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

共 15613 条记录，以下是561-570 订阅

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Advancements in image Deblurring and Performance Metrics Using Deep Learning Technique 12th

Advancements in Image Deblurring and Performance Metrics Usi...

引用

12th International conference on Recent Trends in Computing, ICRTC 2024

作者： Dhavalagimath, Shankramma S. Rajesh, T.M. Singh, Rakesh Kumar Department of Mathematics Dayananda Sagar University Bangalore562112 India Department of Computer Science and Engineering Dayananda Sagar University Bangalore562112 India

ISBN: (纸本)9789819788354

In computer vision and image processing, image deblurring is a crucial phase that attempts to restore the sharpness of the image and clarity of images that have been damaged due to motion blur, defocus, or other factors. The multiple image deblurring methods are thoroughly investigated in this work, along with the performance evaluation metrics that were employed to evaluate each method's efficiency. We started by examining the most recent deblurring techniques, which include both deep learning-based and traditional algorithms. Every technique's advantages, disadvantages, and suitability are examined in different scenarios. Furthermore, we investigate the finer details of performance metrics that are frequently used to rate the quality of deblurred images. These metrics include subjective assessments from user research &objective measurements like PSNR and SSIM. This study aims to elucidate the benefits and drawbacks of current deblurring methods in diverse contexts through extensive experimentation. Moreover, we examine the applicability and consistency of performance evaluation metrics for evaluating the accuracy and perceived quality of deblurred images. By supporting researchers and professionals in identifying suitable techniques and assessment standards for their particular applications, the knowledge gained from this study improves the field of image deblurring. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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RWU3D: Real World ToF and Stereo Dataset with High Quality Ground Truth 12

RWU3D: Real World ToF and Stereo Dataset with High Quality G...

引用

12th International conference on image processing Theory, Tools and Applications, IPTA 2023

作者： Agrawal, Ankita Muller, Tobias Schmahling, Tobias Elser, Stefan Eberhardt, Jorg University of Applied Sciences Institute for Artificial Intelligence Institute for Photonic Systems Hochschule Ravensburg-Weingarten Weingarten Germany

ISBN: (纸本)9798350325416

This paper introduces a new dataset RWU3D that consists of ToF depth images and the corresponding amplitude images as well as high-resolution Stereo images and the respective ground truth data. The corresponding disparity and depth images are also available. The dataset is recorded for the real world scenarios that can be used to obtain a more accurate estimation of a particular scene through the fusion of ToF and Stereo characteristics. We built a system comprising of a state of the art ToF sensor, high-resolution RGB cameras, and a laser scanner for obtaining ground truth. The data from the different sensors is calibrated to one coordinate system, which is a challenging task in itself. The data is recorded for 11 different real world scenes with 770 total frames in total obtained with our system. The dataset would present new challenges for the experimentation with the ToF data and contribute towards the development of algorithms designed to analyze and improve the ToF data. © 2023 IEEE.

关键词： Stereo image processing

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Maritime low-light image enhancement study based on improved ResUnet 23

Maritime low-light image enhancement study based on improved...

引用

6th International conference on Signal processing and Machine Learning, SPML 2023

作者： Wang, Jingyang Pan, Mingyang Zhang, Ruolan Liu, Zongying Li, Shaoxi Navigation College Dalian Maritime University China

ISBN: (纸本)9798400707575

Recent years, both the domestic and international frontiers made headway in the realm of intelligent navigation for ships, so vision-based ship monitoring systems and smart ships had a great development prospect. However, in the night-time environment at sea, the images usually have low contrast, low visibility and generous noise splash. Deep learning algorithms are affected by these problems, which not only limit the detecting and recognizing capability but also bring the unstable elements in the aspect of safety. To solve these problems, this study constructs paired Maritime-Low-Light datasets and build the MLL-ResUnet (Maritime low-light-ResUnet) model based on the ResUnet model to enhance nighttime images at sea. The CurveNLU module is designed, which adjusts the image brightness curve to make an adaptive adjustment of the image brightness for achieving the image enhancement tasks. Lastly, to consider the image output effect and model light-weighting, we introduce the ORSnet (Original Resolution Subnetwork) module and the GhostConv module. In the experimental part, the MML dataset is applied in the several other state-of-the-art image enhancement algorithms and the proposed algorithm. Based on the compared results, we find that the model proposed achieves impressive results. Specifically, it scores 26.865 and 0.84 in the PSNR and SSMI quality evaluation indexes, surpassing the performance of other algorithms. There is strong evidence to support that the proposed model not only ensures the authenticity of the output image, but also has a certain guarantee for the speed of calculation. Overall, the MLL-ResUnet model meets the requirements of the ship monitoring system and enables real-time monitoring of ship movement at night at sea. © 2023 ACM.

