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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1711-1720 订阅

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Semantic Segmentation and image Quality Assessment of Anterior Segment images for Smartphones 14

Semantic Segmentation and Image Quality Assessment of Anteri...

引用

14th International conference on Graphics and image processing (ICGIP)

作者： Chen, Yuanhao Huang, Kun Fan, Wen Chen, Qiang Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Nanjing Med Univ Dept Ophthalmol Affiliated Hosp 1 Nanjing 210029 Peoples R China

ISBN: (纸本)9781510666313;9781510666320

Eye diseases have always been a threat to public health worldwide. Many people suffer from various eye diseases, but there are not enough skilled ophthalmologists to meet the demand for medical care. Thus, finding a method to perform ophthalmic examinations automatically and conveniently is necessary. Although many well-designed ophthalmic diagnosis systems have been proposed to diagnose ophthalmic disorders using artificial intelligence algorithms, they tend to depend on high-quality anterior segment images to perform appropriately. In order to capture high-quality anterior segment images simply with a smartphone, we proposed a system including a semantic segmentation model and an image quality assessment method for anterior segment images. Our proposed segmentation model, namely the multi-task anterior segment image semantic segmentation (MT-ASISS) model, has a designed multi-task learning network structure and achieves an accuracy of 92.63% in Dice and a processing speed of 138ms per frame on smartphones. Our anterior segment image quality assessment method, namely Mixed-Parameters Quality Assessment (MPQA) method, has an accuracy of 92.6% in mean average precision (mAP). The system can help reduce the demand for professional image collecting equipment, share the burden of choosing satisfactory images manually and improve the efficiency of acquiring anterior segment images.

关键词： Anterior Segment image Segmentation Multi-Task Learning image Quality Assessment Lightweight Network

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Presenting a Method for Gluing Error Detection Using image processing

Presenting a Method for Gluing Error Detection Using Image P...

引用

International conference of Signal processing and Intelligent systems (ICSPIS)

作者： Moein Shishebor Vali Derhami Masoud Khouri Computer Engineering Department Yazd University Yazd Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

The application of artificial intelligence and image processing for automatic fault detection in industrial settings holds substantial significance. However, image processing systems encounter challenges in achieving accurate automatic detection, including variable lighting conditions, low image quality, and the complexity of observed objects. This study specifically addresses the detection of gluing errors in automotive parts. An improved template matching algorithm is employed to identify boundary errors. The proposed method incorporates SIFT (Scale-Invariant Feature Transform) for feature extraction, allowing for the identification of key local features in images regardless of changes in scale, rotation, or lighting conditions. To address variations in scale and alignment, affine transformations are employed to standardize the images, ensuring consistent dimensions and orientations. After this preprocessing step, brute-force matching techniques are utilized to accurately match the extracted features from the modified images with a target reference, refining the output and enhancing the detection accuracy of improperly glued areas. Results from executing the algorithm on input images demonstrate the high performance of the proposed method in gluing error detection.

关键词： Accuracy Refining Signal processing algorithms Lighting Transforms Signal processing Feature extraction Reliability Standards Automotive engineering

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A Fast Phase-Preserving Imaging Algorithm based on Segmented Pulse Compression for GNSS-InSAR System

A Fast Phase-Preserving Imaging Algorithm based on Segmented...

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2022 IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2022

作者： Zhou, Jingtian Liu, Feifeng Wang, Zhanze Xu, Zhixiang Zhang, Shuyao Zeng, Zhangfan Beijing Institute of Technology School of Information and Electronics Beijing100081 China Key Laboratory of Electronic and Information Technology In Satellite Navigation Beijing Institute of Technology Ministry of Education Beijing100081 China Hubei University School of Computer Science and Information Engineering Wuhan China

ISBN: (数字)9781665469722

ISBN: (纸本)9781665469722

In GNSS-based InSAR systems, in order to obtain high azimuth resolution, more than 600s data need to be acquired, which results in large data amount. In this paper, a fast phase-preserving imaging algorithm based on segmented pulse compression is proposed to reduce calculation of GNSS-InSAR system. First, a segment echo signal processing model is established based on bistatic configuration. Then, calculation complex of pulse compression is reduced through data segments along range direction. Finally, pulse compression result for imaging is obtained by summing the segment result and image result is obtained by back projection algorithm. Raw data results suggest the effectiveness of the fast phase-preserving imaging algorithm. © 2022 IEEE.

