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

共 16439 条记录，以下是1721-1730 订阅

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Research on Imaging Detection of Defects in X-ray Oil and Gas Casings Based on MIT-MTT Combined Instrument

Research on Imaging Detection of Defects in X-ray Oil and Ga...

引用

Automation, Robotics and Computer Engineering (ICARCE), International conference on

作者： xifeng Yi Yang Zhang Huaigu Tian Juanli Zhang Feng Zhang Yubao Li Sinopec Shengli Oilfield Company Petroleum Engineering Technology Research Institute Dongying PR China Xi'an Key Laboratory of Human-Machine Integration and Control Technology for Intelligent Rehabilitation Xijing University Xi'an PR China Shaanxi Huachen Petroleum Technology Co. Ltd Xi'an PR China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

The paper aims to study the X-ray oil and gas casing defect imaging detection based on the MIT-MTT combination logging instrument. By reviewing and analyzing relevant domestic and international literature, the paper summarizes the principles, advantages, disadvantages, and applications of existing algorithms. The research, based on experimental data from the MIT -MTT combination logging instrument, indicates that X-ray-based defect detection algorithms, combined with the imaging of the MIT -MTT combination logging instrument, play a significant role in improving the accuracy, efficiency, and automation of oil well casing inspection. With the continuous development of deep learning and defect detection technologies, the field is expected to achieve more breakthroughs in the future.

关键词： Deep learning Automation Instruments Oils image processing Imaging Inspection X-ray imaging Robots Defect detection

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MTCA: A Multimodal Summarization Model Based on Two-Stream Cross Attention 2

MTCA: A Multimodal Summarization Model Based on Two-Stream C...

引用

2nd International conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2022

作者： Lu, Qiduo Ye, xia Zhu, Chenhao Academy of Combat Support Rocket Force University of Engineer Xi'an China

ISBN: (数字)9781665476164

ISBN: (纸本)9781665476164

The combination of vision and natural language patterns has become an important topic in the field of computer vision and natural language processing. With the rapid growth of multimodal information, multimodal summarization has received unprecedented attention. In this paper, we propose MTCA which composed of a pre-trained feature extractor, a text encoder, an image encoder, a two-stream cross attention fusion module and a summary decoder to complete the multimodal summarization task. This framework integrates three sub tasks: extractive summarization, abstractive summarization and image selection. At the same time, we introduce a residual network to alleviate the problem of modality-bias. Experiments show that the model is better than the baseline model and the performance is better than the text summarization method which ignores visual modality. © 2022 IEEE.

关键词： Natural language processing systems

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Untrained neural nets for snapshot compressive imaging: theory and algorithms 24

Untrained neural nets for snapshot compressive imaging: theo...

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Proceedings of the 38th International conference on Neural Information processing systems

作者： Mengyu Zhao xi Chen xin Yuan Shirin Jalali ECE Department Rutgers University New Brunswick School of Engineering Westlake University

ISBN: (纸本)9798331314385

Snapshot compressive imaging (SCI) recovers high-dimensional (3D) data cubes from a single 2D measurement, enabling diverse applications like video and hyper-spectral imaging to go beyond standard techniques in terms of acquisition speed and efficiency. In this paper, we focus on SCI recovery algorithms that employ untrained neural networks (UNNs), such as deep image prior (DIP), to model source structure. Such UNN-based methods are appealing as they have the potential of avoiding the computationally intensive retraining required for different source models and different measurement scenarios. We first develop a theoretical framework for characterizing the performance of such UNN-based methods. The theoretical framework, on the one hand, enables us to optimize the parameters of data-modulating masks, and on the other hand, provides a fundamental connection between the number of data frames that can be recovered from a single measurement to the parameters of the untrained NN. We also employ the recently proposed bagged-deep-image-prior (bagged-DIP) idea to develop SCI Bagged Deep Video Prior (SCI-BDVP) algorithms that address the common challenges faced by standard UNN solutions. Our experimental results show that in video SCI our proposed solution achieves state-of-the-art among UNN methods, and in the case of noisy measurements, it even outperforms supervised solutions. Code is publicly available at https://***/Computational-Imaging-RU/SCI-BDVP.

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Impacts of image Compression on the Detection Quality of a Novel Real-Time image processing Platform

Impacts of Image Compression on the Detection Quality of a N...

引用

International conference on Intelligent Transportation

作者： Jannik Mehrke Georg Volk Yannik Stumpp Oliver Bringmann Anestis Terzis Institute of Communication Technology Ulm University of Applied Sciences Dept. of Computer Engineering University of Tübingen

Cloud-based data processing latency mainly depends on the transmission delay of data to the cloud and the used data processing algorithm. To minimize the transmission delay, it is important to compress the transferred data without reducing the quality of the data. When using data compression algorithms, it is important to validate the impact of these algorithms on the detection quality. This work evaluates the effects of image compression and transmission over wireless interfaces on state of the art neural networks. Therefore, a modern image processing platform for next generation automotive processing architectures, as used in software defined vehicles, is introduced. The impacts of different image encoders as well as data transmission parameters are investigated and discussed.

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Deep Learning Deblurring for Improved Vehicle Number Plate Detection

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

引用

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...

引用

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...

引用

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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3D Hand Pose Estimation Based on External Attention

3D Hand Pose Estimation Based on External Attention

引用

Natural Language processing (ICNLP), International conference on

作者： Shoukun Li xiaoying Pan Beibei Wang Jialong Gao School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

3D hand pose for a single depth image is an important topic in computer vision and human-computer interaction, and although significant progress has been made in this field in recent years, there is still room for improvement in accuracy for some specific application scenarios. To address this problem, a 3D hand pose estimation algorithm based on external attention is proposed. First, the target features are extracted by an hourglass network; then, a HEM (Hard Example Mining) loss based on a mean-variance loss function is proposed, which firstly calculates the L2 loss values of all N keypoints, and then sorts these loss values, and back-propagates the gradient only to the first m loss values. Meanwhile, external attention is introduced to enhance the ability to perceive the global information of the target, and the recognition ability of the features is improved by giving the features different influences through the attention weights. Experimental results show that the algorithm achieves an average distance error of 5.42 mm on the ICVL dataset and 7.11 mm on the MSRA dataset, which further improves the detection performance of the 3D hand pose estimation algorithm.

关键词： Human computer interaction Computer vision Three-dimensional displays Accuracy Target recognition Pose estimation Feature extraction Prediction algorithms Natural language processing

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An Improved Genetic Algorithm for Bi-objective Dynamic Orienteering Problem with Time Windows

An Improved Genetic Algorithm for Bi-objective Dynamic Orien...

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2024 8th International conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Qing Wang xiang Lin Yan Zhang College of Systems Engineering National University of Defense Technology Changsha Hunan Province China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

This paper proposes an Improved Genetic Algorithm combined with Path-Reconstruction operators (IGAPR) to solve the Dynamic Orienteering Problem with Time Windows (DOPTW). In this problem, task nodes appear dynamically, challenging the solution strategies of traditional orienteering problems. We divide the entire planning horizon into multiple static time periods and apply a heuristic genetic algorithm within each period to generate high-quality solutions. Experimental results on modified Solomon and Cordeau benchmark instances demonstrate that our approach outperforms traditional greedy algorithms and conventional genetic algorithms in terms of path rewards and fitness value. Additionally, the paths generated by our method adapt to dynamic changes and exhibit good scalability as the number of nodes increases.

关键词： Greedy algorithms Satellites Heuristic algorithms Scalability Imaging Dynamic scheduling Real-time systems Planning image reconstruction Genetic algorithms

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