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

共 3293 条记录，以下是1-10 订阅

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MaskChanger: A Transformer-Based Model Tailoring Change Detection with Mask Classification 13

MaskChanger: A Transformer-Based Model Tailoring Change Dete...

引用

13th Iranian/3rd International machine vision and image processing conference (MviP)

作者： Ebrahimzadeh, Mohammad Manzuri, Mohammad Taghi Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Change detection in multi-temporal remote sensing data enables crucial urban analysis and environmental monitoring applications. However, complex factors like illumination variance and occlusion make robust automated change interpretation challenging. We propose MaskChanger - a novel deep learning paradigm tailored for satellite image change detection. Our method adapts the segmentation-specialized Mask2Former architecture by incorporating Siamese networks to extract features separately from bi-temporal images, while retaining the original mask transformer decoder. To our knowledge, this is the first study in which change detection is converted from the existing per-pixel classification approach into a mask classification approach. Evaluated on the LEviR-CD benchmark of over 600 very high-resolution image pairs exhibiting real-world rural and urban changes, MaskChanger achieves F1-Score of 91.96%, outperforming prior transformer-based change detection approaches.

关键词： Change Detection Remote Sensing image Transformer

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On Comparing Multiple Approaches for Classifying Vessels in imagery 6

On Comparing Multiple Approaches for Classifying Vessels in ...

引用

conference on Artificial Intelligence and machine Learning for Multi-Domain Operations applications vi

作者： Warner, John G. Patel, vishal M. US Naval Res Lab 4555 Overlook Ave SW Washington DC 20375 USA Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9781510674219;9781510674202

Deep learning-based approaches, such as Convolutional Neural Nets (CNNs), have shown high performance in classifying contents of images. CNNs, however, have the notable drawbacks of potentially high computing costs, poor explainability, and wide performance variance if the underlying imagery data deviates from the training baseline. As advanced image processing capabilities are matured, the on-board detection of objects in space-based imagery is increasingly proposed. On-board satellite processing applications, which may be resource-limited, can drive the need for simpler models that reduce the necessary computing burden for edge computing applications. This raises the question of how well classic computer vision techniques can compete with more modern approaches. This paper characterizes and compares the performance of multiple computer vision models for the application of distinguishing maritime vessels from typical clutter in commercial electrooptical (EO) satellite imagery. A Support Vector machine (SVM) model using manually curated features is compared to multiple DL-based models spanning a range of model sizes, with the goal of determining whether classical approaches can compete favorably with DL when computational resources are taken into consideration. Differences in performance and processing resources are characterized between the approaches. Findings include that the SVM-based model may approach the accuracy of some CNN-based models for classifying images of clouds in satellite EO imagery for smaller DL-based models. However, even the smallest DL-based models, which take about the same computational resources as the SVM-based model, generally out-perform the SVMbased model. This finding may have implications for the operational use of on-board processing techniques for satellite payloads.

关键词： CNN spacecraft onboard processing Automatic Target Recognition maritime

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An AI Solution for Web Accessibility and images Classification 13

An AI Solution for Web Accessibility and Images Classificati...

引用

13th International conference on image processing Theory Tools and applications

作者： Noreskal, Laura Feuilloley, Guillaume Charbel, Simon-Pierre Sogeti Part Capgemini Issy Les Moulineaux France Sogeti Part Capgemini Rennes France

ISBN: (纸本)9798331541859;9798331541842

This article details the research on web accessibility conducted at Capgemini's SogetiLabs. We introduce our project aimed at developing an automatic accessibility audit tool for website images. Our AI solution for web accessibility focuses on distinguishing between informative and decorative images in line with RGAA (Referenciel Gen eral d'Am elioration de l'Acessibilite) recommendations and then generating alternative text for informative images. To achieve this, we have established a comprehensive processing workflow. Additionally, we present initial experiments in image classification using Convolutional Neural Networks (CNNs) and YOLO's (You Only Look Once) model.

关键词： machine Learning Web Accessibility RGAA Computer vision image processing Classification YOLO CNN

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Spectral-Spatial Anomaly Detection in Hyperspectral imagery Based on Dual-Domain Autoencoders 13

Spectral-Spatial Anomaly Detection in Hyperspectral Imagery ...

