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检索条件"任意字段=6th Indian Conference on Computer Vision, Graphics and Image Processing"

共 2009 条记录，以下是31-40 订阅

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Wide Line Segments Detection in Grey-Level images via Guided Scale Space Radon Transform 6

Wide Line Segments Detection in Grey-Level Images via Guided...

引用

6th IEEE International conference on image processing, Applications and Systems, IPAS 2025

作者： Goumeidane, Aicha Baya Ziou, Djemel Nacereddine, Nafaa Yala, Nawal CRTI Research Center in Industrial Technologies Algiers Algeria Université de Sherbrooke Département D'Informatique Québec Canada

ISBN: (纸本)9798331506520

Line segment detection is a fundamental procedure in computer vision, pattern recognition, and image analysis applications. the paper proposes a novel method for wide line segment detection especially endpoints determination based on the Guided Scale Space Radon Transform and Hessian orientations. the method begins by determining the centerlines of wide lines and then exploit the image Hessian orientations around these lines to define binary region support of the line segments and then detect endpoints. the method shows to be robust against blur and noise on synthetic images where, the evaluation of the outcomes reveals the correctness of the detection by achieving low errors. In addition, results on real images are very promising. © 2025 IEEE.

关键词： image segmentation

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Waste Sorting System Using AI: Development of An Embedded Solution for Efficient Waste Management 6

Waste Sorting System Using AI: Development of An Embedded So...

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6th IEEE International conference on image processing, Applications and Systems, IPAS 2025

作者： Remili, Amal Mahdi, Mouna Zouari Elleuch, Jihen Frikha Fakhfakh, Ahmed Sellami, Dorra National Engineering School of Sfax Electrical Engineering Department Sfax Tunisia Sfax Digital Research Center Sfax Tunisia

ISBN: (纸本)9798331506520

Waste is causing a serious environmental problem. Waste management and circular economy offers a best alternative for preserving the environment and protecting nature resources. this paper presents an AI-based system for waste identification and sorting, particularly focusing on plastic, which is the less decomposed waste, using computer vision techniques. the data, used for system validation, comprises publicly available datasets and a locally collected dataset, devoted to plastic classification and detection. Performance metrics demonstrate that YOLOv8 outperforms other deep learning solutions, in real-time waste detection and is well suited for implementation on a Raspberry Pi 4 Model B. the system outperforms also existing methods in terms of efficiency, accuracy, and speed, and is a promising solution for addressing the global waste management challenge. © 2025 IEEE.

关键词： AI models computer vision Waste detection Waste management Waste sorting

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Botanical Bio vision: Advancing Plant Leaf Disease Classifications with Convolutional Neural Network 6

Botanical Bio Vision: Advancing Plant Leaf Disease Classific...

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6th International conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Mythili, N. Madhuvarshini, V. Iphiya, Magdiel D. Dept. of Computer Science and Engineering St. Joseph 's Institute of Technology Old Mamallapuram Road 119 Chennai India

ISBN: (纸本)9798331522667

In recent years, the agricultural sector has encountered major challenges due to the widespread presence of plant leaf diseases, which pose serious risks to crop yields and food security. Advancements in artificial intelligence, particularly through Convolutional Neural Networks (CNNs), offer a promising solution for the precise and efficient classification of plant leaf diseases. this study delves into the development and application of a CNN model built using TensorFIow to classify a variety of plant leaf diseases. By leveraging an extensive dataset containing images of both healthy and diseased leaves, the model is trained to accurately recognize and distinguish between several disease types. this approach not only automates the process of disease identification but also significantly improves diagnostic precision and speed, enabling swift and targeted interventions. Experimental findings reveal the CNN model's effectiveness, achieving notable improvements over traditional image processing methods. this research highlights the potential for incorporating deep learning methods into agricultural practices, paving the way for more intelligent and resilient crop management systems. © 2025 IEEE.

关键词： Bacterial Count Dairy Farming Fat Content Machine learning Milk Quality Predictive Models Protein Levels Sustainability

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Using Hash and Deep Learning for Content-Based image Retrieval 6th

Using Hash and Deep Learning for Content-Based Image Retriev...

