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

共 3294 条记录，以下是41-50 订阅

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Context-Aware Automated System for image Caption to ASL Translation for Improved Accessibility in Media applications

Context-Aware Automated System for Image Caption to ASL Tran...

引用

2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Aluko, Oluwafolahanmi Menon, vineetha The University of Alabama in Huntsville Dept. of Computer Science HuntsvilleAL United States

ISBN: (纸本)9798331504847

With the emergence of Artificial Intelligence (AI) and the rapid growth of Generative AI, significant progress has been made in image captioning, delivering remarkably precise captions from static images and, more recently, videos [1]. Alt-Text has significantly progressed due to initiatives aimed at improving the context and sentiment recorded during automated captioning. This paper seeks to elaborate on a portion concerning Natural Language processing (NLP) and its significance in enhancing accessibility and context-awareness within the framework of the American Sign Language (ASL) application. This work presents a case study on significance of providing complete context regarding a caption or image to the end user, encompassing emotions and conscious attributes present within the phrase. This paper extensively elaborates on the accessibility functions presently involved in image-captioning. Is user-dependent (requiring users to supply a caption for the image) or standard machine-created alt-text sufficient for complete accessibility? Might incorporating a sign language, such as American Sign Language (ASL), be essential in providing users with more information than what the text of a caption communicates? The key question is: how can we enhance this image-caption translation to be more context-sensitive to convey the subtleties that could be crucial for user comprehension? The aim is to create a world where all voices resonate and every person feels accepted, and this paper represents an initial move towards an AI revolution that might be crucial in realizing that vision. © 2025 IEEE.

关键词： Computer aided instruction

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18th International conference on Wireless Artificial Intelligent Computing Systems and applications, WASA 2024

18th International Conference on Wireless Artificial Intelli...

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18th International conference on Wireless Artificial Intelligent Computing Systems and applications, WASA 2024

ISBN: (纸本)9783031714696

The proceedings contain 108 papers. The special focus in this conference is on Wireless Artificial Intelligent Computing Systems and applications. The topics include: Profinder: Towards Professionals Recognition on Mobile Devices for Users with Cognitive Decline;truthful Double Auction-Based Resource Allocation Mechanisms for Latency-Sensitive applications in Edge Clouds;V2ICooper: Toward Vehicle-to-Infrastructure Cooperative Perception with Spatiotemporal Asynchronous Fusion;recommendation-Aware Collaborative Edge Caching Strategy in the Internet of Vehicles;towards Robust Internet of Vehicles Security: An Edge Node-Based machine Learning Framework for Attack Classification;device-Edge-Cloud Collaborative video Stream processing and Scheduling Strategy Based on Deep Reinforcement Learning;consistent Low-Latency Scheduling for Microsecond-Scale Tasks in Data Centers;multi-scale Data Reconstruction Based Policy Optimization Algorithm for Skill Learning;Joint Optimization Design of Intelligence Reflecting Surface Assisted MU-MISO System Based on Deep Reinforcement Learning;Secure Motion Verification for High Altitude Platforms with a Hybrid AOA-TDOA-FDOA Scheme;variational Autoencoder Based Automatic Clustering for Multivariate Time Series Anomaly Detection;deep Reinforcement Learning Based Economic Dispatch with Cost Constraint in Cyber Physical Energy System;inceptionNeXt Network with Relative Position Information for Microexpression Recognition;adMarks: image Steganography Based on Adversarial Perturbation;a Review on Binary Code Analysis Datasets;low-Cost Robot Path Planning Mechanism for Escaping from Dead Ends;towards Communication-Efficient Collaborative Perception: Harnessing Channel-Spatial Attention and Knowledge Distillation;improving Anomaly Scene Recognition with Large vision-Language Models;ReSU-Net: State Space Model for 3D Abdominal Multi-organ Segmentation;autocue : Targeted Textual Adversarial Attacks with Adversarial Prompts;universal Sign Language

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18th International conference on Wireless Artificial Intelligent Computing Systems and applications, WASA 2024

18th International Conference on Wireless Artificial Intelli...

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18th International conference on Wireless Artificial Intelligent Computing Systems and applications, WASA 2024

ISBN: (纸本)9783031714634

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18th International conference on Wireless Artificial Intelligent Computing Systems and applications, WASA 2024

18th International Conference on Wireless Artificial Intelli...

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18th International conference on Wireless Artificial Intelligent Computing Systems and applications, WASA 2024

ISBN: (纸本)9783031714665

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Chaos Theory Based Gravitational Search Algorithm For Medical image Segmentation 27th

Chaos Theory Based Gravitational Search Algorithm For Medica...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Rather, Sajad Ahmad Roy, Partha Pratim Das, Sujit Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India Department of Computer Science and Engineering National Institute of Technology Telangana506004 India

ISBN: (纸本)9783031781032

Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image into distinct regions based on optimal pixel values. However, as the number of threshold levels increases, so does the computational cost for segmentation. To address this challenge, a novel method is proposed namely Chaos theory based Gravitational Search Algorithm (CGSA) for multilevel thresholding. CGSA combines the standard Gravitational Search Algorithm (GSA) for exploration with chaotic maps for exploitation of the complex pixel problem space. In this study, Kapur’s entropy method is utilized to segment sample images into various partitions based on optimal pixel values. The effectiveness of CGSA in real-world scenarios is evaluated using COviD-19 chest CT scan imaging datasets from Kaggle database. The quality, symmetry, and consistency of the segmented output are assessed using metrics like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). Qualitative analysis includes convergence curves, segmented graphs, colormap images, and box plots. Statistical validation is conducted using the signed Wilcoxon rank sum test. Additionally, a comparison is made between CGSA’s performance and that of eight state-of-the-art heuristic algorithms. The findings demonstrate the superior performance of CGSA, evidenced by its reduced computational time and enhanced image quality metrics values. Specifically, CGSA achieved SSIM of 0.81, FSIM of 0.82, and PSNR of 24.27, surpassing the performance of other competitive algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signal to noise ratio

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Road Infrastructure Defect Detection using Yolo8Seg Based Approach

Road Infrastructure Defect Detection using Yolo8Seg Based Ap...

