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

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Deep Learning Deblurring for Improved Vehicle Number Plate Detection

Deep Learning Deblurring for Improved Vehicle Number Plate D...

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Smart systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC), International conference on

作者： Kalaimathi B Jayanthy S Suriya Krishna M Subish R Kanna Sanjay K Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

This study describes a novel way for improving automatic license plate recognition (ALPR) systems, with a focus on addressing obstacles associated with indistinct license plate photos. The suggested method combines advanced picture deblurring algorithms with reliable information extraction methods to improve the accuracy and dependability of ALPR systems. The major goal is to create a comprehensive pipeline capable of efficiently extracting license plate information while improving image clarity. This entails using sophisticated deblurring algorithms to reduce distortion effects and hence improve overall image quality. Subsequently, advanced computer vision algorithms recognize relevant elements such as characters and patterns, allowing for accurate content recognition. Finally, post-processing techniques are used to thoroughly evaluate and refine the collected plate content. By greatly enhancing accuracy and dependability, particularly in practical circumstances, the proposed ALPR system promises to make major contributions to traffic management and security applications.

关键词： image quality Accuracy Law enforcement Transfer learning Traffic control Information retrieval Distortion

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Modified U Net Based Learning Model for Semi Supervised Medical image Segmentation 3

Modified U Net Based Learning Model for Semi Supervised Medi...

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3rd International conference on Power, Energy, Control and Transmission systems, ICPECTS 2022

作者： Ramya, P. Babu, S. Venkatesh Venkatesan, G. PSNA College of Engineering and Technology Department of AI and DS Dindigul India SNS College of Engineering Department of CSE Coimbatore India Sri Sai Ram Engineering College Department of EEE Chennai India

ISBN: (纸本)9781665462754

The applications include scene interpretation, medical image processing, robotic perception, video based scrutiny systems, augmented and virtual reality, among many others the image segmentation is a being a key topic. In the literature, many picture segmentation methods have been created, and there have been a lot of recent efforts aimed at creating image segmentation algorithms using deep learning models due to the success of these models in a broader range of vision based utilities. The adaptability of a learning model to segment medical images like Modified U-net based convolution neural network has shown good results in retinal vessel region extraction and has greater feature learning ***, using the DRIVE and STARE datasets, we compare the accuracy, specificity, and sensitivity of modified U-Net deep learning with those of the U-Net model, and we also highlight several promising future research directions. © 2022 IEEE.

关键词： image segmentation

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Color Transformations of Video Endoscopic images

Color Transformations of Video Endoscopic Images

引用

International conference on Vision, image and Signal processing (ICVISP)

作者： Natalia A. Obukhova Alexandr A. Pozdeev Konstantin A. Smirnov Alexandr A. Motyko Olga R. Savelieva SPbSU ETU “LETI” St. Petersburg Russia

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

The paper considers the main procedures of digital processing of endoscopic images aimed at increasing their diagnostic value. It is shown that the existing approaches, solving private problems, lead to the general inconsistency of color transformations in the endoscopic video system. A new multi-objective quality criterion for the synthesis of color transformations, taking into account both the accuracy of color reproduction and the transmission of local color contrast, is proposed. Exemplary methodologies for target-specific color transformation synthesis are developed and validated, addressing critical challenges in endoscopic image enhancement. Experimental results underscore the efficacy of the approach in maintaining color integrity while significantly amplifying the diagnostic value through enhanced contrast articulation. The proposed algorithms were evaluated on diverse datasets of endoscopic images, demonstrating a substantial improvement in local contrast and image clarity compared to existing techniques. The virtual chromoendoscopy method achieved a balance between natural color representation and enhanced diagnostic features, while the bimodal visualization approach optimized the integration of fluorescence and white-light imaging. These results contribute to the evolution of visualization and perceptually optimized imaging solutions, marking a substantial advancement in medical image processing. The study establishes a robust foundation for the development of context-aware imaging methodologies in modern medical systems.

关键词： Visualization Accuracy image color analysis Signal processing algorithms Signal processing Fluorescence Biomedical image processing Medical diagnostic imaging image enhancement

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Traffic image text detection via YOLOv8 based on attention mechanism and rotation proposals location

Traffic image text detection via YOLOv8 based on attention m...