关键词： image segmentation

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A Survey on Music Genre Classification Using Multimodal Information processing and Retrieval

A Survey on Music Genre Classification Using Multimodal Info...

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2023 IEEE International conference on Recent Trends in Electronics and Communication, ICRTEC 2023

作者： Abhyankar, Shreya G. Bharadwaj, Shashank S. Rani, G. Shobha Karigiri, Pruthvi G. Srikanth, Sahana Gurugopinath, Sanjeev Pes University Department of Electronics and Communication Engineering Bengaluru560085 India

ISBN: (纸本)9798350396195

Musical descriptors play a very crucial role in automatic music genre classification. With the increase in digitized music, query and retrieval of musical data has always been subjective and controversial. More often, a musical clip is often spread across the genres, making the classification process very challenging. This paper presents a contemporary survey of the recent advances made in the classification of music genre using different machine learning and deep learning algorithms. Specifically, it looks into the recent developments in the area of genre classification based on audio based descriptors, image based descriptors, and other descriptors which include album cover art, brain signals, lyrics and musical instrument digital interface data. © 2023 IEEE.

关键词： Music

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Enabling Smart Road Safety: Constrained Device Utilization for Accident Prevention in Traffic Congestion

Enabling Smart Road Safety: Constrained Device Utilization f...

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Multimedia University Engineering conference (MECON)

作者： Amna Khatoon Asad Ullah Ishfaq Ahmed Ayesha Aslam Mardeni Bin Roslee Shabeer Ahmad School of Information Engineering Chang'an University Xi'an China School of Information Engineering Xi’ an Eurasia University Xi’ an Shaanxi China School of Marine Science Northwestern Polytechnical University Xi’ an China Faculty of Engineering Multimedia Multimedia University University Cyberjaya Malaysia School of Electronic Engineering Beijing University of posts and Telecommunications

ISBN: (数字)9798331530747

ISBN: (纸本)9798331530754

Computer Vision (CV) analysis on constrained devices plays a crucial role in enhancing road safety and mitigating traffic congestion. This study demonstrates the integration of image segmentation and classification techniques into traffic monitoring systems on devices with limited computational power, such as dashboard cameras, smartphones, and IoT sensors. These technologies enable efficient real-time data collection and processing in densely populated urban areas, facilitating the immediate detection of irregular traffic patterns and potential collision points by leveraging edge computing and optimized algorithms. By implementing these analyses directly on constrained devices strategically placed within traffic systems, our approach provides timely responses to dynamic conditions, significantly reducing accident risks through early warnings and adaptive route suggestions. Our research also shows that CV systems can monitor driver behavior, detect attention or weariness, and give timely signals to improve driver response times. The data supports improving traffic signal timings, which might reduce vehicle idle times and improve traffic flow, thus increasing road safety and reducing environmental impact. The study provides a comprehensive methodology for implementing high-quality CV technologies in resource-constrained environments, highlighting advancements in edge computing and energy-efficient hardware. These advances underscore the importance of CV in building sustainable, resilient transport networks and its versatility in municipal and vehicle traffic management, enabling proactive measures to reduce accidents.

关键词： Computer vision Data collection Road safety Real-time systems Vehicle dynamics Traffic congestion Monitoring Edge computing Accidents Vehicles

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Enhancing Scene Simplification and Optimization for Retinal Prosthesis Platform 5

Enhancing Scene Simplification and Optimization for Retinal ...

