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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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Highly Sensitive Real-Time Detection of Surface Texture Changes 17

Highly Sensitive Real-Time Detection of Surface Texture Chan...

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17th IEEE International conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2024

作者： Yarmilko, Andrii Rozlomii, Inna Mysiura, Yuliia Bohdan Khmelnytsky National University of Cherkasy Department of Automated Systems Software Cherkasy Ukraine Cherkasy State Technological University Department of Information Security and Computer Engineering Cherkasy Ukraine Bohdan Khmelnytsky National University of Cherkasy Department of Information Technologies Cherkasy Ukraine

ISBN: (纸本)9798331520564

Real-time surface analysis is becoming increasingly important in various fields, including industry, medicine, and transportation. This article examines the problem of remote monitoring of surface texture in real time. The authors review the methods and techniques used for this task, focusing on high-precision technologies such as computer vision, laser scanning, and image processing techniques. The paper examines the importance of this real-time approach to support accurate and rapid decisions in industrial processes and medical diagnostics. The authors propose a method for detecting texture artifacts with machine learning and analyze the performance and quality of possible variations of its application. Based on the discussion, the authors draw conclusions regarding the effectiveness and prospects of the proposed approach in real-time remote monitoring of surface textures. They emphasize the importance of further research in this direction and the use of innovative technologies to improve the accuracy and speed of the monitoring process. This article will be a valuable source of information for scientists, engineers and professionals in the field of industrial control and automation. © 2024 IEEE.

关键词： Laser applications

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A novel image processing technique based on deep learning for water consumption detection

A novel image processing technique based on deep learning fo...

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IEEE International Instrumentation and Measurement Technology conference (I2MTC)

作者： Carratu, Marco Dello Iacono, Salvatore Di Leo, Giuseppe Gallo, vincenzo Liguori, Consolatina Pietrosanto, Antonio Univ Salerno Dept Ind Engn Via Giovanni Paolo II 132 Fisciano SA Italy

ISBN: (纸本)9781665483605

In recent years, traditional image processing techniques have seen the introduction of novel tools, able to face issues that are not always handy with classical vision algorithms. For example, classical image processing algorithms (measurement, detection of features, and many others) require a controlled environment, like illumination, target positioning, and vibration that can influence the scene for the correct operation. On the other hand, the machine learning approaches enabled image processing techniques also in non-controlled environments. One of these applications can be represented by developing a leak detector at the household level, based on processing pictures of the mechanical water meter dial. The proposed research investigates using a deep learning approach to detect the minimal movement of the water meter needles related to water leakage. In particular, a CNN was trained to correlate successive differences on the water meter dial images taken with an applied calibrated water flow. From this analysis, it is possible to detect the absence of periods with null consumption and thus detect small water losses.

关键词： Deep Learning CNN Water leakage image processing

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A Comparison Between CCTV and Industrial Cameras for Vehicle Attribute Recognition

A Comparison Between CCTV and Industrial Cameras for Vehicle...

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Iranian machine vision and image processing (MviP)

作者： Mohammadreza Asadi Mohammad Yasin Fakhar Seyedeh Sogand Hashemi Safiyeh Rezaei Mohamad Kiani Abari Seyed Alireza Abbaspour AI Research Center Hoopad Vision Company Isfahan Iran

In machine/computer vision, cameras serve a major role in image acquisition. Surveillance scenarios typically rely on Closed-Circuit Television (CCTV) cameras. This study aims to evaluate industrial cameras within a surveillance application, contrasting their performance with that of CCTV cameras. We explore the comparative analysis of CCTV and industrial cameras for vehicle attribute recognition, specifically concentrating on the recognition of vehicle color and model using deep learning techniques. To train and evaluate the models, we have created datasets from images captured by both a CCTV and an industrial camera. Our findings indicate that the industrial camera outperforms the CCTV. However, employing advanced processing algorithms has the potential to minimize the performance gap between these two cameras. Our research represents one of the initial comparative analyses between these camera types, offering valuable guidance in selecting the most suitable camera for specific applications.