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6th International conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Hung, Bui thanh thuy, Tran Xuan Tam, Le Duy Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh City Ho Chi Minh Viet Nam

ISBN: (纸本)9789819780891

Given the exponential growth of global data, research attention has increasingly focused on content-based image retrieval. While deep learning has optimized computer vision tasks, existing image search tools continue to face challenges in processing speed. If these issues are resolved, image search has the potential to fully replace traditional search methods. the drawback of prior methods lies in lengthy feature vectors, resulting in time-consuming searches and inconvenience. To mitigate these issues, we employ feature extraction through convolutional neural networks and subsequently apply binary hashing. Experimental results on the fashion-product-images dataset show that using a convolutional neural network combined with binary hashing in image search achieves a mean average precision (MAP 20) of 0.99 and significantly improves the image retrieval time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A computer vision Approach for Autonomous Cars to Drive Safe at Construction Zone 6

A Computer Vision Approach for Autonomous Cars to Drive Safe...

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6th IEEE International conference on image processing, Applications and Systems, IPAS 2025

作者： Ahammed, Abu Shad Shahi Amran Hossain, Md Obermaisser, Roman University of Siegen Chair of Embedded Systems Siegen Germany

ISBN: (纸本)9798331506520

To build a smarter and safer city, a secure, efficient, and sustainable transportation system is a key requirement. the autonomous driving system (ADS) plays an important role in the development of smart transportation and is considered one of the major challenges facing the automotive sector in recent decades. A car equipped with an autonomous driving system (ADS) comes with various cutting-edge functionalities such as adaptive cruise control, collision alerts, automated parking, and more. A primary area of research within ADAS involves identifying road obstacles in construction zones regardless of the driving environment. this paper presents an innovative and highly accurate road obstacle detection model utilizing computer vision technology that can be activated in construction zones and functions under diverse drift conditions, ultimately contributing to build a safer road transportation system. the model developed with the YOLO framework achieved a mean average precision exceeding 94% and demonstrated an inference time of 1.6 milliseconds on the validation dataset, underscoring the robustness of the methodology applied to mitigate hazards and risks for autonomous vehicles. © 2025 IEEE.

关键词： Autonomous vehicles

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Road Infrastructure Defect Detection using Yolo8Seg Based Approach 6

Road Infrastructure Defect Detection using Yolo8Seg Based Ap...

引用

6th IEEE International conference on image processing, Applications and Systems, IPAS 2025

作者： Alsubaie, Norah A. Almalki, Ghayda A. Almutairi, Ghada N. Alrumaih, Sarah A. Princess Nourah Bint Abdulrahman University Department of Computer Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798331506520

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. © 2025 IEEE.

关键词： image enhancement

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Authentication and Verification in Human-Robot Cooperative Robotic Cells using Stereo vision and Gesture Control 6

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

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6th IEEE International conference on image processing, Applications and Systems, IPAS 2025

作者： Kovacs, Gabor Sziranyi, Tamas HUN-REN Institute for Computer Science and Control Machine Perception Research Laboratory Budapest Hungary

ISBN: (纸本)9798331506520

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. © 2025 IEEE.

关键词： Human robot interaction

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computer vision-Based Weed-Killing Rover 6th

Computer Vision-Based Weed-Killing Rover

引用

6th International conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Adke, Pallavi Junonikar, Atharva Singh, Ayushi Kanase, Abhimanyu Nair, Prajit Varghese, Anshu Deshmukh, Aditya Pimpri Chinchwad College of Engineering & Research Ravet India

ISBN: (纸本)9789819783281

India's primary industry, agriculture, accounts for the majority of the country's GDP. Weed growth is one of the primary factors that lowers agricultural productivity. In the field, the combination of machine vision and the image processing is showed to be a reliable way for doing point to point real-time detection of weed and for other crop detection, providing with the accurate data to manage the weed tailored to individual sites. the developments in information and electronic technologies are to blame for this. Manual herbicide spraying is one of the most common ways to keep weeds under control. However, this strategy has a lot of negative effects on both agricultural and consumer health. this report summarized the advances in weed detection using open-CV and image detection methods. A detailed explanation was given of the five primary weed identification techniques: firstly preprocessing, secondly feature extraction, third would be segmentation, fourth is extraction, and then lastly classification. the primary difficulty with this model is distinguishing crops from weeds, which can occasionally have identical features and appearances. the model was trained with a sizable dataset in order to address this problem. As a result, this technology will now detect the weed and spray the herbicide in the adequate amount in a targeted manner. this research paper will provide a comprehensive study of various deep learning methods that can be useful for automatic detection and various image processing methodologies. It also provides performance metrics, such as accuracy, and precision for various techniques employed. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Machine vision

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Event-based Sensor Fusion and Application on Odometry: A Survey 6

Event-based Sensor Fusion and Application on Odometry: A Sur...

