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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing"

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Research of Latent Video Stream Compression methods for FPV Control of UAVs 27th

Research of Latent Video Stream Compression Methods for FP...

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27th International conference on distributed Computer and Communication Networks, DCCN 2024

作者： Chenskiy, A. Berezkin, Aleksandr Vivchar, R. Kirichek, Ruslan Kukunin, D. The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Bolshevikov Avenue 22 St. Petersburg Russia

ISBN: (纸本)9783031808524

The efficiency of video stream transmission links between an unmanned aerial vehicle and its operator in mobile and hybrid orbital-terrestrial communication networks directly depends on solving the problem of compressing video stream frames, while ensuring the quality of the restored image. One of the methods of frame compression is the use of variational autoencoders to transfer the latent space obtained during the processing of individual frames. The present paper is devoted to the research of how effectively different algorithms can compress quantized latent space of variational autoencoder VQ-f16 from Stable Diffusion repository. The system of quantized latent space compression algorithms efficiency indicators and their description are presented. A comparative analysis of the efficiency of quantized latent space compression algorithms is conducted. The results of analyzing the efficiency of quantized latent space compression algorithms are presented and recommendations for improving their efficiency are given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Unmanned aerial vehicles (UAV)

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Fast Katz Centrality on Dynamic Graphs

Fast Katz Centrality on Dynamic Graphs

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Prajjwal Nijhara Dishit Sharma Dip Sankar Banerjee Department of Computer Science and Engineering Indian Institute of Technology Jodhpur NH62 Karwar Jodhpur Rajasthan India

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

In network analysis, Katz centrality is widely used to measure node influence by considering both direct and indirect connections weighted by path length. While numerous studies have examined Katz centrality for static graphs, relatively few address the challenges posed by dynamic graphs. Katz centrality can utilize perturbation theory by exploiting iterative solvers to obtain updated Katz scores in dynamic graphs. However, these methods are limited to handling only small changes, as they generally accept only minor structural updates limited to only edge insertions or removals. Additionally, due to an iterative approach, the solutions can be sequential and provide approximate answers, which may reduce accuracy when the graph undergoes frequent updates over time. This paper introduces a novel algorithm that significantly improves the efficiency of Katz centrality calculations in dynamic graphs. After each update, we identify affected nodes using a Breadth First Search (BFS) frontier and then apply dynamic programming, which allows for both edge/node insertions and deletions. Our parallel implementation on a shared memory platform achieves a 5.29 x speedup on an average over the static version. Our algorithm can update a batch of 1 million edges on an existing graph of 1 billion edges in 24.73 seconds.

关键词： Weight measurement Runtime Heuristic algorithms Perturbation methods Directed graphs Network analyzers parallel processing Length measurement Dynamic programming Iterative methods

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Optimizing Forward Wavefield Storage Leveraging High -Speed Storage Media

Optimizing Forward Wavefield Storage Leveraging High -Speed ...

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1st International conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Speglich, Joao Kukreja, Navjot Bisbas, George Saraiva, Atila Huckelheim, Jan Luporini, Fabio Washbourne, John SENAI CIMATEC Supercomp Ctr Salvador BA Brazil Univ Liverpool Liverpool Merseyside England Imperial Coll London London England Argonne Natl Lab 9700 S Cass Ave Argonne IL 60439 USA Devito Codes London England Chevron Tech Ctr Houston TX USA

ISBN: (纸本)9798350364613;9798350364606

Full-Waveform Inversion (FWI) is a commonly used adjoint-based optimization technique in geosciences. Similar to other adjoint-based optimizations, MI involves gradient calculations that impose a substantial memory footprint. This memory footprint is often considered the primary bottleneck in this FWI process and has been the subject of extensive research. Previous studies have explored various approaches to mitigate this issue, including optimal checkpointing strategies, lossy compression, subsampling in time, and other methods aimed at reducing the memory requirements. These investigations have typically operated under the assumption that writing the data to disk would result in significant and unacceptable performance degradation, leading to compromises in precision or increased computational costs to conserve memory resourcesin this work, we challenge this assumption in light of recent advancements in high -bandwidth storage, particularly the utilization of Non-Volatile Memory Express solid-state drives (NVMe SSDs). We provide empirical evidence that, with specific combinations of modern hardware, it is indeed feasible to write temporary data to disk during the forward propagation phase and subsequently read it back during the adjoint computation without experiencing substantial performance degradation.

关键词： waveform inversion Adjoint-based optimization Gradient calculations Memory footprint Large-scale inverse problems parallel computing Non-volatile memory express solid-state drives (NVMe SSDs) high-speed storage

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Learning with convolution and pooling operations in kernel methods 36

Learning with convolution and pooling operations in kernel m...