关键词： image enhancement

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Military vehicles marking detection algorithm on a digital image 4

Military vehicles marking detection algorithm on a digital i...

引用

4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Skyruta, Vladyslav Yurchuk, Iryna Software Systems and Technologies department Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (纸本)9798350395532

Constant complicating of wars due to considerable development of military weapon and moving to new generation of warfare led to a significant change in means of it. More and more armed forces involve AI into solving a wide range of tasks, since modern war produces a lot of data which need to be processed and analyzed. In this work, an effective military vehicles marking detection algorithm was developed in order to make it possible to determine the ownership of military equipment based on its marking. It uses pure image processing algorithms instead of involving neural networks, like in R-CNN model or its derivatives. As a result, the algorithm does not require a huge dataset of marking templates and time for network training and can be applied immediately. The algorithm is not bound to a specific marking pattern and can be applied to any. Also it is designed for detecting military marking without reference to objects. As a result, the algorithm can be easily integrated with other image processing systems, e.g. military vehicles detection. © 2023 IEEE.

关键词： Template matching

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Real-time image processing in A Multi-UAV System for Structural Surveillance Through IoT Platform

Real-time Image Processing in A Multi-UAV System for Structu...

引用

conference on Real-Time image processing and Deep Learning

作者： Falcon, Jose Mehrubeoglu, Mehrube Rangel, Pablo Texas A&M Univ Corpus Christi Elect Engn Program Dept Engn Corpus Christi TX 78412 USA

ISBN: (纸本)9781510650817;9781510650800

The use of location and instruction markers for multi-path planning enhancement in any set of unmanned aerial systems' tasks is crucial to the coordination and effectiveness of the individual unmanned aerial vehicles (UAVs). This research implements OpenCV algorithms that allow multiple UAVs to use ArUco markers to receive data related to location and instruction for the purposes of multi-path planning. OpenCV algorithms are utilized to develop vision-based solutions that will enhance the real-time capabilities of the UAVs. The final goal for the multi-drone system entails inspecting and surveying objects for structural damage and applying the developed image processing algorithms to collected images to determine the significance of damage. This project utilizes OpenCV and Python libraries for multi-drone pathway planning by collecting, transmitting, and displaying real-world industrially valuable data over the network infrastructure as an application of Internet of Things (IoT).

关键词： unmanned aerial vehicle UAV OpenCV ArUco marker Internet of Things real-time image processing

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Fairness-aware Dimensionality Reduction 31

Fairness-aware Dimensionality Reduction

引用

31st European Signal processing conference, EUSIPCO 2023

作者： Kose, O. Deniz Shen, Yanning Electrical Engineering and Computer Science University of California Irvine Irvine United States

ISBN: (纸本)9789464593600

In this era of digitalization, the massive increase in available data leads to great potential for advancing various domains. However, the available data, such as images, videos, and speech signals, generally lie in high-dimensional space, which calls for efficient dimensionality reduction techniques to process them efficiently. Furthermore, while the fairness of algorithms is essential for their deployment in real-world systems, the effect of dimensionality reduction on fairness is an under-explored research area. Motivated by this, this paper puts forth a fairness-aware dimensionality reduction framework that is capable of properly compressing the data while mitigating bias. Specifically, our design targets at reducing the correlation between the compressed data and sensitive attributes, while projecting the data into a new coordinate system where most of its variation can be described. Experimental results on the CelebA dataset demonstrate that the proposed dimensionality reduction framework can improve group fairness measures for image classification while providing comparable utility to the conventional techniques. © 2023 European Signal processing conference, EUSIPCO. All rights reserved.

关键词： image classification

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Conversion of STANAG RGsB Analog video to 24-Bit RGB video Format by Using Analog video Decoder and FPGA 1

Conversion of STANAG RGsB Analog Video to 24-Bit RGB Video F...

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1st IEEE International conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Mallikarjun, Bharti Pappa, M. CMR Institute of technology MTech VLSI & Embedded systems Bengaluru India CMR Institute of technology Department of ECE Bengaluru India

