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5th International Conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

5th International Conference on 3D Imaging Technologies—Mul...

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5th International Conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

ISBN: (纸本)9789819751808

The proceedings contain 59 papers. The special focus in this conference is on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning. The topics include: Digital Twin Technology Approach Based on the Hierarchical IDP Tensor Decomposition;modeling Technology for Complex Dynamic Operating Environment of Power Grid Based on Digital Twins;design of a Digital Twin Platform Based on Distributed Computing and Resource Optimization Algorithms;deep Learning Network Optimization Combining 3D Imaging and Multidimensional Signal processing;time Series Prediction Application of Deep Learning in Multidimensional Signal processing;improving Abstractive Summarization with Graph Sequence Model;advancing Semantic Segmentation and Interpretation of 3D images Through Integrated Deep Learning and Natural Language processing Techniques;children’s Toy Product Design Based on Augmented Reality Technology;based on the Neural Network Classification of Human Behavior Research;development of an Independent Adversarial Sample Detection Model, Based on image Features;Visualization and Analysis of CNN Adversarial Training;video Violence Detection Method Based on Multi-Feature and Graph Convolutional Network;Enhancing Realized Volatility Prediction: An Exploration into LightGBM Baseline Models;terminal Anomaly Discovery Technology Based on Service Behavior Deviation;An Efficient CRNN Model with the Multi-scale Feature Fusion for Text recognition from Chinese Medical Reports;advancing Precision Drug Screening: Integrating Imaging Technology and Artificial Intelligence for Novel Models;a Short Video Retrieval Method Based on Spatio-Temporal Feature Fusion;spatial Data Transformation and Fusion Expression Based on Geometric Algebra;EM2-YOLO: Lightweight remote sensing image Detection;Tower-Type Detection of UAV Aerial image Based on YOLOV5 Network Model.

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Multi-Plant Leaf Disease Detection and Classification using Convolution Neural Network Aad image processing

Multi-Plant Leaf Disease Detection and Classification using ...

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Recent Advances in Science and Engineering Technology (ICRASET), International Conference on

作者： S S Aishwarya Pushpa Ravikumar I V Chaithra Deeksha Sagar Computer Science and Engineering AIT Adichunchanagiri Institute of Technology Chikmagalur Karnataka INDIA Department of Computer Science and Engineering AIT Adichunchanagiri Institute of Technology Chikmagalur Karnataka INDIA

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Every nation’s basic need is the agricultural products. Disease-ridden leaves have an effect on the nation’s agricultural output and financial resources. This research provides a deep learning based plant leaf disease classification and detection method. The website (Plant Village dataset) provided the photos that were used. Since tomatoes, peppers, and potatoes are the most prevalent plant species worldwide and in Iraq specifically, we have used them as examples of certain plant species in our study. There are $\mathbf{2 0 6 3 6}$ photos of plants and their illnesses in this data set. Plant leaf diseases are sorted into 15 class labels in the proposed system using a convolutional neural network (CNN). Of these classes, $\mathbf{1 2}$ are dedicated to diseases. Several various plants that were found, including three groups for healthy leaves and various microorganisms including fungus. Consequently, we achieved exceptional precision in both training and testing; specifically, we achieved $\mathbf{9 8. 2 9 \%}$ accuracy in training and $\mathbf{9 8. 0 2 9 \%}$ accuracy in testing across all utilized data sets. pattern recognition is necessary in order to diagnose these disorders. Using remote sensing techniques to investigate multi- and hyper-spectral picture captures is a frequent approach in this situation. Techniques that use this strategy frequently use digital image processing capabilities to accomplish their objectives. There are numerous kinds of pattern recognition algorithms that provide accurate disease detection. Plant diseases are currently detected using principal component analysis and back propagation. These algorithms are acquired by neural network training supervision.