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International conference on Advanced Mechatronic systems (ICAMechS)

作者： Zhou Xianwang Zhang Xianfeng Engineering College Guangzhou College of Technology and Business Foshan China

ISBN: (数字)9798350366204

ISBN: (纸本)9798350366211

Traffic scene text detection is an important technology in the field of image processing, which has significant practical significance in the applications of assisted driving, automatic driving and intelligent transportation. However, traffic scene text detection faces many challenges, such as background complexity and text Angle diversity. In this paper, the YOLOv8 model is applied to text detection tasks in traffic scenarios. In YOLOv8s model, attention mechanism is introduced to enhance feature extraction ability. At the same time, Circular Smooth Label (CSL) is used for Angle classification, so as to achieve accurate prediction of text with different oblique *** experimental results show that compared with DBNet algorithm, F-measure on self-made datasets CTST and public datasets ICDAR2015 is improved by 1.7% and 2.1%, respectively. In addition, the proposed algorithm is 2.03 times faster than the EAST algorithm in terms of image processing speed, and the detection time of a single image is about 0.03 seconds, which effectively meets the needs of real-time detection during driving.

关键词： Attention mechanisms Mechatronics Shape Text detection Transportation Prediction algorithms Feature extraction Real-time systems Classification algorithms Proposals

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Automated Optic Disk Detection in Fundus images using a Combination of Deep Learning and Local Histogram Matching

Automated Optic Disk Detection in Fundus Images using a Comb...

引用

conference on Medical Imaging - Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Tavakoli, Meysam Univ Pittsburgh Med Ctr Dept Radiat Oncol Pittsburgh PA 15260 USA UPMC Hillman Canc Ctr Pittsburgh PA 15232 USA

ISBN: (数字)9781510649484

ISBN: (纸本)9781510649484;9781510649477

Retinal images have been used in the diagnosis of many ocular diseases such as glaucoma and diabetic retinopathy. Here, automatic detection of optic disk (OD) is essential in deriving clinical parameters to assist clinical diagnosis. In fact, detecting OD center and its boundary is the essential step of most vessel segmentation, disease diagnostic, and retinal recognition algorithms. In this study, we proposed a new approach for localizing OD by combining local histogram matching and the concept of deep learning. The algorithm is composed of 4 steps, image partitioning, Local histogram matching and validation, Convolutional Neural Network (CNN) classification, and OD detection. Here, we used OD of the five reference retinal images in each dataset to extract the histograms of each color channel. Then, we calculated the mean of histograms for each channel as template for creating some OD candidates. An AlexNet-like CNN was applied to classify candidates as ODs or nonODs. The candidates used as an input to feed the CNN for final classification. In this study, we worked on three databases (one rural, MUMS-DB, and two publicly available databases, DRIVE, STARE) including 520 retinal images to evaluate the proposed method. The accuracy of our algorithm was 100%, 90%, and 95% for the DRIVE, STARE, and MUMS-DB respectively. It is shown that this method provides higher detection rates than the existing methods that have reported.

关键词： image segmentation image processing image processing algorithms and systems Optical discs image resolution MATLAB Radon transform

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Using image processing and Optical Character Recognition to Recognise ID cards in the Online Process of Onboarding

Using Image Processing and Optical Character Recognition to ...

引用

2022 International conference on Smart Information systems and Technologies, SIST 2022

作者： Gulnara, Bektemyssova Yerassyl, Akhmer International Information Technology University Almaty Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

A broad range of computer vision studies have been conducted on the recognition of identity documents using mobile devices. A portfolio of techniques and algorithms for solving problems like face recognition, document detection and correction, text field recognition, and so on has been developed, and the lack of data sets has become a critical challenge. The electronic ID card has been commonly used to verify or recognize an individual since its discovery in 2011. Take note of several issues, such as high requirements for detecting the id-card field and challenges recognizing the data in the id-card. In this research, we realized an approach for identifying electronic ID cards using image detection and Optical Character Recognition (OCR). This paper provides a short outline of how banking institutions and other fintech companies utilize highly developed OCR tools to automate their processes, enhancing not just service quality, as well as the precision, speed, and protection of their transaction data. As an outcome of our image processing and OCR techniques, we can achieve 99 percent ID detection. This research was based on the interface of a mobile application, such as those owned by one of Kazakhstan's banks. © 2022 IEEE.

关键词： Optical character recognition

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SWT-UNet: Medical image Segmentation Based on Multi-modality UNet with Sliding Window Transformer Block

SWT-UNet: Medical Image Segmentation Based on Multi-modality...

