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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16444 条记录，以下是1611-1620 订阅

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An Improved Planar Approximation Localization Method in Distributed Airborne Radars

An Improved Planar Approximation Localization Method in Dist...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jing Li Jinshan Ding Liwu Wen Zehua Yu Demin Huang National Key Laboratory of Radar Signal Processing Xidian University Xi’an China School of Electronic Engineering Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In distributed airborne radar systems, the linear target localization method using time of arrival (TOA) is extensively studied due to its excellent robustness and simplicity in computation. Quadratic or higher-order terms related to range are disregarded in conventional linear methods, which results in compromised localization accuracy under high noise conditions. Considering the position uncertainty of moving platforms and a more realistic prior variance in target position estimation, an improved planar approximation algorithm is proposed, which refines target location iteratively. The spherical equations are approximated in each iteration as planar equations involving the current estimated target position. The error introduced by the planar approximation is recalculated and compensated for, enabling high-accuracy target location. In simulations, the proposed algorithm can reduce the average number of iterations by approximately 20%, and achieves a localization accuracy improvement of over 5 dB when the standard deviation of distance measurement error exceeds 1 km.

关键词： Location awareness Accuracy Uncertainty Transmission line matrix methods Target tracking Airborne radar Signal processing algorithms Approximation algorithms Mathematical models Covariance matrices

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Face Detection and Face Recognition using Haar Cascade Algorithm

Face Detection and Face Recognition using Haar Cascade Algor...

引用

Recent Trends in Computer Science and Technology (ICRTCST), International conference on

作者： B. Harshini Department of Electrical and Electronics Engineering VNR Vignana Jyothi Institute of Engineering & Techology Hyderabad India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

Human Face Detection (FD)is a demanding topic in areas of image processing and pattern identification. Facial expression conveys non-verbal cues, which plays crucial role in social relations. Facial Expression is a significant yet challenging task, as we can use it to recognize emotions and mental state of individual. In this paper, using image processing and Machine Learning (ML), we differentiate the captured image with the trained dataset and then display the state of image. These handles detection and recognizing multiple faces in real time. Face Recognition (FR) can be done using two methods like FD and FR. This paper describes use of ML algorithms in real time which is used for FD and FR that is Haar Cascade algorithm.

关键词： Emotion recognition Machine learning algorithms Face recognition image processing Machine learning Market research Real-time systems

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Research on Enhanced Perception and Attention-Driven Person Re-Identification 20

Research on Enhanced Perception and Attention-Driven Person ...

引用

20th International conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Wu, Cancan Li, Dong Zhang, Zhi College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

When matching similarity among pedestrians in images, pedestrian re-identification algorithms are often disturbed by occlusions. A typical tactic is to improve the robustness of occlusion features in the model. However, this approach neglects the noise introduced by other pedestrians in the image, leading to limited learning of relevant information between images. To address these issues, this paper introduces a pedestrian re-identification model that leverages perception-enhanced and attention mechanisms. By enhancing the perception of non-pedestrian noise and erasing non-pedestrian features, we then employ a cross-attention mechanism to subtly embed global and local features for finer feature recognition. Finally, through context attention, we learn the interaction between pedestrian images to improve re-identification accuracy. Results from studies performed on the Market-1501, CUHK03-D, and Duke-ReID datasets indicate that the suggested model surpasses leading contemporary models regarding Rank-1 scores and mean Average Precision (mAP), while maintaining good robustness. © 2024 IEEE.

关键词： Reamers

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Design of a University Innovation and Entrepreneurship Project Management System Based on Web and Collaborative Filtering algorithms

Design of a University Innovation and Entrepreneurship Proje...