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36th conference on Neural Information processing Systems (NeurIPS)

作者： Misiakiewicz, Theodor Mei, Song Stanford Univ Dept Stat Stanford CA 94305 USA Univ Calif Berkeley Dept Stat Berkeley CA USA

ISBN: (纸本)9781713871088

Recent empirical work has shown that hierarchical convolutional kernels inspired by convolutional neural networks (CNNs) significantly improve the performance of kernel methods in image classification tasks. A widely accepted explanation for their success is that these architectures encode hypothesis classes that are suitable for natural images. However, understanding the precise interplay between approximation and generalization in convolutional architectures remains a challenge. In this paper, we consider the stylized setting of covariates (image pixels) uniformly distributed on the hypercube, and characterize exactly the RKHS of kernels composed of single layers of convolution, pooling, and downsampling operations. We use this characterization to compute sharp asymptotics of the generalization error for any given function in high-dimension. In particular, we quantify the gain in sample complexity brought by enforcing locality with the convolution operation and approximate translation invariance with average pooling. Notably, these results provide a precise description of how convolution and pooling operations trade off approximation with generalization power in one layer convolutional kernels.

关键词： Convolution

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Plant Diseases Detection Using Machine Learning and image processing: Review

Plant Diseases Detection Using Machine Learning and Image Pr...

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International conference on Embedded & distributed Systems (EDiS)

作者： Fatima Zohra Touati Lakhdar Loukil Bakhta Amrane Department of Computer Science University of Oran 1 Ahmed Ben Bella Oran Algeria

ISBN: (数字)9798331519872

ISBN: (纸本)9798331519889

An agricultural productivity boost that is sustainable requires the early and accurate identification of plant diseases. Traditionally, human experts have been used to diagnose plant diseases. But it is expensive, time consuming, and in some cases impractical. Currently, machine learning (ML) and deep learning (DL) methods based on image processing techniques have been introduced to classify leaf lesions caused by diseases. Thus, research applying image processing and machine learning techniques for plant diseases has become a hot research topic. In this report, we propose a review of a set of studies carried out in the field of plant disease detection using machine learning and image processing techniques such as segmentation and feature extraction. The aim of this review is to provide useful knowledge and recommendations to researchers and practitioners to develop reliable and efficient methods for detecting plant diseases by processing images using deep learning techniques in particular. To this end, we have conducted an empirical evaluation of select educational curricula focused on the detection of plant diseases.

关键词： Deep learning Productivity Plant diseases image segmentation Accuracy Reviews Feature extraction Reliability Lesions

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AMQGaussian: Efficient 3D Gaussian Representation with Asymmetric Mixed-precision Quantization

AMQGaussian: Efficient 3D Gaussian Representation with Asymm...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Yong Kou Qing Ye Yanci Zhang Xia Yuan Jinlong He College of Computer Science Sichuan University Chengdu China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

3D Gaussian Splatting (3DGS) has recently gained increasing attention in novel-view scene synthesis. However, it requires millions of 3D Gaussian spheres to achieve high-quality rendered images, leading to substantial GPU resource demands for training and rendering. This paper has developed an efficient framework to address the challenges faced by 3DGS. 1) We propose an asymmetric mixed-precision quantization strategy to efficiently quantize and dequantize the parameters of 3D Gaussian Spheres (excluding position and opacity) and utilized the Straight Through Estimator method to address the issue of non-backpropagatable gradients post-quantization. This approach significantly reduces GPU memory usage and model storage space. 2) Using statistical methods, we identify optimal gradient thresholds to enhance the densification algorithm of 3D Gaussian spheres, thereby further improving performance. 3) To address the speed bottlenecks in parallel processing of low-precision data using CUDA, we introduce a fast atomic operation method, increasing training speed tenfold. Overall, we validate the effectiveness of our framework across various datasets. It reduces GPU usage by 50% during training, decreases GPU usage threefold during rendering, halves the model storage space and maintains image quality comparable to 3DGS.

关键词： Training image quality Solid modeling Three-dimensional displays Quantization (signal) Statistical analysis Graphics processing units parallel processing Rendering (computer graphics) image reconstruction

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Realization and optimization of Sobel edge detection algorithm for Domestic DCU accelerators 2

Realization and optimization of Sobel edge detection algorit...

