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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Meifang Gu Hailin Tang Lili Li Institutional Research Center Guangdong Baiyun University Guangzhou Guangdong China Faculty of Intelligent Manufacturing Engineering Guangdong Baiyun University Guangzhou Guangdong China Faculty of Megadata and Computing Guangdong Baiyun University Guangzhou Guangdong China

Face recognition is an important technology in the field of digital image processing, which has a crucial impact on subsequent work. This article introduced an example of facial recognition based on the combination of Hadoop algorithm and facial feature extraction. The experimental results were validated in the MATLAB environment and trained using neural networks to improve system performance and adaptability. The test results indicated that the processing speed was between 0.84 and 0.97; the highest resource utilization rate was 0.06, and the lowest was 0.02; the throughput range of facial data was between 13598 and 15479, and the throughput range of user data was between 5037 and 5879. This indicated that the system had fast recognition speed and high-precision image output, and exhibited good fault tolerance and robustness compared to other methods. Exploring facial image recognition and processing algorithms in the Hadoop environment can utilize Hadoop’s distributed computing capabilities to accelerate the processing of large-scale facial image data, achieve fast and efficient processing of large-scale facial image data, accelerate algorithm operation, and possess distributed computing capabilities. Multiple computing nodes can be used for parallel processing to improve algorithm performance.

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Accelerating Hyperspectral Anomaly Detection With Enhanced Multivariate Gaussianization Based on FPGA

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Yu, Kun Wu, Zebin Sun, Jin Zhang, Yi Xu, Yang Wei, Zhihui Zheng, Shangdong Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China

Hyperspectral anomaly detection (AD), as a frontier research topic in the field of remotely sensed data processing, aims to identify targets of interest from complex and vast images. Existing AD methods typically involve complex models and many parameters, posing challenges in meeting the requirements of computational efficiency in hyperspectral AD. To address this issue, this article presents an AD acceleration algorithm based on the multivariate Gaussian model as well as its field programmable gate array (FPGA) implementation. By exploiting the parallel processing capabilities of FPGA, we introduce an innovative spectral dimensionality reduction method in which the data processing flow can be accomplished in a distributed manner. Then, we employ an improved linear rotation strategy based on correlation coefficients to accelerate the convergence rate of the proposed AD algorithm. The rotation of Gaussianization in the improved strategy is independent of eigenvalue decomposition, thereby substantially reducing the computational complexity involved during the rotation procedure. Furthermore, we apply a pipeline parallel mechanism to facilitate the FPGA implementation of the AD algorithm and to significantly enhance the computational efficiency. Experimental results on an embedded FPGA platform demonstrate that the FPGA implementation of the hyperspectral AD algorithm proposed in this article achieves a significant acceleration rate with guaranteed high detection accuracy.

关键词： Field programmable gate arrays Hyperspectral imaging Computational modeling Detectors Convergence Computational efficiency Computational complexity Classification algorithms Accuracy Reliability Anomaly detection (AD) field programmable gate array (FPGA) hyperspectral image (HSI) linear rotation

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RGBT Tracking via Multi-Adapter Network with Hierarchical Divergence Loss

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IEEE TRANSACTIONS ON image processing 2021年 30卷 5613-5625页

作者： Lu, Andong Li, Chenglong Yan, Yuqing Tang, Jin Luo, Bin Anhui Univ Sch Artificial Intelligence Informat Mat & Intelligent Sensing Lab Anhui Prov Anhui Prov Key Lab Multimodal Cognit Computat Hefei 230601 Peoples R China

