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

共 14919 条记录，以下是251-260 订阅

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A study on advancing Edge Detection and Geometric Analysis through image processing 24

A study on advancing Edge Detection and Geometric Analysis t...

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8th International conference on Graphics and Signal processing

作者： Hamza, Syed Amir Jesser, Alexander Heilbronn Univ Heilbronn Germany Heilbronn Univ Professorship Embedded Syst & Commun Engn Heilbronn Germany

ISBN: (纸本)9798400717024

In the realm of Printed Circuit Board (PCB) manufacturing, the alignment process is pivotal for ensuring the functional integrity of the final product. Traditional image measurement techniques, while foundational, often fall short of achieving the high degree of accuracy and precision necessary for today's complex PCB designs. This research presents a novel approach that significantly enhances measurement accuracy through the application of optimized image processing techniques. By leveraging advanced algorithms within the OpenCV library, we introduce a methodology that accurately transforms pixel coordinates into real-world measurements, crucial for precise PCB alignment. Our technique employs edge detection algorithms such as Canny, Sobel, and Prewit filters, combined with a machine learning model that adapts to variations in real-time imaging conditions. The study delineates the development of a user-friendly graphical interface that streamlines the measurement process, making it accessible for practical industrial application. Results from experimental validations indicate a substantial improvement in measurement precision, with a demonstrable reduction in alignment errors compared to conventional methods. This leap forward not only promises to elevate the standards of PCB manufacturing but also opens avenues for similar advancements in other domains where image measurement is essential. The implications of this work are far-reaching, with the potential to significantly boost the efficiency and reliability of electronic manufacturing processes globally.

关键词： image measurement techniques microns edge detection algorithms real-world coordinate transformation machine learning in imaging graphical user interface (GUI) Printed Circuit Board (PCB)

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High-Fidelity Audio Compression with Improved RVQGAN 37

High-Fidelity Audio Compression with Improved RVQGAN

引用

37th conference on Neural Information processing systems (NeurIPS)

作者： Kumar, Rithesh Seetharaman, Prem Luebs, Alejandro Kumar, Ishaan Kumar, Kundan Descript Inc San Francisco CA 94118 USA

ISBN: (纸本)9781713899921

Language models have been successfully used to model natural signals, such as images, speech, and music. A key component of these models is a high quality neural compression model that can compress high-dimensional natural signals into lower dimensional discrete tokens. To that end, we introduce a high-fidelity universal neural audio compression algorithm that achieves 90x compression of 44.1 KHz audio into tokens at just 8kbps bandwidth. We achieve this by combining advances in high-fidelity audio generation with better vector quantization techniques from the image domain, along with improved adversarial and reconstruction losses. We compress all domains (speech, environment, music, etc.) with a single universal model, making it widely applicable to generative modeling of all audio. We compare with competing audio compression algorithms, and find our method outperforms them significantly. We provide thorough ablations for every design choice, as well as open-source code and trained model weights. We hope our work can lay the foundation for the next generation of high-fidelity audio modeling.

关键词： image enhancement

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Empirical Analysis of Denoising algorithms for CCTV Face images

Empirical Analysis of Denoising Algorithms for CCTV Face Ima...

引用

International conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Bhagwat, Sumedha Rahul Ragha, Leena Ramrao Adik Inst Technol Dept Comp Engn Navi Mumbai India

ISBN: (纸本)9789819984756;9789819984763

image denoising is a critical task in image processing that involves the removal of noise or unwanted distortions from an image while preserving its essential features. Most of the commonly captured pictures are obtained using mobile cameras or CCTV surveillance cameras producing video footage of the activities of people who are stationary or in motion. There is a need to restore such captured footage from noise so that it can become evidence for different criminal cases. Denoising face images captured using CCTV is a challenging task due to fine details being affected by noise. In this paper, we evaluate three image denoising techniques Block-Matching and 3D (BM3D), k-Means Singular Value Decomposition (KSVD), and Weighted Nuclear Norm Minimization (WNNM). The performance of these methods is analyzed using Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Visual Information Fidelity (VIF). It is observed that the overall performance of KSVD is better for a Gaussian and Salt and Pepper noise.

关键词： image denoising BM3D WNNM KSVD Video surveillance images CCTV images

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A wandb based corrosion detection of metals through live video analytics 2

A wandb based corrosion detection of metals through live vid...