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Accurate image Registration Using Evolutionary Algorithms 5th

Accurate Image Registration Using Evolutionary Algorithms

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5th International conference on Data Science, machine Learning and applications, ICDSMLA 2023

作者： Gobi, N. Kumar, Ritesh Kumar, Deepak Singh, Ashutosh Kr. Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Mathematics Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Noida India

ISBN: (纸本)9789819780426

Photograph registration is aligning or more excellent snapshots of the same bodily object or scene;it's widely used in many regions of image processing and pc imaginative and prescient. Evolutionary algorithms are optimization algorithms stimulated with herbal choice aid and used efficiently for image registration. The principal advantage of evolutionary algorithms is their potential to remedy complex optimization tasks without relying on a given set of heuristics. Additionally, they require fewer parameters for the convergence than conventional optimization algorithms with gradient descent and might reap better accuracy with reduced computational time. Evolutionary algorithms can also deal with multiple goals simultaneously, making them suitable for photo registration packages. Genetic algorithms can deal with both simple and complex problems and can be efficiently deployed for registration responsibilities in a disbursed computing place. Evolutionary algorithms have been used efficaciously for many picture registration programs, which include medical imaging, satellite picture registration, and face reputation. Their combination of high accuracy and low computational time makes them a famous desire for registration troubles inside the subject of pc vision. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic algorithms

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Artificial Intelligences-Based Approaches for Generating image Caption 3rd

Artificial Intelligences-Based Approaches for Generating Ima...

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3rd International conference on Data, Engineering, and applications, IDEA 2021

作者： Ingale, S.P. Bamnote, G.R. Prof Ram Meghe Institute of Technology and Research Badnera India

ISBN: (纸本)9789811946868

Most of the visual information presented to humans doesn’t have a description associated with it, but humans can generally understand the visual information without any detailed description accompanying them. But for a machine to perform the same task of interpreting the same images and generate description in a Natural language form is a hard task. For a machine, the process involves two basic human tasks of seeing things and understanding them. In computing, these problems come under the domains of Natural Language processing and Computer vision. Therefore creating descriptions of the image we need a combination of both tasks to be performed. The image description process consists of two parts: first part is to detect the important objects, features attributes, and the relationships between them in an image that comes under the field of Computer vision, and then it should also generate semantically and syntactically accurate phrases, sentences which the area of Natural Language processing. As this task of Describing images with human-readable language is a cognitive task the recent progress in area of machine and Deep learning which are the subset of Artificial Intelligence in which computer algorithms try to copy and adapt the workings of the human brain in processing data and has accelerated the study of this challenging problem of image description. In this paper, the basic techniques and ways to do image captioning are discussed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Performance Evaluation of Median Filter Hardware Chip for image and Signal processing

Performance Evaluation of Median Filter Hardware Chip for Im...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International conference on

作者： Sreesh Gaur Akash Goel Pawan Kumar Pal Amit Kumar Singh Sanger Department of Computer Science KIET Group of Institutions Ghaziabad India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The median filter is a valuable image processing tool that can be used in many applications to advance picture quality, and inferior noise, and acquire data ready for more analysis. The median filter, a non-linear image processing technique, has excessive application in many diverse fields because of its capacity to remove noise while preserving edges. The filter is significant for both simple and complex imageprocessing occupations since it is together easy to comprehend and fast to use. An active method to attain real-time performance is to devise a median filter on a FieldProgrammable Gate Array (FPGA) for image signal processing and machine vision applications. FPGAs are well-suited for photo filtering due to their capability to achieve parallel processing, which is one of their prominent advantages. The research article meets with the design and simulation of the average filter in HDL and synthesis on the FPGA for assessing the performance indices.

关键词： Performance evaluation Filtering machine vision Noise Parallel processing Logic gates Hardware Real-time systems Delays Field programmable gate arrays

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Ghosting Effect Removal for Multi-Frame Super-Resolution on CCTV videos with Moving Objects 2022

Ghosting Effect Removal for Multi-Frame Super-Resolution on ...

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5th International conference on machine vision and applications (ICMVA)

作者： Singian, Jarrett Ethan Tan, Jade Nicole Tierro, Martin Angelo Del Gallego, Neil Patrick Ilao, Joel Antioquia, Arren Matthew De La Salle Univ Manila Philippines De Le Salle Univ Graph Animat Multimedia & Entertainment GAME Lab Manila Philippines De Le Salle Univ Ctr Automat Res CAR Manila Philippines

ISBN: (纸本)9781450395670

With the increased use of closed-circuit television (CCTV) footage for security and surveillance purposes as well as for object or person recognition and efficiency monitoring, high-quality CCTV videos are necessary. In this paper, we propose Corgi Eye, a moving object removal + super-resolution framework for enhancing CCTV footages to remove ghosting artifacts caused by performing multiframe super-resolution (MISR) on moving objects. Our method extends the framework of Eagle Eye, which is an existing MISR framework tailored for mobile devices. Our results demonstrate that the system can completely remove ghosting effects caused by moving objects while performing MISR on CCTV footage. Our proposed method demonstrates competitive performance when compared to Eagle Eye, achieving a 16% increase in terms of PSNR metric. Additionally, our method can produce clear images, on par with deep learning approaches such as ESPCN and SOF-VSR.