RGBT tracking has attracted increasing attention since RGB and thermal infrared data have strong complementary advantages, which could make trackers all-day and all-weather work. Existing works usually focus on extracting modality-shared or modality-specific information, but the potentials of these two cues are not well explored and exploited in RGBT tracking. In this paper, we propose a novel multi-adapter network to jointly perform modality-shared, modality-specific and instance-aware target representation learning for RGBT tracking. To this end, we design three kinds of adapters within an end-to-end deep learning framework. In specific, we use the modified VGG-M as the generality adapter to extract the modality-shared target representations. To extract the modality-specific features while reducing the computational complexity, we design a modality adapter, which adds a small block to the generality adapter in each layer and each modality in a parallel manner. Such a design could learn multilevel modality-specific representations with a modest number of parameters as the vast majority of parameters are shared with the generality adapter. We also design instance adapter to capture the appearance properties and temporal variations of a certain target. Moreover, to enhance the shared and specific features, we employ the loss of multiple kernel maximum mean discrepancy to measure the distribution divergence of different modal features and integrate it into each layer for more robust representation learning. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against the state-of-the-art methods.

关键词： Feature extraction Target tracking Data mining Mobile computing Adaptation models Ad hoc networks Kernel RGBT tracking multiple adapters parallel design hierarchical divergence loss representation learning

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parallel vs distributed Edge Detection for Large Medical image Datasets

Parallel vs Distributed Edge Detection for Large Medical Ima...

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E-Health and Bioengineering Conference (EHB)

作者： Renato Constantin Ivanescu Rodica Nagy Anca Istrate Ofiteru Cristina Comanescu Alexandru Manda Department of Computers and Information Technology University of Craiova Craiova Romania University of Medicine and Pharmacy of Craiova University of Craiova Craiova Romania

ISBN: (数字)9781665485579

ISBN: (纸本)9781665485586

The medical routine of the future is strongly influenced by medical information technology. The quality and efficiency of medicine are at higher standards due to the image-based methods and the increase in computation power. parallel and distributed computing are attractive alternatives to improve processing performance in many areas of study and this paper investigates such solutions for increasing the throughput on large datasets in medical imaging. In this research, different edge detection techniques are used for image processing, as it is the most common and challenging task in the medical domain. The results are relevant and present the effectiveness of the two processing approaches compared to a single CPU for the acceleration of medical image processing. The CUDA processing platform using a hybrid streams-based programming model is the parallel approach. The distributed system is a Linux-based cluster, which uses message passing for communication and efficient load balancing between nodes for high performance. This paper highlights parallel computing as a very good solution for processing large image datasets in the medical domain. Good results were also obtained for the distributed system.

关键词： image processing image edge detection Graphics processing units Streaming media Programming Load management Task analysis

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Jigsaw: A Slice-and-Dice Approach to Non-uniform FFT Acceleration for MRI image Reconstruction 35

Jigsaw: A Slice-and-Dice Approach to Non-uniform FFT Acceler...

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35th IEEE International parallel and distributed processing Symposium (IPDPS)

作者： West, Brendan L. Fessler, Jeffrey A. Wenisch, Thomas F. Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781665440660

The Fast Fourier Transform (FFT) is a fundamental algorithm in signal processing;significant efforts have been made to improve its performance using software optimizations and specialized hardware accelerators. Computational imaging modalities, such as MRI, often rely on the Non-uniform Fast Fourier Transform (NuFFT), a variant ()I' the EFT for processing data acquired from non-uniform sampling patterns. The most time-consuming step of the NuFFT algorithm is "gridding," wherein non-uniform samples are interpolated to allow a uniform FFT to be computed over the data. Each non-uniform sample affects a window of non-contiguous memory locations, resulting in poor cache and memory bandwidth utilization. As a result, gridding can account for more than 99.6% of the NuFFT computation time, while the FFT requires less than 0.4%. We present Slice-and-Dice, a novel approach to the NuFFT's gridding step that eliminates the presorting operations required by prior methods and maps more efficiently to hardware. Our GPI! implementation achieves gridding speedups of over 250x and 16x vs prior ***-art CPU and GPU implementations, respectively. We achieve further speedup and energy efficiency gains by implementing Slice-and-Dice in hardware with JIGSAW, a streaming hardware accelerator for non-uniform data gridding. JIGSAW uses stall-free fixed-point pipelines to process M non-uniform samples in approximately M cycles, irrespective of sampling pattern-yielding speedups of over 1500x the CPU baseline and 36x the state-of-the-art GPU implementation, consuming similar to 200 mW power and similar to 12 mm(2) area in 16 nm technology. Slice-and-Dice GPU and JIGSAW ASIC implementations achieve unprecedented end-to-end NuFFT speedups of 8x and 36x compared to the state-of-the-art GPU implementation, respectively.

