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IEEE International Conference on Signal and image processing (ICSIP)

作者： Fulong Wang Shiyu Peng Yuanhao Gong Minghan Li Shuanglong Liu Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control Key Laboratory of Physics and Devices in Post-Moore Era College of Hunan Province Hunan Normal University Changsha China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

The particle Markov-chain Monte Carlo (PMCMC) method is a stochastic algorithm that combines Particle Filters (PFs) and Markov-chain Monte Carlo (MCMC) techniques. This approach is widely used in Bayesian inference for high-dimensional state spaces and nonlinear, non-Gaussian dynamic systems. However, current PMCMC accelerators face significant challenges due to their intensive computational complexity and the intricate particle routing, limiting their application in real-time scenarios. To address these challenges, we propose a novel distributed PMCMC method that leverages parallel computing to enhance hardware execution speed. Additionally, our method introduces a particle exchange scheme that not only resolves the accuracy issues caused by particle routing in distributed PMCMC but also achieves faster computing speed. Our design is implemented on a Xilinx Kintex-7 xc7k480t FPGA device. Experimental results demonstrate that our accelerator is nearly 65 ×faster than CPU performance, and provides speedups up to 5× compared to existing FPGA-based accelerators.

关键词： Performance evaluation Monte Carlo methods Accuracy Particle accelerators Computer architecture Routing Hardware

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RiverMapper: Step-wisely Mapping the Surface Rivers on Optical Remote Sensing images

RiverMapper: Step-wisely Mapping the Surface Rivers on Optic...

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2022 International Conference on Neural Networks, Information, and Communication Engineering, NNICE 2022

作者： Zhang, Peng Pan, Hengyue Yang, Ke Dou, Yong Niu, Xin National Laboratory for Parallel and Distributed Processing National University of Defense Technology 410073 China Artificial Intelligence Research Center National Innovation Institute of Defense Technology BeiJing100850 China

ISBN: (纸本)9781510655171

Accurately mapping the surface rivers is important in ecological environment monitoring and disaster prevention. The development of remote sensing technology and computer vision greatly improves the efficiency of this task. However, there are few methods that map the rivers from an image directly. The existing automatic river mapping methods usually had two successive stages: waterbody extraction and flow-path extraction, where the latter methods were very dependent on the waterbody masks generated by the former methods. Errors in waterbody masks caused breaks and redundancies in the extracted graphs. This paper proposed RiverMapper, which mapped the rivers step-wisely without dividing into two stages. Following the directions and actions predicted by the convolution neural network, RiverMapper walked along the rivers step by step and cropped the fixed-size image patches at each step for segmentation. Final river graphs were constructed by the waterbody mask patches and those tracks generated by RiverMapper. We applied RiverMapper on optical remote sensing images containing the Changjiang River and the Huanghe River. Without the degradation of the performance on waterbody extraction, RiverMapper outperformed other methods in terms of the local topological and geometrical similarity between the predicted and the ground-truth river graphs. © 2022 SPIE.

关键词： Mapping

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Area-NeRF: Area-based Neural Radiance Fields

Area-NeRF: Area-based Neural Radiance Fields

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

作者： Zonxin Ye Wenyu Li Peng Qiao Yong Dou National Key Laboratory of Parallel and Distributed Computing School of Computer National University of Defense Technology Changsha China

Neural Radiance Field (NeRF) has received widespread attention for its photo-realistic novel view synthesis quality. Current methods mainly represent the scene based on point sampling of ray casting, ignoring the influence of the observed area changing with distance. In addition, The current sampling strategies are all focused on the distribution of sampling points on the ray, without paying attention to the sampling of the ray. We found that the current ray sampling strategy for scenes with the camera moving forward severely reduces the convergence speed. In this work, we extend the point representation to area representation by using relative positional encoding, and propose a ray sampling strategy that is suitable for camera trajectory moving forward. We validated the effectiveness of our method on multiple public datasets.

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parallel Optimisation and Implementation of a Real-Time Back Projection (BP) Algorithm for SAR Based on FPGA

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SENSORS 2022年第6期22卷 2292页

作者： Cao, Yue Guo, Shuchen Jiang, Shuai Zhou, Xuan Wang, Xiaobei Luo, Yunhua Yu, Zhongjun Zhang, Zhimin Deng, Yunkai Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 101408 Peoples R China