引用

13th Iranian/3rd International machine vision and image processing conference (MviP)

作者： Aghili, Mohamad Ebrahim Ghassemian, Hassan Arani, Maryam Imani Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Hyperspectral anomaly detection is crucial for applications like aerial surveillance in remote sensing images. However, robust identification of anomalous pixels remains challenging. A novel spectral-spatial anomaly detection technique called Dual-Domain Autoencoders (DDA) is proposed to address these challenges. First, Nonnegative Matrix Factorization (NMF) is applied to decompose the hyperspectral data into anomaly and background components. Refinement of the designation is then done using intersection masking. Next, a spectral autoencoder is trained on identified background signature pixels and used to reconstruct the image. The reconstruction error highlights spectral anomalies. Furthermore, a spatial autoencoder is trained on principal component patches from likely background areas. Fused reconstruction error from the spectral and spatial autoencoders is finally used to give enhanced anomaly detection. Experiments demonstrate higher AUC for DDA over individual autoencoders and benchmark methods. The integration of matrix factorization and dual-domain, fused autoencoders thus provides superior anomaly identification. Spatial modeling further constrains the background, enabling accurate flagging of unusual local hyperspectral patterns. This study provides the effectiveness of employing autoencoders trained on intelligently sampled hyperspectral pixel signatures and spatial features for improved spectral-spatial anomaly detection.

关键词： hyperspectral image anomaly detection autoencoder matrix factorization spectral-spatial features

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Flexible and Transportable Illumination Control System for machine vision applications 10th

Flexible and Transportable Illumination Control System for M...

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International conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent Systems

作者： Gusic, Muhamed Sokic, Emir Univ Sarajevo Dept Automat Control & Elect Fac Elect Engn Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9783031662706;9783031662713

During the development of machine vision setups for industrial applications, namely automatic visual inspection systems, the type of required lighting and correct illumination position and intensity are often not known in advance. In order to solve a specific machine vision task, an engineer needs to propose and implement a suitable image processing algorithm and design the accompanying lighting system. Testing different illumination concepts, especially "in the field", using off-the-shelf lighting systems is time consuming. The paper presents a custom-made, cost effective, transportable and flexible system which may be used for testing different illumination techniques and simultaneously control multiple lighting modules. In addition to the hardware part, the developed software used for control the system is presented in the paper. The proposed device usefulness is demonstrated by solving a suitable multi-light digital image processing task.

关键词： machine vision visual inspection illumination control system embedded systems digital image processing

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A Review of machine vision applications in Aerospace Manufacturing Quality Inspection 4

A Review of Machine Vision Applications in Aerospace Manufac...

引用

4th International conference on Computer, Control and Robotics (ICCCR)

作者： Deng, Li Liu, Gang Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai Peoples R China

ISBN: (纸本)9798350373141;9798350373158

machine vision is a technology that uses image processing and analysis techniques to acquire and understand image information, enabling the recognition, measurement, and detection of objects. machine vision is widely used in aerospace manufacturing for automated production, quality inspection, and robot guidance. It can improve the efficiency and quality of aerospace manufacturing, reduce labor costs and risks, promote innovation and optimization, adapt to various inspection needs, and realize intelligent, automated, and digital manufacturing processes. This paper introduces the types of vision technologies, overviews the typical applications of machine vision for quality inspection in aerospace manufacturing, (including component surface inspection, drilling quality inspection, assembly quality inspection, and gluing quality inspection), analyzes the advantages, challenges, and development trends of machine vision for quality inspection in aerospace manufacturing, and looks forward to future research directions to support further research and application.

关键词： machine vision Aerospace manufacturing Component surface inspection Drilling quality inspection Assembly quality inspection Gluing quality inspection

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Attention-Based image Caption Generation 5th

Attention-Based Image Caption Generation

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

作者： Manasa, M. Sowmya, D. Reddy, Y. Supriya Sreedevi, Pogula G Pulla Reddy Engn Coll Dept Comp Sci & Engn Data Sci Kurnool 5185007 India G Pulla Reddy Engn Coll Dept Comp Sci & Engn Kurnool 5185007 India Rajeev Gandhi Mem Coll Engn & Technol Dept Comp Sci & Engn Nandyal 518501 India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

The automatic development of meaningful, detailed textual descriptions for supplied images is a difficult task in the fields of computer vision and natural language processing. As a result, an AI-powered image caption generator can be incredibly useful for producing captions. In this study, we present a unique method for creating picture captions utilizing an attention mechanism that concentrates on pertinent areas of the image while it creates captions. On benchmark datasets, our model, which uses deep neural networks to extract picture attributes and produce captions, obtains state-of-the-art results, confirming the effectiveness of the attention mechanism in raising the caliber of the generated captions. We also offer a thorough evaluation of the performance of our approach and talk about potential future directions for enhancing image caption generation.