引用

6th IEEE International conference on image processing, Applications and Systems, IPAS 2025

作者： Zhang, Jiaqiang Yu, Xianjia Sier, Ha Zhang, Haizhou Westerlund, Tomi University of Turku TIERS Lab Turku Finland

ISBN: (纸本)9798331506520

Event cameras, inspired by biological vision, are asynchronous sensors that detect changes in brightness. they offer notable advantages in environments characterized by high-speed motion, low lighting, or wide dynamic range. these distinctive properties render event cameras particularly effective for sensor fusion in robotics and computer vision, especially in enhancing traditional visual or LiDAR-inertial odometry. Conventional frame-based cameras suffer from limitations such as motion blur and drift, which can be mitigated by the continuous, low-latency data provided by event cameras. Similarly, LiDAR-based odometry encounters challenges related to the loss of geometric information in environments such as corridors. To address these limitations, unlike the existing event camera-related surveys, this survey presents a comprehensive overview of recent advancements in event-based sensor fusion for odometry applications particularly investigating fusion strategies that incorporate frame-based cameras, inertial measurement units, and LiDAR. the survey critically assesses the contributions of these fusion methods to improving odometry performance in complex environments, while highlighting key applications, and discussing the strengths, limitations, and unresolved challenges. Additionally, it offers insights into potential future research directions to advance event-based sensor fusion for next-generation odometry applications. © 2025 IEEE.

关键词： computer vision

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6th International conference on Security and Privacy in New Computing Environments, SPNCE 2023

6th International Conference on Security and Privacy in New ...

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6th International conference on Security and Privacy in New Computing Environments, SPNCE 2023

ISBN: (纸本)9783031736988

the proceedings contain 23 papers. the special focus in this conference is on Security and Privacy in New Computing Environments. the topics include: Consortium Blockchain Storage Optimization Based on Fountain Codes;an Incentive Mechanism and An Offline Trajectory Publishing Algorithm Considering Sensing Area Coverage Maximization and Participant Privacy Level;Research on Face Recognition System Based on RLWE Homomorphic Encryption;feedback Feed-Forward Iterative Learning Control for Non-affine Nonlinear Discrete-Time Systems with Varying Trail Lengths;open-Closed-Loop Iterative Learning Control Based on Differential Evolution Algorithm for Nonlinear System;overview of Vehicle Edge Computing and Its Security;conFlow: Contrast Network Flow Improving Class-Imbalanced Learning in Network Intrusion Detection;anomaly Detection of Unstable Log Data Based on Contrastive Learning;An Integration-Enhanced ZNN Approach for Chaotic Combination Synchronization with External Disturbances∗;a Lightweight Anomaly Detection Method for Industrial Processes Based on Event Correlation Behavior;a Novel Polar Code-Based Key Encapsulation Mechanism with Non-permutation Equivalent Public Key;two-Stage Multi-lingual Speech Emotion Recognition for Multi-lingual Emotional Speech Synthesis;EncoderMU: Machine Unlearning in Contrastive Learning;NoCrypto: A Web Mining Behavior Detection Method Based on RGB images;image Copy-Move Forgery Detection in the Social Media Based on a Prior Density Clustering and the Point Density;detection of Speech Spoofing Based on Dense Convolutional Network;speech Emotion Recognition Based on Recurrent Neural Networks with Conformer for Emotional Speech Synthesis;Route Privacy-Preserving Authentication Scheme Based on PUF in VANETs;Stable NICE Model-Based Picture Generation for Generative Steganography;computer-Generated image Forensics Based on vision Transformer with Forensic Feature Pre-processing Module;VoIP Steganalysis Using Shallow Multiscale Convolution and

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