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

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Circular Synthetic Aperture Radar for Near-field Terahertz Imaging 5

Circular Synthetic Aperture Radar for Near-field Terahertz I...

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5th International conference on Multimedia, Signal processing and Communication Technologies (IMPACT)

作者： Gezimati, Mavis Singh, Ghanshyam Univ Johannesburg Ctr Smart Informat & Commun Syst Dept Elect & Elect Engn Sci Auckland Pk Kingsway CampusPOB 524 ZA-2006 Johannesburg South Africa

ISBN: (纸本)9781665476478

The potential of synthetic aperture radar (SAR) systems to yield high resolution images has led to its exploration in new applications. The conventional microwave radar imaging suffer limited range resolution in the sub-millimeter range and optical imaging methods are constrained by the diffraction limit. In the Terahertz (THz) spectrum, SAR systems are capable of operating in all weather, all time and penetrating through clouds, smoke, dust etc. to achieve better resolution images beyond the diffraction limit. In this paper, the different aspects of Multimedia computing are investigated in THz circular SAR as a new mode of radar imaging for near-field applications for example indoor environment. We have explored a new toolbox that enables the rapid development of near-field THz imaging SAR systems and generation of large near-field THz imaging scenarios that could be used for data driven applications. A circular SAR imaging system is presented for 2D (two-dimensional) imaging of targets in THz-SAR and the radar images are reconstructed using the Back Propagation and Polar Formatting algorithms. The SAR performance evaluation metrics are reported and scope for future work is discussed.

关键词： Synthetic aperture radar (SAR) THz imaging Circular SAR Near-field Radar image reconstruction High resolution imaging Reconstruction algorithms MIMO FMCW Multimedia

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Research on Fruit Picking Positioning Based on Binocular Vision

Research on Fruit Picking Positioning Based on Binocular Vis...

引用

Signal processing and Intelligent Computing (SPIC), International conference on

作者： Lijuan Qin Xiaohan Wang Yubin Zhu Ting Wang School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Based on YOLOv5 object detection algorithm, fruit images are extracted from complex backgrounds in response to occlusion of branches and leaves, fruit overlap, and light effects caused by environment. Method of adding center coordinate value of image based on prediction box of fruit image determines centroid coordinate of target image. Then, by optimizing the focus camera and using Zhang Youzheng's calibration method, the image is calibrated using Matlab calibration box. Obtained image is stereo corrected using local stereo matching algorithm to obtain three-dimensional coordinates of centroid of target fruit, which are then converted into physical coordinates for positioning, After actual measurement, a positioning result with an error of ± 20mm was obtained, confirming its applicability for practical positioning operations.

关键词： YOLO Visualization Signal processing algorithms Visual systems Signal processing Cameras Prediction algorithms Calibration Standards Matlab

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Construction Site Fence Recognition Method Based on Multi-Scale Attention Fusion ENet Segmentation Network 35

Construction Site Fence Recognition Method Based on Multi-Sc...

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35th International conference on Software Engineering and Knowledge Engineering, SEKE 2023

作者： Zhang, Xing Tang, Tinglong Wu, Yirong Quan, Tingwei Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang China Wuhan National Laboratory for Optelectronics Huazhong University of Science and Technology Wuhan China

In this paper, we propose a fence recognition method based on the ENet (Efficient neural Network) segmentation network to address the problems of traditional segmentation networks, which have poor performance in recognizing fences with a large range of scale variations and hollow structures. Firstly, a multi-scale attention fusion ENet segmentation network is designed, which is trained using the fence with obvious color features. Then, a morphological algorithm is used to process the predicted image to restore the fence segmentation results. The designed multi-scale attention fusion segmentation network performs better on fence datasets than traditional methods. In addition, the activation function Leaky_Relu6 further enhances the stability and generalization ability of the network. The experiments are conducted on 540 fence images from different construction sites, and the computed IoU is 90%. The processing speed is about 28 frames per second. The experimental results show that our proposed network outperforms traditional segmentation algorithms in fence recognition performance, and achieves robustness in different construction scenarios while meeting the requirements of both accuracy and speed. © 2023 Knowledge systems Institute Graduate School. All rights reserved.

关键词： image segmentation

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Advanced Hybrid algorithms for High-Efficiency image Compression in Satellite and Drone systems

Advanced Hybrid Algorithms for High-Efficiency Image Compres...