关键词： Training Accuracy Neural networks Classification algorithms pattern recognition Convolutional neural networks remote sensing Diseases Testing Principal component analysis

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Rust-Style Patch: A Physical and Naturalistic Camouflage Attacks on Object Detector for remote sensing images

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remote sensing 2023年第4期15卷 885-885页

作者： Deng, Binyue Zhang, Denghui Dong, Fashan Zhang, Junjian Shafiq, Muhammad Gu, Zhaoquan Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou 510006 Peoples R China Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Peng Cheng Lab Dept New Networks Shenzhen 518055 Peoples R China

Deep neural networks (DNNs) can improve the image analysis and interpretation of remote sensing technology by extracting valuable information from images, and has extensive applications such as military affairs, agriculture, environment, transportation, and urban division. The DNNs for object detection can identify and analyze objects in remote sensing images through fruitful features of images, which improves the efficiency of image processing and enables the recognition of large-scale remote sensing images. However, many studies have shown that deep neural networks are vulnerable to adversarial attack. After adding small perturbations, the generated adversarial examples will cause deep neural network to output undesired results, which will threaten the normal recognition and detection of remote sensing systems. According to the application scenarios, attacks can be divided into the digital domain and the physical domain, the digital domain attack is directly modified on the original image, which is mainly used to simulate the attack effect, while the physical domain attack adds perturbation to the actual objects and captures them with device, which is closer to the real situation. Attacks in the physical domain are more threatening, however, existing attack methods generally generate the patch with bright style and a large attack range, which is easy to be observed by human vision. Our goal is to generate a natural patch with a small perturbation area, which can help some remote sensing images used in the military to avoid detection by object detectors and im-perceptible to human eyes. To address the above issues, we generate a rust-style adversarial patch generation framework based on style transfer. The framework takes a heat map-based interpretability method to obtain key areas of target recognition and generate irregular-shaped natural-looking patches to reduce the disturbance area and alleviates suspicion from humans. To make the generated adversarial example

关键词： adversarial attack object detection style transfer deep learning

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Batch processing for Enhanced InISAR Imaging of Space Targets

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2024年 62卷

作者： Qian, Wenshuo Wang, Junling Zhao, Lizhi Li, Haiguang Tang, Fujie Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China 63610 Uint Korla 841001 Peoples R China

High-precision 3-D reconstruction of satellites is critical for automatic target recognition (ATR). This article introduces a batch processing-based method to enhance the performance of interferometric inverse synthetic aperture radar (InISAR) imaging. By utilizing the phase information within the inverse synthetic aperture radar (ISAR) image sequence, the main error components of height estimation are accurately estimated via the ordinary least squares (OLSs) method. In addition, an arc interval determination strategy for optimizing the batch processing performance is proposed. To ensure accurate phase unwrapping in challenging imaging scenarios, a phase unwrapping method based on the minimum standard deviation criterion is also proposed. Simulation experiments validate the effectiveness and superiority of the proposed algorithms. Compared with the traditional two-ISAR-image InISAR imaging method, the batch processing approach improves the precision of height estimation by approximately 50% with an arc length of approximately 15 degrees.

关键词： Radar imaging Imaging Radar Spaceborne radar Three-dimensional displays Estimation Satellites Batch production systems Orbits Space vehicles Batch processing interferometric inverse synthetic aperture radar (InISAR) inverse synthetic aperture radar (ISAR) image sequence phase unwrapping space target

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Application of Computer Artificial Intelligence Technology in image processing Botany Control System of Mikania Micrantha

Application of Computer Artificial Intelligence Technology i...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Shuqiang, Li Wei, Chen University of Electronic Science and Technology of China Department of Mechanical and Electrical Engineering Zhongshan Institute Zhongshan Zhongshan China Industry Research Center Wonders Information Co. Ltd. Beijing China

ISBN: (纸本)9798350309614

The Nano-Hyperspec spectrometer was used to preprocess Mikania micrantha images, such as geometric correction, image denoising, radiation correction and bad band elimination. The optimal exponential coefficient method (OIF), adaptive band method (ABS), automatic subspace segmentation (ASP) combined with adaptive band selection method (ASP+ ABS) were used to screen the bands. In this way, the feature bands with relatively large information and low correlation between bands are obtained, and then three remote sensing images are formed. SVM algorithm was used to classify three different types of Mikania micrantha images, evaluate the recognition ability of three kinds of hyperspectral characteristics on Mikania Micrantha images, and screen out the hyperspectral characteristics that can better reflect Mikania Micrantha images. Micrantha Hyperspace was mapped in the study area, and the mapping accuracy was 79.62%. The user accuracy is 67.85%. The ABS spectral selection method was used to locate Micrantha Hyperspace, and the accuracy of mapping was 75.63%, and the accuracy of user was 67.96%. (ASP+ ABS) algorithm was 86.95%. The user accuracy is 92.65%. © 2023 IEEE.