ISBN: (纸本)9798350371802

Now a days innovations have become even more invasive;this work outlines a method for converting STANAG RGsB analog video to a 24-bit RGB video format utilizing an analog video decoder and a field-programmable gate array (FPGA). The STANAG RGsB (Red, sync-on-Green, Blue) is a common analog video format used in military applications, and the conversion to a digital RGB format enables compatibility with modern display systems. The proposed solution involves employing an analog video decoder to extract the RGsB video signal and synchronizing it with the FPGA-based video processing system. The analog video decoder converts the RGsB signal into digital video component. Once the digital video component is fed into the FPGA, it performs required processing tasks. The FPGA implementation includes functions such as colour space conversion, synchronization adjustment, resolution scaling, and necessary image enhancements or corrections. The FPGA's programmability allows for customization and optimization of the video processing algorithms to suit the specific requirements of the application. Furthermore, the FPGA can also incorporate additional functionalities such as overlaying graphical elements, text, or video analytics onto the converted RGB video stream. The proposed solution offers a flexible and scalable approach for converting STANAG RGsB analog video to a digital RGB format, leveraging the capabilities of both analog video decoders and FPGA-based video processing. This conversion enables seamless integration of legacy analog video sources into modern video systems, facilitating enhanced situational awareness and visual communication in military applications and beyond. The resulting digital video output provides improved compatibility with modern display systems, hence can be readily displayed on LCD/LED monitors, projectors, or other digital display devices without the need for specialized analog interfaces or converters. © 2024 IEEE.

关键词： Military applications

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Deep learning in the design and application of thoracic medical image-Assisted diagnosis algorithm 7

Deep learning in the design and application of thoracic medi...

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7th International conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Xiao, Yineng The Global Intellectual Property Institute Nanjing University Suzhou215163 China Advanced Institute of Information Technology Peking University Hangzhou311200 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

In recent years, with the development of deep learning (DL) technology and the continuous improvement of algorithms, DL-Assisted diagnosis systems based on medical images have rapidly developed. Compared with traditional image processing methods, DL trains models by combining a large amount of relevant data (e.g. clinical data, imaging data, etc.) and then uses the models to predict disease-related information. Compared with traditional medical image processing algorithms, DL has better performance in recognition, segmentation and classification of medical images. In this paper, an AI-Aided diagnosis algorithm is developed for lung cancer, a malignant tumour disease, based on clinical chest CT data and imaging data. The algorithm uses chest CT images as the object of study, and classifies and evaluates patients based on various factors such as their age, gender, tumour volume and location, as well as their knowledge of the disease. A pre-Trained model was first used to establish an algorithm for lung cancer tumour segmentation and recognition. Convolutional NNs are then applied to learn solutions to the feature extraction and classification problems. Finally, the results obtained are used as model output and the system performance is evaluated to complete the diagnostic process for lung cancer as a class of malignant tumour disease. © 2023 SPIE. All rights reserved.

关键词： Diseases

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Intelligentization of film and television post-production based on complex image processing and scene switching algorithms 4

Intelligentization of film and television post-production ba...

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4th International conference on Smart systems and Inventive Technology, ICSSIT 2022

作者： Wang, Jiangtao Wang, Qi College of Art Hebei University Baoding071000 China

ISBN: (纸本)9781665401180

Intelligentization of the film and television post-production based on the complex image processing and scene switching algorithms is studied in the paper. At present, the film and television media is in a period of rapid de velopment, and new media formats are constantly emerging. The emergence of high-definition television HDTV, the rapid development of the network puts forward new requirements for film and television post-production, in order to adapt to these changes, various software manufacturers continue to improve software functions and keep pace with the *** development of computer virtual technology and the application of the design field have caused a fundamental change in the traditional design process. Therefore, the handover procedure caused by any delay should be minimized, which has attracted the attention of many researchers and seeking new solutions. The novel solutions are proposed model improve the complex image processing and scene switching algorithm performance. Through the verification, the performance is guaranteed. © 2022 IEEE

关键词： Digital television

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Knowledge graph construction for power grid systems based on fuzzy matching algorithm 3

Knowledge graph construction for power grid systems based on...

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3rd International conference on Advanced algorithms and Signal image processing, AASIP 2023

作者： Haitao, Gui Junyang, Tian Heming, Hou Renming, Li Guilin Power Supply Bureau Guangxi Power Grid Co Ltd Guilin Guangxi541002 China Guangxi Power Grid Power Dispatching and Control Center Nanning Guangxi530023 China

ISBN: (纸本)9781510668522

To facilitate the processing of data pertaining to defects and faults in the power grid regulation system, as well as knowledge graph-based support to help regulators in their decision-making, a knowledge graph construction method employing a fuzzy matching algorithm has been devised, the outcomes are presented in the form of a visual knowledge graph. Initially, a fuzzy matching algorithm is devised to to match the defect with the defect-fault database. Subsequently, the Neo4j database is utilized to construct a knowledge graph that provides guidance on fault locations. Lastly, by conducting a case analysis of the data extracted from a specific region's distribution network, experimental results demonstrate the method's capability to accurately match target data. Moreover, the visual knowledge graph constructed has proven effective in enhancing regulators' decision-making efficiency. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Defects

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