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

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Temporal Deep Learning image processing Model for Natural Gas Leak Detection Using OGI Camera

Temporal Deep Learning Image Processing Model for Natural Ga...

引用

2024 Offshore Technology conference Asia, OTCA 2024

作者： Korjani, Mehdi Conley, David Smith, Mark Clean Connect Inc DenverCO United States

ISBN: (纸本)9781959025030

Natural gas extraction systems often encounter manufacturing defects or develop defects over time, leading to gas leaks. These leaks pose challenges, causing revenue losses and environmental pollution. Detecting gas leaks in the vast array of extraction, transfer, and storage equipment within these systems can be arduous, allowing leaks to persist unnoticed. Additionally, natural gas leaks are not visible to naked eyes, further complicating their detection. We developed a novel deep learning image processing model that utilizes videos captured by a specialized Optical Gas Imaging (OGI) camera to detect natural gas leaks. The temporal deep learning algorithm is designed to identify patterns associated with gas leaks and improve its performance through supervised learning. Our model incorporates algorithms to detect background environments, motion, equipment, and classify gas leaks. Our model employs leak identification algorithms to determine the presence of gas leaks. These algorithms calculate the probability of detected motion indicating a gas leak based on long-term and short-term background subtraction, detected motion, motion duration, equipment location, and telemetry data. To minimize false positives, we have developed image segmentation and object detection models to identify known objects, such as equipment, people, and cars, within the video footage. To train our model we collect more than 10,000 short videos from real fields and include simulated data with known rate controlled gas release in different situations. Data consist of wide range of weather situations including different temperature, wind speed, humidity in sunny, rainy, and snowy fields. We validated our model by conducting experiments involving actual footage from the field. The model achieved a 98% true positive rate, and a 100% true negative rate, correctly refraining from sending an alarm for all non-releases. Additionally, we developed a postprocessing algorithm capable of estimating the

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Automatic summarization of Instagram social network posts by combining semantic and statistical approaches 6

Automatic summarization of Instagram social network posts by...

引用

6th International conference on Pattern Recognition and image Analysis (IPRIA)

作者： Tabanmehr, Zainab Akhtarkavan, Ehsan Khatam Univ Dept Comp Engn Tehran Iran

ISBN: (纸本)9798350314557

The increasing spread of data and text documents such as articles, web pages, books, posts on social networks, etc. on the Internet, creates a fundamental challenge in various fields of text processing under the title of "automatic text summarization". Manual processing and summarization of large volumes of textual data is a very difficult, expensive, time-consuming, and impossible process for human users. Text summarization systems are divided into extractive and abstract categories. In the extractive summarization method, the final summary of a text document is extracted from the important sentences of the same document without any kind of change. In this method, it is possible to repeat a series of sentences repeatedly and interfere with pronouns. But in the abstract summarization method, the final summary of a textual document is extracted from the meaning of the sentences and words of the same document or other documents. Many of the performed works have used extraction methods or abstracts to summarize the collection of web documents, each of which has advantages and disadvantages in the results obtained in terms of similarity or size. In this research, by developing a crawler, extracting the popular text posts from the Instagram social network, suitable pre-processing, and combining the set of extractive and abstract algorithms, the researcher showed how to use each of the abstract algorithms. and used extraction as a supplement to increase the accuracy and accuracy of another algorithm. Observations made on 820 popular text posts on the Instagram social network show the accuracy (80%) of the proposed system.

关键词： text summarization extractive approach abstract approach natural language processing social networks

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An Effective Threshold Based Technique for Retinal image Blood Vessel Segmentation on Fundus image Using Average and Gaussian Filters 2nd

An Effective Threshold Based Technique for Retinal Image Blo...

引用

2nd International conference on Computing, Communication, and Learning, CoCoLe 2023

作者： Ray, Rahul Jena, Sudarson Parida, Priyadarsan SUIIT Sambalpur University Odisha Burla768018 India SUIIT Sambalpur University Odisha Burla768018 India GIET University Odisha Gunupur765022 India

ISBN: (纸本)9783031569975

The fundamental components of automated retinal blood vessel segmentation for eye disease screening systems are segmentation algorithms, retinal blood vessel datasets, classification algorithms, performance measure parameters and noise removing filters etc. This paper is presenting retinal blood vessel segmentation techniques on 2D retinal images captured by fundus camera and examining the performance of different algorithms for a better and accurate result. The goal of this study is to comprehensively investigate and assess retinal blood vessel extraction algorithms using publicly accessible High-Resolution Fundus Diabetic Retinopathy (HRF-DR) and Iowa Normative Set for processing images of the retina-artery vein ratio (INSPIRE-AVR) datasets, with a focus on important performance measurements and characteristics. The effectiveness of the proposed methodology is measured using the performance metrics like Sensitivity, Specificity and Accuracy with 94.27%, 87.86% and 95.61% for HRF-DR dataset and 91.10%, 86.97% and 95.16% for INSPIRE-AVR dataset respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image segmentation

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Comparative Study of image processing Neural Networks using Face Mask Dataset 1

Comparative Study of Image Processing Neural Networks using ...

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1st IEEE International conference on Advances in Electronics, Communication, Computing and Intelligent Information systems, ICAECIS 2023

作者： Sunny Jindal, Rajni Delhi Technological University Department of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350348057

Over the years there has been huge improvements in the performance of image processing algorithms due to increase in computation power of Devices as well as use of Neural Networks. This paper focuses on comparison of size and performance of different image Classification networks used widely. Each one of the machine learning models studied presents a new technique to process the input images that might be useful in different scenarios. Our main focus is to identify the framework with low latency, high accuracy as they can be used practically in surveillance equipment's that need to be fast and accurate. © 2023 IEEE.