关键词： artificial intelligence image processing mikania micrantha prevention and cure UAV

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Personalized healthcare museum exhibition system design based on VR and deep learning driven multimedia and multimodal sensing

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PERSONAL AND UBIQUITOUS COMPUTING 2022年第3期27卷 973-988页

作者： Zhao, Ling Hubei Inst Fine Arts Dept Ind Design Wuhan Wuhan 430000 Hubei Peoples R China

With the vigorous development of the Internet, multimedia technology, and virtual reality technology, digital museum has been born and gradually entered the stage of rapid development. As a typical application of 3D virtual scene in the field of traditional culture protection and communication, 3D virtual museum has become an important trend in the development of museums in recent years. Three-dimensional virtual museum can maximize the development of museum functions and better play the museum's cultural communication and heritage protection and other functions. Faced with such rich cultural relic resources, how to construct 3D virtual museum quickly becomes an important research problem. Virtual reality technology integrates the latest research and development achievements of digital image graphics processing technology, computer technology, artificial intelligence, multimedia technology, sensor technology, and other information technologies, which provides powerful support and help for us to create and experience virtual world. The combination of virtual reality technology and cultural heritage protection has created a new research field. Based on this, this paper studies personalized healthcare museum exhibition system design based on the VR and deep learning-driven multimedia and multimodal sensing. The novel computational model is designed to improve the systematic performance of the VR framework, and the application scenarios are tested. Through the verification, the performance is tested.

关键词： Virtual reality Human-computer interaction pattern recognition Neural networks Multimedia technology Digital museum Personalized healthcare Multimodal sensing

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remote sensing Object Detection Based on Improved Detection Transformer

Remote Sensing Object Detection Based on Improved Detection ...

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Yuanzhong Hu Po Hu Haihui Wang Yuzhuo Wang School of Computer Science and Engineering and School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Nowadays, the accuracy of remote sensing image target recognition has greatly improved. It enables humans to efficiently monitor crop growth, track climate changes and natural disasters, and even provide critical strategic intelligence for national defense. However, due to the challenges posed by the large number of objects and the immense size of remote sensing images, there is still considerable room for improvement in recognition accuracy and efficiency. Detection Transformer (DETR) framework utilizes the Transformer architecture, change the object detection task to a set prediction problem, thus simplifying the traditional post-processing workflow. Through its attention mechanisms, DETR effectively captures object features in intricate scenes by modeling global contextual information. Nonetheless, there is still potential for improvement in extracting fine-grained details. In this study, three modifications are proposed to the DETR framework to boost remote sensing object detection accuracy. First, a multi-scale feature extraction approach is employed to capture deeper and more detailed image features. Second, a bidirectional learning path is introduced to enhance the contextual relationships between image features. Experimental results demonstrate that our proposed method has achieved strong competitiveness compared to other approaches

关键词： Accuracy Target tracking Target recognition Object detection Transformers Feature extraction remote sensing Monitoring Software engineering Meteorology

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Mid-long-wave infrared broadband absorber based on multi-layer plasma cavities metasurface 12

Mid-long-wave infrared broadband absorber based on multi-lay...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Duan, Shenghua Yang, Guang Zhang, Xinyu National Key Laboratory of Science & Technology on Multispectral Information Processing Huazhong University of Science & Technology Wuhan430074 China School of Artificial Intelligence & Automation Huazhong University of Science & Technology Wuhan430074 China