引用

image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Peng Yu Wuhan Polytechnic Wuhan Hubei China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

Given the significant challenges affecting college learners in regards to entrepreneurship and innovation as viewed in the traditional Chinese enterprise cooperation, the need for an innovative and entrepreneurship training program remains essential. This paper therefore seeks to design a project management system intended to improve entrepreneurship and innovation in universities. The system will be designed to run on a WEB-based operation module that relies on a mathematical and statistical model used to advance its prospects. An entrepreneurial and innovation classification system will further be incorporated into the system through the use of the CBLOF(t) concept. Computer algorithms will in this case play a critical role in the personalization and the optimization of the innovative and entrepreneurial project management system. Based on this, a monitoring function will equally be incorporated into the system to address abnormalities in the processing of innovative and entrepreneurial data. The outcomes of the systems are intended to enable university students to gain a deeper understanding of innovative and entrepreneurial projects, thus promoting the quality of project management.

关键词： Training Technological innovation image processing Entrepreneurship Project management Computer applications Mathematical models Classification algorithms Monitoring Optimization

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Dynamic Line Sag Monitoring Using image-Based Bézier Curve Fitting

Dynamic Line Sag Monitoring Using Image-Based Bézier Curve ...

引用

IEEE SSD International Multi-conference on systems, Signals and Devices

作者： Achref Abed Hafedh Trabelsi Faouzi Derbel Faculty Engineering Sciences University of Applied Sciences Leipzig Leipzig Germany Department of Electrical Engineering and Information Technology National Engineering School of Sfax Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper presents an automated system for detecting the sag of overhead transmission lines using image processing. A camera, mounted centrally between two towers, is repositioned using stepper motors to ensure symmetrical alignment with the transmission line. By detecting the lowest point of the line in the image, the system estimates the sag through Bezier curve fitting and converts the pixel-based measurements into real-world dimensions. The proposed system demonstrates high precision and accuracy, providing a reliable method for sag measurement in transmission line monitoring.

关键词： Power transmission lines Accuracy image processing Heuristic algorithms Poles and towers Transmission line measurements Real-time systems Safety Curve fitting Monitoring

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A Deep Neural Network for image Classification Using Mixed Analog and Digital Infrastructure 3rd

A Deep Neural Network for Image Classification Using Mixed A...

引用

3rd International conference on Emergent Converging Technologies and Biomedical systems, ETBS 2023

作者： Kala, R. Punitha, M. Poomani Alias Banupriya, P.G. Veerasamy, B. Bharathi, B. Alzubi, Jafar Ahmad Abed Associate professor Department of Computer Science and Engineering Karpagam College of Engineering Tamil Nadu Coimbatore641032 India Department of Information Technology Sethu Institute of Technology Kariapatti Puloor India Department of Computer Science and Engineering Karpagam Institute of Technology Tamil Nadu Coimbatore641032 India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil626126 India Department of Information Technology Coimbatore Institute of Engineering and Technology Tamil Nadu Coimbatore641109 India School of Engineering Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9789819986453

Recently, Deep Learning (DL) methods are a well-known solution for image and object detection in computer vision applications. The software-based implementation of DL algorithms requires huge resources and consumes more power. To overcome these challenges, a hardware implementation of Deep Neural Network (DNN) using the integrated analog–digital architecture was proposed in this article. The Very Large-Scale Integration (VLSI)-based DNN implementation utilizes Pulse-Width Modulation (PWM), and analog–digital circuits. The integration of analog and digital architecture integrates the benefits of both circuits, which reduces power consumption and enhances processing speed and classification accuracy. The fabrication of the VLSI chip includes the DNN neuron circuit, PWM circuit, digital adders-subtracters, and SRAM memory. The experimental result verifies that the presented DNN VLSI architecture reduces the power consumption of the neural system, and enhances the image classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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On the Impact of Face image Quality on Morphing Attack Detection

On the Impact of Face Image Quality on Morphing Attack Detec...

引用

IEEE International Joint conference on Biometrics (IJCB)

作者： Annalisa Franco Matteo Ferrara Chengcheng Liu Christoph Busch Davide Maltoni Dipartimento di Informatica - Scienza e Ingegneria University of Bologna Xi’an Jiaotong University Norwegian University of Science and Technology and Hochschule Darmstadt

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

The morphing attack is widely acknowledged as an important security threat to face recognition systems in the context of electronic machine readable travel documents and several possible countermeasures have been recently proposed. Among the existing solutions, differential Morphing Attack Detection (MAD) algorithms, based on the comparison of the document image (possibly morphed) and a trusted live capture, proved to be quite effective and robust in detecting this kind of attack. However, deploying such solutions in a real-world operational scenario requires the capability of dealing with images of variable quality in terms of illumination, pose, focus, etc. This paper analyzes the impact of face image quality on MAD performance through an extensive image quality assessment, carried out on a large and realistic operational dataset using different state-of-the-art algorithms, thus providing useful insights for the development of more robust MAD systems.