引用

2nd IEEE International conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： John Deva Prasanna, D.S. Sai Neeraj, Kuruvella Jaya Krishna, G. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of DSBS India SRM Institute of Science and Technology Department of DSBS India

ISBN: (纸本)9798350368109

Detection of corrosion in moving objects like ships is challenging due to the dynamic nature of the input image. Existing machine learning techniques are suitable for static images and the algorithms suffer in performance when is a live video. In this paper, image processing for detecting corrosion using YOLOv8 which more suitable for processing live videos as speed and accuracy is better. This makes YOLO v8 for corrosion detection in live videos. In addition, Weights and Biases (W&B). is used in the algorithm as it is pivotal in establishing the connections between neurons and biases helps in circumventing flexible inputs. By combining YOLOv8's and W&B approach the accuracy and efficiency of corrosion detection systems is improved. This can ultimately assist in better maintenance and preservation of essential infrastructure resources. © 2024 IEEE.

关键词： Video signal processing

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Nearly Tight Bounds For Differentially Private Multiway Cut 37

Nearly Tight Bounds For Differentially Private Multiway Cut

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37th conference on Neural Information processing systems (NeurIPS)

作者： Dalirrooyfard, Mina Mitrovic, Slobodan Nevmyvaka, Yuriy Morgan Stanley Machine Learning Res New York NY 10036 USA Univ Calif Davis Dept Comp Sci Davis CA USA

ISBN: (纸本)9781713899921

Finding min s-t cuts in graphs is a basic algorithmic tool, with applications in image segmentation, community detection, reinforcement learning, and data clustering. In this problem, we are given two nodes as terminals and the goal is to remove the smallest number of edges from the graph so that these two terminals are disconnected. We study the complexity of differential privacy for the min s-t cut problem and show nearly tight lower and upper bounds where we achieve privacy at no cost for running time efficiency. We also develop a differentially private algorithm for the multiway k-cut problem, in which we are given k nodes as terminals that we would like to disconnect. As a function of k, we obtain privacy guarantees that are exponentially more efficient than applying the advanced composition theorem to known algorithms for multiway k-cut. Finally, we empirically evaluate the approximation of our differentially private min s-t cut algorithm and show that it almost matches the quality of the output of non-private ones.

关键词： image segmentation

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Research on the detection of dislodged fault in photovoltaic modules based on line scanning

Research on the detection of dislodged fault in photovoltaic...

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2024 International conference on algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Zhao, Desheng Gao, Dedong Su, Weihong Zhang, Shuai Meng, Xiangchun School of Mechanical Engineering Qinghai University Qinghai Xining810016 China

ISBN: (纸本)9781510685840

This study addresses the detection of dislodged fault in photovoltaic (PV) power stations by proposing a visible light image processing method utilizing line scanning. The method employs HSV (Hue-Saturation-Value) color space conversion and morphological processing to preprocess images, which achieves precise segmentation and shape simplification of PV module areas through flood fill and polygon contour fitting techniques. For fault detection, the study introduces a line-scanning-based algorithm. By setting appropriate thresholds and continuous occurrence parameters, the algorithm effectively identifies and marks dislodged areas of PV modules in the original image. Experimental results show that this method significantly improves detection accuracy and computational efficiency, providing technical support and solutions for the safe operation and maintenance of PV systems. © 2024 SPIE.

关键词： image segmentation

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Heart Diseases Classification by ECG Trace images Using Deep Learning 4th

Heart Diseases Classification by ECG Trace Images Using Deep...

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4th International conference on Digital Technologies and Applications (ICDTA)

作者： Ali, Sajad M. Daway, Hazim G. Kadhim, Ahlam M. Mustansiriyah Univ Coll Sci Dept Phys Baghdad Iraq

ISBN: (纸本)9783031686498;9783031686504

Recently, many deep learning algorithms have emerged as advanced techniques in the medical field for diagnosing diseases, including heart disease. In this study, an approach was followed that is based on electrocardiogram (ECG) images to detect different heart diseases. Pre-processing was performed for the data images using morphology technology to remove lines from the background ECG paper image to obtain an image containing only the changes of electrical activity for the potion's heart. The pre-processed data images are trained at a rate of 80% of each class data image in the training stage and 20% of each class image used for the testing stage in the efficiency evaluating stage of each model. Seven classification models have been proposed in binary classification. Models 1-7 have been trained to classify the natural ECG case (Nrm) with the other diseases. Models' efficiency is calculated using four measures, where the accuracy reaches 100%, the precision reaches 100%, the specificity is 100%, and the f1-score is 100%. For models 6 and 7, the results of the accuracy reached (88.1366 and 91.0978)%, precision (80.7443 and 91.0834)%, specificity (79.1734 and 88.8665)%, and f1-score (79.4476 and 89.8999) %. The proposed diagnostic system is fast, accessible, more sensitive, and harmless. It is also more cost-effective than any other diagnostic method.