ISBN: (数字)9781510674929

ISBN: (纸本)9781510674912

Efficient absorption in the mid-long-wave infrared region is crucial for various applications, including thermal imaging and sensing, infrared spectroscopy, and camouflage. A multilayered metal-insulator-metal-based infrared plasmonic metamaterial absorber utilizing a stacked nanocavity meta-surface consisting of a double-layer cylindrical metal-insulator-metal structure (DCMIM) is proposed. This absorber demonstrates wideband absorption capabilities with an average absorption rate exceeding 75% in the 3-14μm range. To understand the spectral absorption mechanisms, a comprehensive analysis is conducted using the Finite Difference Time Domain and Fabry-Perot resonance model. The analysis reveals physical mechanisms such as strong coupling between plasmon resonances and phonon vibrations loss from the dielectric spacer, as well as localized surface plasmon resonance. It is worth noting that the quantities of plasma cavities significantly impacts the performance of the absorber. By adjusting the material type and thickness of the film layer, it becomes possible to effectively compensate for the performance limitations of a single dielectric layer material within specific wavelength bands. Experimental results, combined with theoretical analysis of the DCMIM structure, establish a new approach to the design of broadband infrared absorbers. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Metal insulator boundaries

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Classification of MRI's images using the Fractional Fourier Transform and the Local Binary pattern information by Artificial Neural Network 29

Classification of MRI's images using the Fractional Fourier ...

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Conference on Infrared remote sensing and Instrumentation XXIX

作者： Estrada Marmolejo, Carlos A. Alvarez-Borrego, Josue Garcia-Torales, Guillermo Flores Nunez, Jorge Luis Univ Guadalajara Av Revoluc 1500 Guadalajara 44430 Jalisco Mexico CICESE Carretera Ensenada Tijuana 3918 Zona Playitas Ensenada 22860 Baja California Mexico

ISBN: (数字)9781510644991

ISBN: (纸本)9781510644991;9781510644984

pattern recognition techniques are widely used in computer vision, classification of radio signals, and voice recognition. The fractional Fourier transform is used to recognize patterns using binary rings masks and segment images. This technique has the characteristic of being invariant to position and rotation and finally obtaining a one-dimensional signature. On the other hand, Neural Networks are used for pattern recognition based on a deep neural network algorithm. It has the characteristic of training large datasets with millions of images. Artificial Neural Networks(ANNs) are used for several applications such as pattern recognition and classification of input data. In particular, the ANN has been used to evaluate medical images from the brain to assess if the image corresponds to Alzheimer's disease. One disadvantage of the neural network is a large amount of time to learn depending on the number of patterns to be identified or classified and the ability to adapt and recognize patterns. Besides, the fractional Fourier transform cannot analyze a large amount of information. In this work, a comparison between the Artificial Neural Network and the Fractional Fourier Transform is presented to determine which will be the best for recognizing a batch of selected medical images. We propose a reconstruction method using both techniques for precise image recognition and the evaluation of their respective metrics such as accuracy, precision, sensitivity, and specificity. The medical images regarding Alzheimer's disease are no dementia, very mild dementia, mild dementia presenting the best perfomance regarding the receiver operating characteristics and moderate dementia was the worst classified related to the number of images of the dataset.

关键词： pattern recognition one-dimensional signature fractional Fourier transform binary rings masks Artificial Neural Network computer vision image processing local binary pattern neural network

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Content-based image Retrieval in Cyber-Physical System Modeling of Robots 11th

Content-based Image Retrieval in Cyber-Physical System Model...

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Anantha Prabha, P. Subashree, B. Deva Priya, M. Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9789811988240

Cyber-Physical System (CPS) is used in industries and automated plants as they have modern administration abilities and are capable of performing real-time processing in a distributed architecture. In mechanical plants, they are expected to perform image recognition and retrieval, and other Machine Learning (ML) processes for efficient functioning and screening of assembling units and products. The existing strategies like remote sensing retrieval and the Gaussian mixture model for image Retrieval (IR) are not suitable for huge datasets. The drawbacks of the existing strategies can be eliminated based on features of data by using Improvised Density Scale-Invariant Feature Transform (ID-SIFT). This approach mainly focuses on geometrical features like color, texture, size and shape-based image matching offering an improved accuracy of 89%. This approach is relatively efficient when compared to existing algorithms used in IR. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cyber Physical System

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