关键词： Runtime Power demand Fast Fourier transforms Magnetic resonance imaging Software algorithms Graphics processing units Signal processing algorithms

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SDCRKL-GP: Scalable deep convolutional random kernel learning in gaussian process for image recognition

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NEUROCOMPUTING 2021年 456卷 288-298页

作者： Wang, Tingting Xu, Lei Li, Junbao Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Heilongjiang Peoples R China

Deep convolutional neural networks have shown great potential in image recognition tasks. However, the fact that the mechanism of deep learning is difficult to explain hinders its development. It involves a large amount of parameter learning, which results in high computational complexity. Moreover, deep convolutional neural networks are often limited by overfitting in regimes in which the number of training samples is limited. Conversely, kernel learning methods have a clear mathematical theory, fewer parameters, and can contend with small sample sizes;however, they are not able to handle high-dimensional data, e.g., images. It is important to achieve a performance and complexity trade-off in complicated tasks. In this paper, we propose a novel scalable deep convolutional random kernel learning in Gaussian process architecture called SDCRKL-GP, which is characterized by excellent performance and low complexity. First, we successfully incorporated the deep convolutional architecture into kernel learning by implementing the random Fourier feature transform for Gaussian processes, which can effectively capture hierarchical and local image-level features. This approach enabled the kernel method to effectively handle image processing problems. Second, we optimized the parameters of deep convolutional filters and Gaussian kernels by stochastic variational inference. Then, we derived the lower variational bound of the marginal likelihood. Finally, we explored the model architecture design space selection method to determine the appropriate network architecture for different datasets. The design space consists of the number of layers, the channels per layer, and so on. Different design space selections improved the scalability of the SDCRKL-GP architecture. We evaluated SDCRKL-GP on the MNIST, FMNIST, CIFAR10, and CALTECH4 benchmark datasets. Taking MNIST as an example, the error rate of classification is 0.60%, and the number of parameters, number of computations and memo

关键词： Kernel method Gaussian process Random Fourier feature Convolutions Network design space search

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Initial Results on the Effectiveness of distributed Edge Detection for Large image Data Sets

Initial Results on the Effectiveness of Distributed Edge Det...

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IEEE Southeastcon

作者： Ivanescu Constantin Renato Mocanu Mihai University of Craiova Craiova Romania

image data is expanding rapidly, along with technology development, so efficient solutions must be considered to achieve high, real-time performance in the case of processing large image datasets. parallel processing is increasingly used as an attractive alternative to improve the performance, when using existing distributed architectures but also for sequential commodity computers. It can provide speedup, efficiency, reliability, incremental growth, and flexibility. We present such an alternative and stress the effectiveness of the methods to accelerate computations on a small cluster of PCs compared to a single CPU. Our paper is focused on applying edge detection on large image data sets, as a fundamental and challenging task in image processing and computer vision. Five different techniques, mainly Sobel, Prewitt, LoG, Canny, and Roberts, are compared in a simple experimental setup that includes the OpenCV library functions for image pixels manipulation. Gaussian blur is used to reduce high-frequency components to manage the noise that edge detection is impacted by. Overall, this work is part of a more extensive investigation of image segmentation methods on large image datasets, but the results presented are relevant and show the effectiveness of our approach.

关键词： Computers image segmentation image edge detection distributed databases parallel processing Real-time systems Libraries

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distributed Deep Learning-based Model for Large image Data Classification 23

Distributed Deep Learning-based Model for Large Image Data C...