关键词： image Caption Computer vision Natural Language processing Attention Mechanism Performance

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On-machine Wear Measurement for Milling Cutter Based on machine vision 5

On-machine Wear Measurement for Milling Cutter Based on Mach...

引用

5th International conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM)

作者： Yu, Jiarui Zan, Tao Liu, Weibo Li, Yikun Peng, Junxi Lei, Qichang Beijing Univ Technol Coll Mech & Energy Engn Beijing Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Beijing Univ Technol Coll Beijing Dublin Int Beijing Peoples R China

ISBN: (纸本)9798350363272;9798350363265

As a key factor in the milling process, the wear status of the milling cutter has a significant impact on the machining quality of the workpiece. To detect wear on a milling machine efficiently and precisely, this paper presents the development of a milling machine wear detection system based on machine vision and digital image processing. The system including link mechanisms and industrial camera is designed for auxiliary localization and collection of on-machine images of milling cutter status. The image preprocessing method based on automatic threshold segmentation and Canny edge detection operator is proposed to identify the edge of cutter wear. The Maximum connected domains algorithm is used to screen the wear area of the milling cutter and the amount of wear is obtained based on a calibrated scaling method. Experimental results show that the proposed system is suitable for industrial use due to its rapid detection speed and strong recognition accuracy, which are desirable for engineering applications.

关键词： milling cutter wear detection machine vision image processing auxiliary localization mechanism

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On the impact of learning-based image compression on computer vision tasks 47

On the impact of learning-based image compression on compute...

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conference on applications of Digital image processing XLviI

作者： Akamatsu, Shunsuke Testolina, Michela Upenik, Evgeniy Ebrahimi, Touradj Waseda Univ Adv Multimedia Syst Lab Shillman Hall 4013-14-9 OkuboShinjuku Ku Tokyo 1690072 Japan Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG CH-1015 Lausanne Switzerland

ISBN: (纸本)9781510679344;9781510679351

The image compression field is witnessing a shift in paradigm thanks to the rise of neural network-based models. In this context, the JPEG committee is in the process of standardizing the first learning-based image compression standard, known as JPEG AI. While most of the research to date has focused on image coding for humans, JPEG AI plans to address both human and machine vision by presenting several non-normative decoders addressing multiple image processing and computer vision tasks in addition to standard reconstruction. While the impact of conventional image compression on computer vision tasks has already been addressed, no study has been conducted to assess the impact of learning-based image compression on such tasks. In this paper, the impact of learning-based image compression, including JPEG AI, on computer vision tasks is reviewed and discussed, mainly focusing on the image classification task along with object detection and segmentation. This study reviews the impact of image compression with JPEG AI on various computer vision models. It shows the superiority of JPEG AI over other conventional and learning-based compression models.

关键词： JPEG AI learning-based image compression computer vision image classification object detection

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Are Natural Domain Foundation Models Useful for Medical image Classification?

Are Natural Domain Foundation Models Useful for Medical Imag...

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IEEE/CVF Winter conference on applications of Computer vision (WACV)

作者： Huix, Joana Pales Soderberg, Magnus Ganeshan, Adithya Raju Matsoukas, Christos Haslum, Johan Fredin Smith, Kevin KTH Royal Inst Technol Stockholm Sweden Sci Life Lab Stockholm Sweden AstraZeneca Gothenburg Sweden

ISBN: (纸本)9798350318920;9798350318937

The deep learning field is converging towards the use of general foundation models that can be easily adapted for diverse tasks. While this paradigm shift has become common practice within the field of natural language processing, progress has been slower in computer vision. In this paper we attempt to address this issue by investigating the transferability of various state-of-the-art foundation models to medical image classification tasks. Specifically, we evaluate the performance of five foundation models, namely SAM, SEEM, DINOv2, BLIP, and OPENCLIP across four well-established medical imaging datasets. We explore different training settings to fully harness the potential of these models. Our study shows mixed results. DINOv2 consistently outperforms the standard practice of imageNET pretraining. However, other foundation models failed to consistently beat this established baseline indicating limitations in their transferability to medical image classification tasks.

关键词： Algorithms Algorithms and algorithms applications Biomedical / healthcare / medicine Datasets and evaluations formulations machine learning architectures

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