This study conducts an in-depth evaluation of imaging algorithms and software and hardware architectures to meet the capability requirements of real-time image acquisition systems, such as spaceborne and airborne synthetic aperture radar (SAR) systems. By analysing the principles and models of SAR imaging, this research creatively puts forward the fully parallel processing architecture for the back projection (BP) algorithm based on Field-Programmable Gate Array (FPGA). The processing time consumption has significant advantages compared with existing methods. This article describes the BP imaging algorithm, which stands out with its high processing accuracy and two-dimensional decoupling of distance and azimuth, and analyses the algorithmic flow, operation, and storage requirements. The algorithm is divided into five core operations: range pulse compression, upsampling, oblique distance calculation, data reading, and phase accumulation. The architecture and optimisation of the algorithm are presented, and the optimisation methods are described in detail from the perspective of algorithm flow, fixed-point operation, parallel processing, and distributed storage. Next, the maximum resource utilisation rate of the hardware platform in this study is found to be more than 80%, the system power consumption is 21.073 W, and the processing time efficiency is better than designs with other FPGA, DSP, GPU, and CPU. Finally, the correctness of the processing results is verified using actual data. The experimental results showed that 1.1 s were required to generate an image with a size of 900 x 900 pixels at a 200 MHz clock rate. This technology can solve the multi-mode, multi-resolution, and multi-geometry signal processing problems in an integrated manner, thus laying a foundation for the development of a new, high-performance, SAR system for real-time imaging processing.

关键词： synthetic aperture radar (SAR) back-projection algorithm (BP) real-time image processing field-programmable gate array (FPGA)

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Information Science, parallel and distributed Systems (ISPDS), International Conference on

作者： Ya Zhou Ning Zhao Guimin Huang Nanxiao Deng Qingkai Guo Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

In sentence similarity research methods, sentence similarity is often calculated from semantic aspects, however, the influence of other features is ignored. For example, the influence of sentence syntactic structure and word order on sentence similarity is not considered. Therefore, This paper proposes a method to measure the degree of similarity between sentences based on a multi-feature fusion of semantics, syntactic similarity and word order similarity, respectively. For the aspect of semantic similarity, in this paper, we first preprocess the sentence and obtain the word vectors, and then obtains sentence vector features through neural networks, so as to calculate the sentence semantic similarity; Finally, for the aspect of word order similarity, word order is considered as the auxiliary feature information to calculate sentence similarity since word order has a smaller effect on sentence similarity compared to the above information. The experimental results show that the proposed model has an accuracy of 77.4 % and an F1 value of 84.3% on the MRPC dataset.

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Satellite image Classification using Convolutional Neural Network with Huber Loss Function for Land Cover Mapping in Forest Ecosystem

Satellite Image Classification using Convolutional Neural Ne...

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distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Ramy Riad Al-Fatlawy P. Raviprakash Gummagatta Yajaman Vybhavi Sripelli Jagadish S Meenakshi Sundaram Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Malla Reddy Engineering College for Women (UGC-Autonomous) Hyderabad India Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India School of Computer Science & Artificial Intelligence SR University Warangal India Department of CSE Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Satellite image classification for land cover involves determining high-resolution imagery to recognize and classify various types of land covers. This process assists in monitoring forest health, managing resources, and assessing biodiversity effectively. However, accurately classifying images for land cover is difficult because of diverse and complex nature of forest environments and varying image qualities. In this research, the Convolutional Neural Network with Huber Loss Function (CNN-HLF) is proposed to classify the satellite images accurately for landcover mapping in forest ecosystem. The CNN-HLF effectively manage the variability and complexity of forest environments which improves the performance of land cover. Initially, the Eurosat dataset is used to determine the CNN-HLF performance. Resizing and image sharpening is applied to adjust the size and enhance the clarity of features. DenseNet201 is employed for extracting features effectively from pre-processed images. The proposed CNN-HLF achieves a high accuracy of 99.87% using Eurosat dataset compared to existing methods like GoogleNet with pre-processing and Lightweight parallel CNN (LPCNN).

关键词： Training Accuracy Ecosystems Land surface Forestry Feature extraction Stability analysis Robustness Satellite images Convolutional neural networks

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Intelligent image processing based on VPX architecture 8

Intelligent image processing based on VPX architecture

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8th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Tao, Xin Ou, Weilin He, Wenbo Huazhong Institute of Electro-Optics Wuhan National Laboratory for Optoelectronics Wuhan430074 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Multiple bands cameras with high pixel resolution and fast frame rate bring challenges for real-time signal process which is important in monitoring and alerting application. Especially, the deep neural network algorithm which has been applied in practical EO system, requires high parallel computation ability. Thus, the traditional signal processing method in imaging sensor cannot satisfy the novel demands for balancing the capability and consumption. The system based on VPX architecture and all optical signal transmission can provide a promising improvement in computation capability, transmission bandwidth and data transfer capability. In this paper, a novel signal processing system for intelligent computing based on VPX architecture is indicated. Based on the system structure and components indication, the main configuration design is also explained. Especially the thermal deisgn of the full system is explained in details, because the thermal effect and related methods are important in high speed conputation system. The main intelligent signal processing circuit is based on FPGA SOC processor. After introducing the hardware design and scheme, an optimized SSD algorithm is introduced for multi-scales target detection, and the method to applying these algorithms on FPGA chip for real-time compuation is explained. Finally, this method is verified in both visible and IR images, especially for the small target in IR images. The whole design is verified that it can provide a promising solution for high performance and distributed EO imaging system. © 2022 SPIE