关键词： image quality Q-factor Correlation Face recognition Lighting Logic gates Streaming media Security Reliability Probes

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Sclera Segmentation on the Half-UNet Neural Network

Sclera Segmentation on the Half-UNet Neural Network

引用

Digital Signal processing and its Applications (DSPA), International conference on

作者： Sergei Chernyadyev Natalya Kharina Department of the Radio Electronic Means Vyatka State University Kirov Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Nowadays, technology for biometric authentication technologies is actively implemented, users can usually authenticate themselves by their face, voice, fingerprint, vascular bed of the finger, palm, iris. Using images of sclera vessels for authentication is mathematically and technically more complex, so it is used less often. In addition, existing sclera segmentation algorithms often do not allow ensuring the required quality of authentication systems. The paper proposes an algorithm for sclera segmentation based on the lightweight convolutional neural network Half-UNet, and compares the obtained results with known algorithms for sclera segmentation. Analysis of the obtained results shows that the developed algorithm effectively highlights the eye sclera region, surpassing known modern algorithms, and can be used in implementation of biometric authentication system as a stage of processing scanner images before the sclera vessels segmentation stage.

关键词： Performance evaluation image segmentation Neural networks Memory management Signal processing algorithms Authentication Biometric authentication Mobile handsets System implementation Iris recognition

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An Adaptive Single image Dehazing Method Based on Multi-Feature Fusion

An Adaptive Single Image Dehazing Method Based on Multi-Feat...

引用

Chinese Control and Decision conference, CCDC

作者： Guifang Shao Tao Wei Qingyuan Zhu Yunlong Gao Minyu Cheng Wusi Wen Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University Xiamen China State Key Laboratory of Intelligent Agricultural Power Equipment Henan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Dehazing plays a crucial role in enhancing image quality for successive image processing applications. However, current single image dehazing algorithms demonstrate varied performance on different scenes. In this paper, we introduce an adaptive image dehazing scheme designed to enhance dehazing efficiency. Our approach involves classifying input images into 6 scenes through multiple feature extraction and analysis techniques. We have developed a method to select the top three appropriate dehazing methods from 15 ones, based on a comprehensive fusion evaluation of multiple indexes for each scene. Moreover, we have created specific color cast correction means to tackle two distinct issues. Comparative experiments on various collection images have verified the efficiency of our proposed method.

关键词： image quality Scene classification image color analysis Machine vision Feature extraction Classification algorithms Indexes

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LW-YOLOv8: An Lightweight Object Detection Algorithm for UAV Aerial imagery

LW-YOLOv8: An Lightweight Object Detection Algorithm for UAV...

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Natural Language processing (ICNLP), International conference on

作者： Hu Chen Dianwei Wang Jie Fang Yuanqing Li Sidi Xu Zhijie Xu School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China School of Computing and Engineering University of Huddersfield Huddersfield UK

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Deep learning is vital for improving drone intelligence through UAV aerial object detection. However, current algorithms struggle to balance detection speed and accuracy. To address this issue, in this paper we propose a lightweight UAV aerial image object detection algorithm based on YOLOv8 named LW-YOLOv8. Firstly, the lightweight VanillaNet architecture is used in the backbone network to effectively reduce the number of YOLOv8 network parameters. Then, a feature pyramid network AFPNs based on shallow network fusion is proposed to improve the detection performance of small targets in UAV aerial images. Experimental results on the VisDrone2019 dataset show that compared with the YOLOv8 algorithm, the proposed algorithm reduces the number of parameters and improves the detection accuracy simultaneously.

关键词： Deep learning Accuracy Object detection Lightweight structures Network architecture Autonomous aerial vehicles Feature extraction Natural language processing Drones

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