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International image processing, applications and Systems conference (IPAS)

作者： Norah A. AlSubaie Ghayda A. AlMalki Ghada N. AlMutairi Sarah A. AlRumaih Department of Computer Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This research introduces "Jaddah," an innovative AI-based system for the automated detection of road infrastructure defects using advanced computer vision and machine learning techniques. The system addresses the limitations of traditional road inspection methods, which are often slow and prone to human error. Jaddah develops a mobile application that efficiently detects, classifies, and segments road defects at the pixel level. By utilizing a comprehensive dataset of high-resolution images, the model training process is significantly enhanced. The YOLOv8-seg model is implemented to achieve precise defect localization and segmentation, ensuring high accuracy in identifying and categorizing road defects. Performance metrics show an impressive 87% mAP50, demonstrating reliable defect detection. These results contribute to improved infrastructure maintenance, enhanced road safety, and greater operational efficiency.

关键词： Measurement Location awareness Computer vision Urban planning machine learning Inspection Road safety Maintenance Mobile applications Defect detection

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viSTA: A visual and Textual Attention Dataset for Interpreting Multimodal Models

VISTA: A Visual and Textual Attention Dataset for Interpreti...

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IEEE Winter applications and Computer vision Workshops (WACVW)

作者： Harshit Tolga Tasdizen School of Computing University of Utah Salt Lake City UT USA Scientific Computing and Imaging Institute University of Utah Salt Lake City UT USA

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

The recent developments in deep learning (DL) led to the integration of natural language processing (NLP) with computer vision, resulting in powerful integrated vision and Language Models. Despite their remarkable capabilities, these models are frequently regarded as black boxes within the machine learning research community. This raises a critical question: which parts of an image correspond to specific segments of text, and how can we decipher these associations? Understanding these connections is essential for enhancing model transparency, interpretability, and trustworthiness. To answer this question, we present an image-text aligned human visual attention dataset (viSTA) 1 1 The data is available at https://***/h-pal/Data-for-viSTA that maps specific associations between image regions and corresponding text segments. We then compare the internal heatmaps generated by VL models with this dataset, allowing us to analyze and better understand the model's decision-making process. This approach aims to enhance model transparency, interpretability, and trustworthiness by providing insights into how these models align visual and linguistic information. We conducted a comprehensive study on text-guided visual saliency detection in these VL models. This study aims to understand how different models prioritize and focus on specific visual elements in response to corresponding text segments, providing deeper insights into their internal mechanisms and improving our ability to interpret their outputs.

关键词： Measurement visualization image segmentation Computer vision Analytical models Computational modeling machine vision Natural language processing Reliability Saliency detection

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Computer vision-Based Hand Recognition and Gesture Control for Dino Games

Computer Vision-Based Hand Recognition and Gesture Control f...

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Balumuri Dinesh C V Naveeth Reddy N. Senthamilarasi Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Hand Recognition and Gesture Control For Dino Game Using Computer vision to control the popular Chrome Dino game using hand recognition and gesture control through computer vision techniques. The system leverages real-time image processing and machine learning algorithms to detect and interpret hand gestures, allowing for an intuitive and interactive gaming experience. The proposed method utilizes a webcam to capture live video feed, from which hand landmarks are extracted using a pre-trained neural network model. Various hand gestures, such as swipe and hold, are then mapped to corresponding in-game actions such as jumping and ducking. This gesture-based control mechanism not only enhances user engagement but also demonstrates the potential of computer vision in creating touchless interfaces for gaming applications.

关键词： Hands Computer vision machine learning algorithms Sensitivity Webcams Scalability Lighting Games Streaming media Real-time systems

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Authentication and Verification in Human-Robot Cooperative Robotic Cells using Stereo vision and Gesture Control

Authentication and Verification in Human-Robot Cooperative R...

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International image processing, applications and Systems conference (IPAS)

作者： Gábor Kovács Tamás Szirányi Machine Perception Research Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

The integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators’ movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation.

关键词： Automation Service robots Tracking Human-robot interaction Authentication Systems architecture User experience Real-time systems Stereo vision Safety

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Control of Technical Parameters of Industrial Products Through machine vision 43rd

Control of Technical Parameters of Industrial Products Throu...

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43rd IBIMA conference on Artificial intelligence and machine Learning, IBIMA-AI 2024

作者： Gheorghita, Vlad National University of Science and Technology POLITEHNICA Bucharest Romania

ISBN: (纸本)9783031774928

During the production process, a lot of undesirable or faulty products are also created. Since these products cannot be used or distributed on the market, they must be discarded. Product integrity and quality inspection, which is also a result of production process efficiency and optimization, is one of the most important procedures in modern industrial applications. The quickest and most efficient way to make sure products meet the quality requirements is through an automated verification process. These systems are considered as essential by some applications because replacing them with human employees would necessitate a substantial workforce and related costs. This research provides a mechanical experimental system that will be able to verify fast and control the most important parameters of various products using the artificial intelligence approach and machine vision techniques. The cost of the designed system is low and the structure used is clear. The demonstration of the utility of the created product was carried out on a toothed wheel with many essential technical characteristics, a widely used landmark in the field of manufacturing engineering. image processing techniques are used such as threshold function, calibration, pattern matching, measure. The experimental model designed is easy to implement and can represent a transition towards the application of modern industry 5.0 principles in different processing companies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： machine vision

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