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Recent Progresses in Science, Engineering and Technology (ICRPSET), International conference on

作者： Priyankar Biswas Ualiur Rahman Rahat Ashiquzzaman Lemon Ishita Mazumder Department of Electrical and Electronic Engineering Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj Bangladesh

ISBN: (数字)9798331509477

ISBN: (纸本)9798331509484

In satellite and drone imaging, a huge amount of data is generated; therefore, image compression should be done efficiently. This hybrid algorithm document deals with Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Singular Value Decomposition (SVD) based approaches for effective image compression with quality preservation. The proposed approach will be suitable for satellite and drone images since it reduces the cost of storage and transmission. The simulations demonstrate that, for the same amount of distortion, the algorithm outperforms state-of-the-art algorithms by as much as above 48% in compression ratio or up to ten times in processing speed. This is enabled by the effective combination of strengths between lossy and lossless techniques in solving some unique challenges about satellite and drone systems, including limited resources and real-time processing. Therefore, this may be an effective means of boosting image compression in remote sensing.

关键词： image coding Satellites Transforms Discrete wavelet transforms Satellite images Discrete cosine transforms Time complexity Remote sensing Drones Singular value decomposition

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Review of various image Mining Techniques 6

Review of various Image Mining Techniques

引用

6th International conference on Computing Methodologies and Communication, ICCMC 2022

作者： Kavitha, C. Balaganesh, D. Sukumar, M. Mathan, J. Madurai Kamaraj University College Dept of Computer Science Tamil Nadu Madurai India Lincoln University College Faculty of Computer Science and Multimedia Malaysia

ISBN: (纸本)9781665410281

One of the easiest ways of communication is possible through interpreting wide range of images on various applications. It is the non-structural data that does not have clear semantics. image mining techniques implemented on the images are used to extract features/train the real time systems. This can be achieved using combination of various algorithms of image processing, machine learning and artificial intelligence. This paper discusses about those image mining techniques and the challenges faced by various researchers. © 2022 IEEE.

关键词： Real time systems

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Accurate Segmentation and Classification of Brain Tumor using Deep Learning Approaches 2

Accurate Segmentation and Classification of Brain Tumor usin...

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2nd IEEE International conference on Advances in Information Technology, ICAIT 2024

作者： Benedict, J.N. Shanmugapriya, S. Senthil Pandi, S. Kumar, P. Department of CSE Rajalakshmi Engineering College Chennai India Department of CSE Kalasalingam Academy of Research and Education Srivilliputtur India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Brain tumors are abnormal growths of tissue within the skull, posing serious health risks including brain cancer and related diseases. In healthcare, there's a growing focus on early detection methods, facilitated by surveillance decision support systems, to identify brain tumors promptly. Among the leading techniques for detection is the analysis of Magnetic Resonance images (MRIs), which provide crucial information about abnormal tissues. Researchers and doctors are actively exploring ways to improve the accuracy and efficiency of brain tumor detection through advanced imaging technologies and data analysis algorithms. By enhancing early detection capabilities, healthcare professionals aim to improve patient outcomes and facilitate timely interventions for individuals at risk of developing brain tumors. This study aims to identify and localize tumor areas in the brain through the utilization of advanced image processing techniques and a deep wavelet autoencoder method. Initially, a specialized clustering technique is applied to MRI images to segment the tumor areas. Subsequently, the detected tumor regions are refined using thresholding and level-set segmentation methods to achieve accurate delineation. Finally, a deep wavelet autoencoder is employed for precise classification of brain tumors based on the extracted features. Evaluation metrics are utilized to assess the performance of the proposed model, demonstrating its superiority over existing state-of-the-art approaches across all evaluation criteria. The combination of clustering, segmentation, and deep learning techniques allows for the efficient recognition and localization of brain tumors, providing valuable insights for medical diagnosis and treatment planning. The robustness and accuracy of the proposed model make it a promising tool for enhancing the efficiency and efficacy of brain tumor detection in clinical practice. © 2024 IEEE.

关键词： image segmentation

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A Conversion Algorithm for ECG signals on a 2D array based on Digital Signal processing 11

A Conversion Algorithm for ECG signals on a 2D array based o...