关键词： ghosting effect removal image segmentation video segmentation image processing image enhancement super-resolution background subtraction moving object detection

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Drone-Based applications for Tailings Dam Monitoring

Drone-Based Applications for Tailings Dam Monitoring

引用

APCOM 2023 conference: Intelligent Mining: Innovation, vision, and Value

作者： Gomez, Jose A. Sattarvand, Javad Department of Mining & Metallurgical Engineering Mackay School of Earth Sciences & Engineering University of Nevada RenoNV United States

ISBN: (纸本)9780873355216

Failures of tailings dams have been happening lately. Due to the lack of laws on particular design criteria and stability requirements related monitoring during construction and maintenance, they are thought to be more fragile than hydraulic dams. Monitoring the dam is therefore necessary to understand its current condition and guarantee its safety. The physical condition of the dam could be evaluated with the early identification of seepage. Additionally, due to their adaptability and capacity for high-resolution data collecting, UAVs are an excellent choice for efficiently covering the tailings dam site. UAVs may capture high-quality photos when equipped with a high-resolution RGB camera, thermal sensors, or multispectral sensors. When these sensors are paired with image processing and machine learning algorithms, the result is a reliable estimate of the dam condition. © 2023 Society for Mining, Metallurgy & Exploration Inc. All rights reserved.

关键词： Learning algorithms

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ROBUST 3D CELL SEGMENTATION: EXTENDING THE viEW OF CELLPOSE 29

ROBUST 3D CELL SEGMENTATION: EXTENDING THE VIEW OF CELLPOSE

引用

IEEE International conference on image processing (ICIP)

作者： Eschweiler, Dennis Smith, Richard S. Stegmaier, Johannes Rhein Westfal TH Aachen Inst Imaging & Comp Vi Aachen Germany Norwich Res Pk John Innes Ctr Norwich Norfolk England

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Increasing data set sizes of 3D microscopy imaging experiments demand for an automation of segmentation processes to be able to extract meaningful biomedical information. Due to the shortage of annotated 3D image data that can be used for machine learning-based approaches, 3D segmentation approaches are required to be robust and to generalize well to unseen data. The Cellpose approach proposed by Stringer et al. [1] proved to be such a generalist approach for cell instance segmentation tasks. In this paper, we extend the Cellpose approach to improve segmentation accuracy on 3D image data and we further show how the formulation of the gradient maps can be simplified while still being robust and reaching similar segmentation accuracy. The code is publicly available and was integrated into two established open-source applications that allow using the 3D extension of Cellpose without any programming knowledge.

关键词： Cellpose 3D Segmentation Generalist Robust Fluorescence Microscopy

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Feature Extraction from image Sensing (Remote) : image Segmentation 5

Feature Extraction from Image Sensing (Remote) : Image Segme...

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5th IEEE International conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Chauhan, Kaushal Tomar, Harshit Kamal, Kushagra Goel, Pallavi Galgotias College Of Engineering And Technology Department Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350330861

In this research paper, an overview of computer methods for segmenting continuous-tone images into meaningful parts and characterizing these parts with 'features' is presented. image segmentation is an essential pre-processing step in computer vision and image processing softwares. This paper discusses various techniques and algorithms used for image segmentation, including both supervised and unsupervised methods. The performance of these techniques is evaluated on different test images, and future research directions are proposed. Feature extraction from image sensing is a critical step in multimedia processing. This paper explores the process of feature extraction from remotely sensed images. With the increasing use of remote sensing technology, it is crucial to have effective and accurate methods for extracting features from remotely sensed data, which are utilized in various applications such as land-use mapping, environmental proctoring, and in natural-disaster management. This paper discusses several techniques for feature extraction, including pixel-based, object-based, and machine learning-based approaches. Additionally, it highlights the difficulties associated with feature extraction from remote sensing data, such as atmospheric effects, radiometric and geometric distortions, and the presence of noise. The paper concludes by discussing future directions in remote sensing feature extraction, including the integration of deep learning methods and the development of more robust methods has the ability of handling large-scale datasets. With the help of deep learning methods and algorithms, especially CNN's, has tremendously improved the accuracy of image scene classification. In this study, a CNN model which is already trained was utilized to fetch-out deep features from images at various network layers, including the average pooling layer. © 2023 IEEE.

关键词： image segmentation

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