaches is implemented on real clinical helical CT data acquired from a Siemens Sensation 16 scanner and compared to the straightforward implementation of the Alternating Minimization AM) algorithm of OSullivan and Benac [1]. We parallelize the computationally expensive projection and backprojection operations by exploiting the massively parallel hardware architecture of 3 NVIDIA TITAN x Graphical processing Unit GPU) devices with CUDA programming tools and achieve an average speedup of 72x over a straightforward CPU implementation. We implement a multi-GPU based voxel-driven multislice analytical reconstruction algorithm called Feldkamp-Davis-Kress FDK) [2] and achieve an average overall speedup of 1382x over the baseline CPU implementation by using 3 TITAN x GPUs. Moreover, we propose a novel adaptive surrogate-function based optimization scheme for the AM algorithm, resulting in more aggressive update steps in every iteration. On average, we double the convergence rate of our baseline AM algorithm and also improve image quality by using the adaptive surro

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Multi-GPU Acceleration of Iterative x-ray CT image Reconstruction

Multi-GPU Acceleration of Iterative X-ray CT Image Reconstru...

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作者： Mitra, Ayan Washington University in St. Louis

学位级别：Ph.D.

x-ray computed tomography is a widely used medical imaging modality for screening and diagnosing diseases and for image-guided radiation therapy treatment planning. Statistical iterative reconstruction (SIR) algorithms have the potential to significantly reduce image artifacts by minimizing a cost function that models the physics and statistics of the data acquisition process in x-ray CT. SIR algorithms have superior performance compared to traditional analytical reconstructions for a wide range of applications including nonstandard geometries arising from irregular sampling, limited angular range, missing data, and low-dose CT. The main hurdle for the widespread adoption of SIR algorithms in multislice x-ray CT reconstruction problems is their slow convergence rate and associated computational time. We seek to design and develop fast parallel SIR algorithms for clinical x-ray CT scanners. Each of the following approaches is implemented on real clinical helical CT data acquired from a Siemens Sensation 16 scanner and compared to the straightforward implementation of the Alternating Minimization (AM) algorithm of O’Sullivan and Benac [1]. We parallelize the computationally expensive projection and backprojection operations by exploiting the massively parallel hardware architecture of 3 NVIDIA TITAN x Graphical processing Unit (GPU) devices with CUDA programming tools and achieve an average speedup of 72x over a straightforward CPU implementation. We implement a multi-GPU based voxel-driven multislice analytical reconstruction algorithm called Feldkamp-Davis-Kress (FDK) [2] and achieve an average overall speedup of 1382x over the baseline CPU implementation by using 3 TITAN x GPUs. Moreover, we propose a novel adaptive surrogate-function based optimization scheme for the AM algorithm, resulting in more aggressive update steps in every iteration. On average, we double the convergence rate of our baseline AM algorithm and also improve image quality by using the adaptive

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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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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： 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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