关键词： Data transfer

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Detecting and analysing geomorphological structures in images of comet 67P/Churyumov-Gerasimenko using Fourier transform

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MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2021年第3期503卷 3449-3459页

作者： Ruzicka, Birko-Katarina Schroeter, Matthias Pack, Andreas Boehnhardt, Hermann Max Planck Inst Sonnensyst Forsch Justus von Liebig Weg 3 D-37077 Gottingen Germany Georg August Univ Gottingen Geowissensch Zentrum Abt Isotopengeol Goldschmidtstr 1 D-37073 Gottingen Germany Max Planck Inst Dynam & Self Org D-37077 Gottingen Germany

We present a method for automatized detection and analysis of quasi-periodic lineament structures from images at pixel precision. The method exploits properties of the images' frequency domain found by using the Fourier transform. We developed this method with the goal of detecting lineament structures in an image of the Hathor cliff of comet 67P/Churyumov-Gerasimenko, which are caused by layerings and furrows in the nucleus material. Using our method, we determined the orientation and wavelength range of these structures. The detected layering edges have similar orientations and spatial separations of 9-20 m, and are ubiquitous throughout the image. We suggest that the layerings are a global feature of the comet nucleus that provide information about formation and evolution of comet 67P. The furrows are non-uniformly distributed throughout the image. Their orientation is broadly parallel to the direction of the local gravity vector at the Hathor cliff, with spacings similar to those of the layering structures. The furrows are interpreted as signatures of local down-slope movement of cliff material. We demonstrate that the developed method is broadly applicable to the detection and analysis of various kinds of quasi-periodic structures like geological layering, folding and faulting, and texture analysis in general. In order to facilitate the application of our method, this paper is accompanied by a demo program written in MATLAB.

关键词： methods: data analysis techniques: image processing comets: general comets: individual: 67P/Churyumov Gerasimenko

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image Inpainting using Automatic Structure Propagation with Auxiliary Line Construction

Image Inpainting using Automatic Structure Propagation with ...

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Summer Virtual Conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD-Summer), ACIS International

作者： Yuto Urano Irawati Nurmala Sari Weiwei Du Department of Information Science Kyoto Institute of Technology Kyoto Japan Information and Human Science Kyoto Institute of Technology Kyoto Japan

ISBN: (纸本)9798350396386

Existing image inpainting methods used traditional and deep learning methods to restore a large missing region in the damaged image. This often leads to color discrepancy and blurriness. Pre-processing of prior line detection by user assistance is usually employed to reduce the blurry of center region by segmenting the large region into more minor. However, it operates manually, which is time-consuming. This paper introduces a technique to generate two-line types: penetrator and interactor in constructing auxiliary lines as guidance. These lines assist structure propagation established automatically, while the remaining small regions are filled by texture propagation. Experiments on large regular masks demonstrate that our proposed approach generates higher-quality results than other methods.

关键词： Deep learning image segmentation image color analysis image restoration Artificial intelligence Software engineering

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Instant water body variation detection via analysis on remote sensing imagery

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JOURNAL OF REAL-TIME image processing 2021年第5期18卷 1577-1590页

作者： Wu, Yirui Han, Pengfei Zheng, Zhan Hohai Univ Coll Comp & Informat Nanjing Peoples R China

Water resource is one of the most valuable natural resources for human beings, which requires to be monitored for careful protection. Inspired by a significant power of machine learning methods, researchers have successfully developed many applications to automatically perform identification on the water body via analyzing remote sensing images. Since a similar category of ground objects could show a large difference in spectral representation, researchers try to propose distinctive and effective features to offer redundant information for category classification. Moreover, large amount of high-resolution remote sensing images require analyzing algorithms to be parallel processed for instant feedback. Based on these requirements, we propose a novel water body variation detection via analysis on remote sensing images. Specifically, the proposed method firstly perform pixel-level classification to locate abnormal changes with thoughts of visual word patterns. Afterwards, the proposed method proposes block division method to construct parallel running version with Mapreduce structure. With high representational and parallel running abilities, the proposed method is capable to accurately detect variation areas on remote sensing images with instant feedback. Experiments on several self-collected datasets show the proposed method has achieved better efficiencies than comparative studies.

关键词： Real-time RS applications of detection and estimation theory Remote sensing images Water body variation detection method distributed processing

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