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2nd International conference on Mechanical Engineering, Intelligent Manufacturing and Automation Technology, MEMAT 2022

作者： Zhang, Hao Han, Lin Xie, Jingming Ma, Ke School of Information Engineering Zhengzhou University China National Supercomputing Center in Zhengzhou Zhengzhou China

ISBN: (纸本)9783800757619

Sobel edge detection algorithm is one of the commonly used methods in image segmentation algorithm based on boundary, but the deployment of this kind of image processing algorithm is still blank in domestic DCU accelerators. To efficiently implement Sobel edge detection algorithm on DCU multi-core platform, a parallel Sobel edge detection algorithm for DCU platform is designed. Based on the domestic DCU accelerators, the Sobel algorithm was parallelized, its granularity was divided, and its parallel implementation was heterogeneous. The parallel execution efficiency of Sobel algorithm was improved by hiding the global memory delay and using texture memory to improve the cache hit ratio. The results show that the parallel algorithm on DCU platform achieves the acceleration ratio of 1.32~2.22 compared with the serial algorithm and achieves the parallelization of Sobel edge detection algorithm efficiently on DCU platform. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： image segmentation

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Mapping the density distribution pattern of microvasculature in whole-mouse brain 17

Mapping the density distribution pattern of microvasculature...

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17th International conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Li, Yuxin He, Weijie Cao, Jia Li, Anan Li, Xiangning Jiang, Tao School of Computer Science and Engineering Xi'an University of Technology Xi'an710048 China HUST-Suzhou Institute for Brainsmatics Suzhou215123 China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

Analyzing the vascular network structure of the brain is crucial for understanding brain function and preventing and treating cerebrovascular diseases. By studying indicators such as the length density and branching point density of the brain's microvascular network, we can better understand the brain's vascular network and provide a scientific basis for cerebrovascular disease research. Traditional methods rely on statistical analysis of isolated brain regions, which fail to capture the spatial distribution characteristics of micro-vessels across the entire brain and overlook spatial information from different brain regions. To address this, we propose a computational pipeline for the spatial distribution of vascular density in the brain. First, we use a deep learning model to segment brain vascular images from HD-fMOST imaging of mouse brains. Then, we perform skeletonization on the 3D vascular segmentation images and obtain vessel diameter information through distance transformation. Finally, we filter capillaries based on their diameter and calculate the feature parameters of these capillaries to acquire vascular parameter distributions across the entire brain. The entire process is enhanced by MPI parallel processing and multithreading techniques to improve image processing efficiency. Results indicate that our proposed method can capture the spatial distribution characteristics of microvascular density across the whole brain by integrating multiple datasets. This method provides a powerful tool for comprehensive research on brain vasculature. © 2025 SPIE.

关键词： image segmentation

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MTP: A Minimal Task Perturbation Framework for Mitigating Poisoning Attacks in Federated Learning

MTP: A Minimal Task Perturbation Framework for Mitigating Po...

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image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Weiqiang Jiang Tianyu Su Guangtao Wang Dongqi Han China Mobile Group Co. Ltd Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (MoE) Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Federated Learning achieves privacy protection by allowing different participants to jointly train models. However, this characteristic also makes Federated Learning susceptible to data poisoning attacks. Many existing defense methods can successfully defend against various data poisoning attacks, but they are often only effective against one of the independent and identically distributed (IID) or non-independent and identically distributed (non-IID) scenarios. In this paper, we propose a novel minimal task perturbation framework named MTP for mitigating data poisoning attacks in both IID and non-IID scenarios. To efficiently identify potential attackers, MTP utilizes a weight similarity ensemble mechanism based on four distance algorithms and clusters clients according to the fused weight similarity. Moreover, MTP uses a clipped weight aggregation to minimize the impact on the performance of main task. Experimental results show that the proposed MTP outperforms state-of-the-art methods in stability and security, and maintain higher accuracy of the main task.

关键词： Privacy Computer vision Federated learning Perturbation methods image processing distributed databases Stability analysis Security Protection Detection algorithms

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Smoothing Point Adjustment-Based Evaluation of Time Series Anomaly Detection 48

Smoothing Point Adjustment-Based Evaluation of Time Series A...

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48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Liu, Mingyu Wang, Yijie Xu, Hongzuo Zhou, Xiaohui Li, Bin Wang, Yongjun National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Changsha China

ISBN: (纸本)9781728163277

Anomalies in time series appear consecutively, forming anomaly segments. Applying the classical point-based evaluation metrics to evaluate the detection performance of segments leads to considerable underestimation, so most related studies resort to point adjustment. This operation treats all points as true positives within a segment equally when only one individual point alarms, resulting in significant overestimation and creating an illusion of superior performance. This paper proposes smoothing point adjustment, a novel range-based evaluation protocol for time series anomaly detection. Our protocol reflects detection performance impartially by carefully considering the specific location and frequency of alarms in the raw results. It is achieved by smoothly determining the adjustment range and rewarding early detection via a ranging function and a rewarding function. Compared with other evaluation metrics, experiments on different datasets show that our protocol can yield a performance ranking of various methods more consistent with the desired situation. © 2023 IEEE.

关键词： Anomaly Detection Evaluation Protocol Point Adjustment Time Series

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