引用

5th International IEEE Novel Intelligent and Leading Emerging Sciences conference, NILES 2023

作者： Holiel, Heidi Ahmed Fawzi, Sahar Ali Al-Atabany, Walid Nile University Medical Imaging and Image Processing Research Group Center for Informatics Science Sheikh Zayed City Egypt Cairo University Systems and Biomedical Engineering Department Giza Egypt Helwan University Biomedical Engineering Department Helwan Egypt

ISBN: (纸本)9798350381030

Retinal prostheses are designed to aid individuals with retinal degenerative conditions such as Retinitis Pigmentosa (RP) and Age-related Macular Degeneration (AMD). These prostheses seek to restore vision and improve the perceived scene by stimulating degenerated retinal cells using retinal stimulating electrodes. While these electrodes allow more efficient interaction with the surroundings, they offer limited *** paper presents an innovative approach to revolutionize the visual perception of retinal prosthesis users. The key idea behind the proposed approach is to fuse information extracted from advanced Meanshift and saliency detection algorithms. This proposed approach aims to improve the perceived scene for users, leading to enhanced visual perception and increased efficiency of retinal prostheses. © 2023 IEEE.

关键词： Computer vision

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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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Multiscale Feature Extraction-Based Remote Sensing image Matching Network

Multiscale Feature Extraction-Based Remote Sensing Image Mat...

引用

Intelligent Communications and Computing (ICC), International conference on

作者： Haoyang Tang Ziqiao Zhao Yujie Gao Zuqiang Bai Dongfang Yang School of Automation Xi'an University of Posts & Telecommunications Xi'an China Unit 96941 of the People's Liberation Army Xi'an China Computer Image Processing Xi'an High tech research Institute Xi'an China

ISBN: (数字)9798331530365

ISBN: (纸本)9798331530372

This paper addresses the challenges of feature extraction and real-time matching in heterogeneous remote sensing images caused by the differing imaging principles of various sensors. We propose a remote sensing image matching network based on Multiscale feature extraction. The network establishes a matching framework for aerial and satellite images, employing a Multiscale Feature Enhancement Module (MSFEM) to extract scale-invariant features and perform salient feature point detection. This approach effectively captures local features and contextual information from heterogeneous images. Subsequently, the feature points and descriptors are decoded and fed into the SuperGlue feature matching network for alignment. Experimental results demonstrate that our proposed matching method improves matching accuracy by 13.79% compared to D2Net and by 2.86% compared to LoFTR, validating the network's potential for feature extraction and matching tasks in heterogeneous remote sensing imagery.

关键词： image sensors Accuracy image matching Feature extraction Real-time systems Sensors Satellite images Remote sensing

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Application of image Security Transmission Encryption Algorithm Based on Chaos Algorithm in Networking systems of Artificial Intelligence

Application of Image Security Transmission Encryption Algori...

引用

Networking systems of AI (INSAI), International conference on

作者： Hongmei Tian Zhijie Yuan Jiao Zhou Rui He Xi’an University of Technology Xi’an China China Coal Technology & Engineering Group Xian Research Institute Xi’an China

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

This study focuses on enhancing the security of image transmission in Networking systems of Artificial Intelligence (NSAI) by implementing an advanced encryption algorithm (AEA) based on chaotic algorithms. The research begins by exploring the fundamental concepts, structures, and practical applications of NSAI. It then introduces the development of the AEA, specifically designed to safeguard image data during transmission. The methodology involves integrating a chaotic algorithm into the encryption and decryption processes, thereby improving the confidentiality and integrity of the images. Additionally, an encryption protocol consistent with NSAI specifications is developed to further enhance the security of transmitted images. Through simulations, the AEA demonstrates exceptional performance, with minimal errors, high accuracy, and outstanding efficiency in encryption tasks. Overall, this study concludes that the AEA represents a significant advancement in securing image transmission within NSAI, providing robust protection for future applications.

关键词： Training Protocols image communication Simulation Reliability engineering Encryption Complexity theory Security Artificial intelligence Protection

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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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