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Proceedings of the 7th International Conference on Future Networks and distributed Systems

作者： M Faisal Nurnoby Khalid A. Abu Shawarib Ayaz Ul Hassan Khan SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum and Minerals Saudi Arabia

ISBN: (纸本)9798400709036

Artificial intelligence has shown great potential in a variety of applications, from natural language models to audio visual recognition, classification, and manipulation. AI Researchers have to work with massive amount of collected data for use in machine learning, raising some challenges in effectively managing and utilizing the collected data in the training phase to develop and iterate on more accurate, and more generalized models. In this paper we conducted a review on parallel and distributed machine learning methods and challenges. We also propose a distributed and scalable deep learning model architecture which can span across multiple processing nodes. We tested the model on the MIT Indoor dataset, to evaluate the performance and scalability of the model using multiple hardware nodes, and showed the scaling characteristics of the different model using different model sizes. We find that distributed training is 80% faster using 2 GPUs than 1 GPU. We also find that the model keeps the benefits of distributed training such as speed and accuracy regardless of its size or training batch size.

关键词： Deep Learning

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Generic parallel data structures and algorithms to GPU superpixel image segmentation

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DISPLAYS 2022年 74卷

作者： Mansouri, Abdelkhalek Creput, Jean-Charles Qiao, Wen-Bao Univ Bourgogne Franche Comte UTBM CIAD UMR 7533 F-90010 Belfort France Beijing Informat Sci & Technol Univ Comp Sch Beijing 100101 Peoples R China

We propose parallel implementation on GPU (graphics processing unit) system for some generic algorithms applied to superpixel image segmentation problem. The aim is to provide standard algorithms based on generic decentralized data structures that could be easily improved and customized on many optimization problems on parallel platforms. Note that superpixel segmentation are clustering algorithms applied to image processing. Two types of algorithms are presented and implemented on GPU based on common parallel data structures. Firstly, we present a parallel implementation of the well-known k-means algorithm with application to 3D data. It is based on a cellular grid subdivision of space that allows closest point findings in constant optimal time for bounded distributions. Secondly, we present an application of the parallel Boruvka minimum spanning forest algorithm to compute watershed segmentation. Both techniques are fully executed on GPU and share the same data structures that embed disjoint-set-trees and distributed-link-lists. We evaluate our k-means approach with regards to state-of-the-art methods, that are, the well known SLIC algorithm, and the adaptive segmentation approach SPASM. We argue that our implementation has the shortest execution time among the tested methods, with a near real time performance and quasi linear acceleration factor, while it provides more regular shape superpixel segmentation based on hexagonal tessellation. Watershed minimum spanning forest method is presented and evaluated accordingly to the same experimental framework.

关键词： k-means Minimum spanning forest Watershed computation parallel computing GPU Superpixel segmentation

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Single Node Acceleration of Generative Adversarial Networks using HPC for image Analytics 5

Single Node Acceleration of Generative Adversarial Networks ...

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5th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2022

作者： Ravikumar, Aswathy Sriraman, Harini School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9781450397612

Generative Adversarial Networks (GAN) are approaches that are utilized for data augmentation, which facilitates the development of more accurate detection models for unusual or unbalanced datasets. Computer-assisted diagnostic methods may be made more reliable by using synthetic pictures generated by GAN. Generative adversarial networks are challenging to train because too unpredictable training dynamics may occur throughout the learning process, such as model collapse and vanishing gradients. For accurate and faster results the GAN network need to trained in parallel and distributed manner. We enhance the speed and precision of the Deep Convolutional Generative Adversarial Networks (DCGAN) architecture by using its parallelism and executing it on High-Performance Computing platforms. The effective analysis of a DCGAN in Graphic processing Unit and Tensor processing Unit platforms in which each layer execution pattern is analyzed. The bottleneck is identified for the GAN structure for each execution platforms. The Central processing Unit is capable of processing neural network models, but it requires a great deal of time to do it. Graphic processing Unit in contrast, side, are a hundred times quicker than CPUs for Neural Networks, however, they are prohibitively expensive compared to CPUs. Using the systolic array structure, TPU performs well on neural networks with high batch sizes but in GAN the shift between CPU and TPU is huge so it does not perform well. © 2022 ACM.

关键词： Graphics processing unit

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