引用

2023 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Yangqianhui Han, Dong Gang, Xiaoming Ma, Jiajun Yuan, Xu Zhejiang University School of Mechanical Engineering Hangzhou China Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou China Anshan Municipal Central Hospital Anshan China Shenhua Hollysys Information Technology Co. Ltd Beijing China Dalian University of Technology School of Software Technology Dalian China

ISBN: (纸本)9798350337488

Medical images are information carriers that visually reflect and record the anatomical structure of the human body, and play an important role in clinical diagnosis, teaching and research, etc. Modern medicine has become increasingly inseparable from the intelligent processing of medical images. In recent years, there have been more and more attempts to apply deep learning theory to medical image segmentation tasks, and it is imperative to explore a simple and efficient deep learning algorithm for medical image segmentation. In this paper, we conduct research on multi-modal medical image segmentation algorithms with a hybrid architecture of Convolutional Neural Networks and Vision Transformer. This paper proposes a multi-modal medical image segmentation model SWT-UNet based on the CNN-ViT hybrid framework. The self-attention mechanism and sliding window design of the visual Transformer are used to capture the global feature association and break the limitation of the receptive field caused by the inductive bias of the convolution operation. At the same time, a widened self-attentive vector is used to streamline the number of modules and compress the model size, so as to fit the characteristics of a small amount of medical data, which makes the model easy to be overfitted. Experiments on two multi-modal medical image datasets validate that the algorithm can achieve efficient medical image segmentation at a lightweight scale. © 2023 IEEE.

关键词： attention mechanism medical image segmentation multi-modality Vision Transformer

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ADHHI airborne hyperspectral imager: camera structure and geometric correction 28

ADHHI airborne hyperspectral imager: camera structure and ge...

引用

conference on image and Signal processing for Remote Sensing XXVIII

作者： Dong, Jieke Duan, Yansong Zhou, Qi Zhao, Xinbo Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

ISBN: (纸本)9781510655386;9781510655379

For decades, the design of remote sensors has to make trade-offs among many characteristics such as the field of view (FOV), spatial resolution, spectral resolution, radiometric resolution, and the number of bands. It's inevitable to weaken some characteristics to enhance others. Moreover, these problems lead to using multi-sources of remote sensing data in practical projects where a single sensor can't meet the relevant requirements. The Airborne Dual-mode High-resolution Hyperspectral imager (ADHHI) provides a new solution to the above limitations by the technology of multi-camera stitching. In this way, many excellent but conflicting characteristics can be separated into different imaging sub-systems, and are combined together during data processing. Supported by related processing algorithms and software, ADHHI embeds many excellent characteristics into one system, such as high spatial resolution, high spectral resolution, and high radiometric resolution. Firstly, this paper picks some common imaging sensors to illustrate the problem of conflicting characteristics. Secondly, we introduce the camera structure and sensor parameters of ADHHI. Then, we sketch out the data processing workflow and elaborate on relevant principles and results of the whole geometric correction, such as homospectral stitching and hetero-spectral registration. Finally, this airborne hyperspectral imager's advantages and application prospects are concluded.

关键词： geometric correction image stitching hyperspectral imager airborne pushbroom

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Survey of Approaches to Non-Realistic Neural Style Transfer 31

Survey of Approaches to Non-Realistic Neural Style Transfer

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31st conference of Open Innovations Association FRUCT, FRUCT 2022

作者： Varlamova, Arina Kitov, Victor Lomonosov Moscow State University Moscow Russia

ISBN: (纸本)9789526924472

Despite researchers interest toward style transfer problem, there is still no foremost method available. Difficulties in problem formalization make a comparison of methods especially complicated. This paper covers twelve widely used algorithms in order to provide their comprehensive description and advantages one over another. © 2022 FRUCT Oy.

关键词： Technological innovation image processing Real-time systems Convolutional neural networks

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Exploration of Motion Increase Rate Based on Infrared Feature Capture and CFD Numerical Simulation

Exploration of Motion Increase Rate Based on Infrared Featur...

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Electronics, Energy systems and Power Engineering (EESPE), IEEE International conference on

作者： Minghan Wang Beijing University of Technology Beijing China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

The purpose of this study is to explore the motion incremental speed technology based on infrared feature capture and CFD numerical simulation, through the sensor, image processing and analysis algorithms, etc. On the basis of exploring the target's motion characteristics, combined with the Chancellor's theory of ballistic trajectory, and real-time capture of the target's thermal feature information is to achieve accurate target tracking and motion trajectory adjustment. Firstly, this paper adopts the image processing and analysis algorithm, and uses the normal distribution model to process the infrared data, so as to construct the target motion model. Secondly, CFD numerical simulation is used to analyse mechanisms such as motion boost guidance in order to provide more accurate target tracking and trajectory correction schemes. Finally, the motion region of the updraft is predicted by spherical averaging of the fluctuation equation. The results show that the infrared feature capture technique is able to maintain efficient motion track correction and motion speed-up capability based on real-time data processing in dynamic environments.

关键词： Target tracking Accuracy image processing Gaussian distribution Numerical simulation Mathematical models Real-time systems Data models Numerical models Trajectory

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