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11th International conference on Communications, Circuits and systems (ICCCAS)

作者： Cesar Cabanillas, Julio Kemper, Guillermo del Carpio, Christian Univ Peruana Ciencias Aplicadas Fac Engn Sch Elect Engn Lima Peru

ISBN: (数字)9781665469890

ISBN: (纸本)9781665469890

This work proposes a computational algorithm to convert digital files containing electrocardiogram (ECG) information into 1D signals. Many medical databases have in storage files containing ECG information that is not easy to process for computational algorithms. Digitization by the proposed method makes it possible to modernize the databases of many health centers in order to perform post-processing of the signals obtained. This method is based on applying digital signal processing techniques to images obtained from a PDF file produced by an electrocardiograph. The proposed algorithm takes into consideration the thickness of the printed signal in the PDF image so that it does not introduce distortion in the final 1D signal. Due to the distribution of the ECG signals on the PDF files the algorithm identifies and segments the signals on 2 dimensions. The results show that the proposed method can correctly reproduce the information of the ECG waves captured in the PDF file regardless of the elements outside the ECG signal such as the background grid or the different information indicators, whether they are labels or references of the ECG signals. The algorithm has an accuracy of 95% based on the statistical analysis performed for all samples.

关键词： Digital Signal processing ECG signals Conversion Printed Signal Digitization 2D array

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Automatic Recognition of Non-standard Number Plates using YOLOv8 18

Automatic Recognition of Non-standard Number Plates using YO...

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18th INDIAcom;11th International conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Tupsounder, Asaavi Patwari, Renuka Ambati, Roja Chavarkar, Arya Shirsat, Kavita Vidyalankar Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9789380544519

A critical feature of vehicle movement applications is the detection and identification of a vehicle's License Plate (LP). Despite technological and algorithm developments, differences in LP properties by nation, such as number system, colors, character language, fonts, and size, necessitate more study to enhance detection and recognition. Despite substantial study, many systems function in well-defined contexts, necessitating the use of expensive equipment to capture photos from slow-moving vehicles or generate high-quality shots. This study describes a deep learning-based automated approach for LP detection and recognition, which is further separated into detection and character recognition. Yolov8 (You Only Look Once Version-8) was utilized for automatic pixel separation and gradient computation, making complicated model training easier. Using deep learning, Easy OCR (Optical Character Recognition) was utilized to retrieve text from number plates. Easy OCR (Optical Character Recognition) was utilized to extract text from license plates, with deep learning algorithms employed to effectively read text in difficult settings. For image and video processing, OpenCV (Open-Source Computer Vision Library) was employed. The project titled 'Automatic Number Plate Using YOLOv8 Model' leverages the advanced capabilities of the YOLOv8 model to develop a robust system for automated number plate recognition, serving as the core component for real-time object detection and number plate localization within images and video streams. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： ANPR (Automatic Number Plate Recognition) Deep learning image Recognition Non-Standard OpenCV YOLOv8

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Comparing the Effectiveness and Performance of image processing algorithms in Face Recognition

Comparing the Effectiveness and Performance of Image Process...

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International Carpathian Control conference (ICCC)

作者： Hussam M. Rostum József Vásárhelyi Institute of Automation and Info-Communication University of Miskolc Miskolc Hungary

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

Many institutions have recently embraced biometric security solutions, utilizing biological measurements to safeguard against fraudulent activities, theft, and various security threats. Face recognition technology holds a pivotal role within the realm of bio-metric security systems, serving purposes such as authentication, monitoring, individual identification, and identity verification. This article aims to delve into the examination of facial recognition systems grounded in deep learning. This focus arises due to the intricate nature of the process and the existence of numerous hurdles and variables that impact algorithm performance. The objective here is to illuminate the foremost challenges that real-world systems encounter, often overlooked in previous research. Additionally,under these challenges, the article will conduct a comparative analysis of the performance of prominent facial recognition algorithms, namely VGGFace, FaceNet, and ArcFace. This academic approach will allow to make informed choices when selecting the most suitable algorithms for specific applications.

关键词： Deep learning Employee welfare Databases Face recognition image processing Lighting Biology

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A Two-Stage Multispectral Demosaicking Method Using Neville Filters and Directional Interpolation

A Two-Stage Multispectral Demosaicking Method Using Neville ...

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Signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Wenzhuo Yang Yakun Liu Yan Shi Wei Li Ran Tao School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Snapshot multispectral imaging systems typically capture multispectral images in a single shot by covering the sensor with a multispectral filter array (MSFA). A demosaicking algorithm is generally required for such systems to reconstruct the full-resolution multispectral images. A two-stage demosaicking method is proposed in this paper. In the first stage, the image is progressively interpolated by Neville filters. In the second stage, the directional interpolation with a novel weighting function is performed to enhance the quality of the pre-interpolated image. Experimental results demonstrate that the proposed approach achieves superior performance in both subjective and objective assessments.

关键词： Interpolation Multispectral imaging Array signal processing Filtering algorithms Information filters Data processing image reconstruction Sensor arrays

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