关键词： CNN Deep Learning ECG images pre-processing data

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algorithms of Signal processing in the System for High-Speed Corrugation Monitoring of Rails 6

Algorithms of Signal Processing in the System for High-Speed...

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6th International Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2024

作者： Koluchkin, Vasiliy Ya. Marenov, Nikita E. Bauman Moscow State Technical University Moscow Russia

ISBN: (纸本)9798350382891

In real operating conditions of the control systems based on the parallax method with structured laser illumination, due to background solar illumination, nonlinear distortions of signals, known as the blooming effect, may occur. Such distortions lead to a decrease in the accuracy of the systems of operational control of straightness of railroad rails based on the above method. The purpose of the research presented in this paper is to evaluate the effectiveness of the subpixel peak position refinement algorithm under normal conditions and under background illumination conditions in an operational high-precision inspection system. As a result of numerical experiments of the rail surface straightness control system model, the effectiveness of subpixel refinement algorithms is investigated in the presence of internal noise of the image registration device, as well as in the presence of distortions caused by the blooming effect. © 2024 IEEE.

关键词： Geometrical optics

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Vision Transformer Based Hash Coding for Efficient image and Audio Retrieval with Global and Local Equilibrium Distance Constraints

Vision Transformer Based Hash Coding for Efficient Image and...

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2024 IEEE International conference on systems, Man, and Cybernetics, SMC 2024

作者： Liu, Ye Pan, Yan Yin, Jian School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Lizhi Inc. Artificial Intelligence and Big Data Department Guangzhou Beijing China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (纸本)9781665410205

With the application of efficient retrieval in information systems and retrieval augmented generation with vector database for large language models, hash coding algorithms have made progress in recent years. The rise of transformer technology in the field of deep learning has brought the possibility to further improve the effect of hash coding algorithms. We introduce the vision transformer framework to both images and audios, and propose a novel approach for the tasks of multi-label image retrieval and audio event retrieval. In the proposed hash coding model, global and local equilibrium distance constraints are integrated, so that the hash codes for images can be better obtained through the global hash centers and local similar samples. In order to realize end-to-end training and hash code generation for audios, we adopt the adapter of mel spectrogram, thus the proposed approach can be simply converted and applied to audio hash coding. Comparative experiments verify that better results can be achieved on multiple image and audio datasets. © 2024 IEEE.

关键词： image coding

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Recognition of Lawn Weeds based on Retinex Algorithm with Local Density Fusion 15

Recognition of Lawn Weeds based on Retinex Algorithm with Lo...

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15th International conference on Signal processing systems, ICSPS 2023

作者： Hua, Chunjian Li, Xiuqin Zhang, Airong School of Mechanical Engineering Jiangnan University Jiangsu China Wuxi Pioneer Intelligent Equipment Company China

ISBN: (纸本)9781510675056

In order to solve the problem of difficulty in segmenting the foreground of lawn weed images due to the similarity between the foreground and background grayscale, this paper proposes a Retinex enhancement algorithm based on local density fusion. Firstly, image preprocessing is completed based on local variance to realize prominent image foreground and smooth cluttered background;Secondly, using multi threshold segmentation and operational differentiation, pixels are divided into three categories: foreground, background, and pixels to be subdivided. Local density is used to extract spatial information of pixels to be subdivided;Finally, the sigmoid function is used to optimize the reflection component grayscale transformation coefficient, achieving the fusion of local density information and obtaining the final image. The results show that the algorithm proposed in this paper expands the gray difference between weeds and lawn grass while suppressing background noise;Compared with other enhancement algorithms, the peak signal-to-noise ratio, average running time, and segmentation accuracy have all been improved greatly. At the same time, through image analysis, it has been found that the algorithm results in occasional problems of uneven brightness and over segmentation of fine leaf weeds in the image. In response to these two problems, this article finally proposes two corresponding optimization measures, namely global brightness adjustment based on a new adaptive gamma function and Retinex reflection component saturation adjustment based on AHE. © 2024 SPIE.

关键词： image enhancement

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