关键词： Deep neural networks

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Research on Machine Vision Intelligent Detection System Based on Faster R-CNN 4

Research on Machine Vision Intelligent Detection System Base...

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4th IEEE International conference on Data Science and Computer Application, ICDSCA 2024

作者： Wang, Xueting Zeng, Fanshuo Song, Weizhao Zhang, Zhipeng He, Huitong Zhou, Zheyuan Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350368239

With the rapid development of artificial intelligence technology, visual inspection and image processing algorithms have been continuously improved in accuracy and efficiency, and intelligent inspection systems based on machine vision have been widely used in product quality control, defect detection and process monitoring. In this paper, a machine vision intelligent inspection system based on Faster Region-based Convolutional Network (Faster R-CNN) is proposed, which aims to improve the detection accuracy and efficiency. In this paper, the overall architecture of the system is introduced, including the image acquisition module, the data preprocessing module and the detection module based on Faster R-CNN. In the data preprocessing module, the Deep Convolution Generative Adversarial Networks (DCGAN) algorithm was proposed for image expansion generation, and high-resolution image samples that were basically consistent with the collected image datasets were obtained. Then, the Faster R-CNN model was optimized and adjusted for specific application scenarios, so that the average accuracy (mAP) of the model reached 78.23% and the FPS value reached 11.0. ©2024 IEEE.

关键词： Faster R-CNN image processing image recognition Machine vision intelligent inspection system

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Realtime Scene Enhancement of Low Light image/Video 5

Realtime Scene Enhancement of Low Light Image/Video

引用

5th International conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Zeng, Huixia Xiao, Ping School of Electronic and Information Engineering Guangzhou City University of Technology Guangdong Guangzhou510800 China

ISBN: (纸本)9798350368284

The quality of image and videos plays a vital role in case of real-Time systems. images are captured without sufficient illumination, lead to low dynamic range and high propensity for generating high noise levels. The processing ideas and networks of several mainstream low-light image enhancement neural network algorithms such as DRBN, DSLR, ZeroDCE, EnlightenGAN, as well as the key technologies used are compared and analyzed. In this paper, the EnglightenGAN neural network is used as the core image enhancement algorithm, and NVIDIA AGX Xavier equipment and video stream processing technology are used to achieve the entire process of video acquisition, video frame encoding, image enhancement processing, frame decoding, and video stream display. It can adapt to most low-light and dark scenes which has a good enhancement effect on video streams collected in low-light scenes. The real-Time performance and image enhancement effect of the system achieved good results, with FPS reaching 10, SSIM reaching 0.51753, and PSNR reaching 27.89973. © 2024 IEEE.

关键词： Video streaming

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Research on image Sharpness Evaluation in Microstructure processing

Research on Image Sharpness Evaluation in Microstructure Pro...

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2024 IEEE International conference on Manipulation, Manufacturing and Measurement on the Nanoscale, 3M-NANO 2024

作者： Zhang, Jiajun Wang, Zhe Li, Yiquan Xu, Jinkai Changchun University of Science and Technology Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing College of Mechanical and Electrical Engineering China Changchun University of Science and Technology School of Life Science and Technology China

ISBN: (纸本)9798350362107

To address issues of insufficient sensitivity and weak anti-noise characteristics in existing image sharpness evaluation algorithms, we propose a method combining local variance and gradient analysis. Traditional methods like the Tenengrad function use horizontal and vertical gradient values, while multi-directional gradients can increase noise sensitivity and false detections. Our approach integrates the Tenengrad function with gray variance processing, analyzing the deviation of each pixel's brightness from the average to distinguish content from background. Normalizing these values helps reduce background and impurity pixel influence. Preprocessing with an anisotropic diffusion filter smooths the image while preserving edges. Experimental results show this method offers higher sensitivity and better noise resistance than traditional functions, suitable for autofocus systems. © 2024 IEEE.

关键词： Anisotropy

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IMDL-BenCo: A Comprehensive Benchmark and Codebase for image Manipulation Detection & Localization 38

IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Ma, Xiaochen Zhu, Xuekang Su, Lei Du, Bo Jiang, Zhuohang Tong, Bingkui Lei, Zeyu Yang, Xinyu Pun, Chi-Man Lv, Jiancheng Zhou, Jizhe College of Computer Science Sichuan University China Department of Computer and Information Science University of Macao China Engineering Research Center of Machine Learning and Industry Intelligence MOE China

A comprehensive benchmark is yet to be established in the image Manipulation Detection & Localization (IMDL) field. The absence of such a benchmark leads to insufficient and misleading model evaluations, severely undermining the development of this field. However, the scarcity of open-sourced baseline models and inconsistent training and evaluation protocols make conducting rigorous experiments and faithful comparisons among IMDL models challenging. To address these challenges, we introduce IMDL-BenCo, the first comprehensive IMDL benchmark and modular codebase. IMDL-BenCo: i) decomposes the IMDL framework into standardized, reusable components and revises the model construction pipeline, improving coding efficiency and customization flexibility;ii) fully implements or incorporates training code for state-of-the-art models to establish a comprehensive IMDL benchmark;and iii) conducts deep analysis based on the established benchmark and codebase, offering new insights into IMDL model architecture, dataset characteristics, and evaluation standards. Specifically, IMDL-BenCo includes common processing algorithms, 8 state-of-the-art IMDL models (1 of which are reproduced from scratch), 2 sets of standard training and evaluation protocols, 15 GPU-accelerated evaluation metrics, and 3 kinds of robustness evaluation. This benchmark and codebase represent a significant leap forward in calibrating the current progress in the IMDL field and inspiring future breakthroughs. Code is available at: https://***/scu-zjz/IMDLBenCo. © 2024 Neural information processing systems foundation. All rights reserved.

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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

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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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