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检索条件"任意字段=5th International Conference on Robotics and Computer Vision, ICRCV 2023"

共 929 条记录，以下是1-10 订阅

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2023 5th international conference on robotics and computer vision, icrcv 2023

2023 5th International Conference on Robotics and Computer V...

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5th international conference on robotics and computer vision, icrcv 2023

ISBN: (纸本)9798350326369

the proceedings contain 54 papers. the topics discussed include: an improved YOLOv7-tiny-based lightweight network for the identification of fish species;PPC-US-LSF3DOD: a pseudo-point-clouds based unsupervised and scalable late-sensor-fusion method for 3D obstacle detection;optical character recognition of medical records based on deep learning;a comparative study of pre-trained CNNs and GRU-based attention for image caption generation;primary brain tumors image segmentation based on 3D-UNET with deep supervision and 3D brain modeling;an improved rotation-equivariant detector for aerial object detection;empirical UAV power consumption evaluation for optimized power constrained low cost UAV line of products path planning;and CTDS-Net: CNN-transformer fusion network for dermoscopic image segmentation.

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Smart vision Bot 5th

Smart Vision Bot

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5th international conference on Data Science, Machine Learning and Applications

作者： Nandeshwar, Vikas J. Borse, Om Buthale, Prateek Borse, Chirayu Borhade, Bharati Borse, Sayali Vishwakarma Inst Technol Dept Engn Sci & Humanities Pune 411037 Maharashtra India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Smart vision robots have become increasingly essential in various industries and applications due to their ability to perform complex visual tasks with high precision and efficiency. this project focuses on the development of an autonomous surveillance and tracking system. the system aims to utilize computer vision algorithms for object detection and tracking. It has potential applications in security, automation, and remote monitoring. the project aims to enhance safety and efficiency in various environments through autonomous surveillance capabilities.

关键词： Automation Autonomous Surveillance Bot Smart vision Real-time

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Progressive Feature Fusion for Enhanced Foreground Segmentation 5th

Progressive Feature Fusion for Enhanced Foreground Segmentat...

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5th international conference on Data Science, Machine Learning and Applications

作者： Rachana, Sudam Neeli, Shrenika Kumar, Gurrampally Yerra, Harini JB Inst Engn & Technol Dept Informat Technol Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Foreground segmentation is an important part of computer vision and has many useful applications, such as tracking objects and detecting anomalies. However, deep learning models often struggle with the difficult task of preserving small details when extracting features, which is crucial for accurately segmenting edges. To overcome this challenge, we have developed a new segmentation network with a cascading architecture. this innovative approach gradually incorporates finer details into higher-level features, continuously improving the segmentation results. We have thoroughly evaluated our method using the challenging CDnet2014 dataset, and it has shown exceptional performance with an impressive F-measure of 0.9868. this research demonstrates the potential of cascading networks to greatly improve foreground segmentation in computer vision applications, and it is completely original work.

关键词： Foreground segmentation computer vision Feature extraction Cascading architecture Anomalies detection

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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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9th international Skin Imaging Collaboration Workshop, ISIC 2024, 7th international Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2024, Embodied AI and robotics for Healthcare Workshop, EARth 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

9th International Skin Imaging Collaboration Workshop, ISIC ...

引用

9th international Skin Imaging Collaboration Workshop, ISIC 2024, 7th international Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2024, Embodied AI and robotics for Healthcare Workshop, EARth 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031776090

the proceedings contain 23 papers. the special focus in this conference is on Skin Imaging Collaboration, Interpretability of Machine Intelligence in Medical Image Computing, Embodied AI and robotics for Healthcare Workshop and MICCAI Workshop on Distributed, Collaborative and Federated Learning. the topics include: DeCaF 2024 Preface;i2M2Net: Inter/Intra-modal Feature Masking Self-distillation for Incomplete Multimodal Skin Lesion Diagnosis;from Majority to Minority: A Diffusion-Based Augmentation for Underrepresented Groups in Skin Lesion Analysis;segmentation Style Discovery: Application to Skin Lesion Images;a vision Transformer with Adaptive Cross-Image and Cross-Resolution Attention;lesion Elevation Prediction from Skin Images Improves Diagnosis;DWARF: Disease-Weighted Network for Attention Map Refinement;PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans;Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings Using Spine MRI Data;interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis;TextCAVs: Debugging vision Models Using Text;evaluating Visual Explanations of Attention Maps for Transformer-Based Medical Imaging;Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI;EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting;VISAGE: Video Synthesis Using Action Graphs for Surgery;a Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery;SurgTrack: CAD-Free 3D Tracking of Real-World Surgical Instruments;MUTUAL: Towards Holistic Sensing and Inference in the Operating Room;Complex-Valued Federated Learning with Differential Privacy and MRI Applications;enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications;federated Impression for Learning with Distributed Heterogeneous Data;A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation;probing the Effic

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Real-Time Occlusion-Aware Object Tracking 5

Real-Time Occlusion-Aware Object Tracking

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5th National and the 1st international conference on Applied Research in Electrical Engineering, AREE 2025

作者： Seyfipoor, Mahdi Zafarqandi, Mohammad Javad Samii Mohammadi, Siamak University of Tehran School of Electrical and Computer Engineering Tehran Iran University of Tehran Computer Engineering Tehran Iran University of Tehran Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350377170

this paper presents a novel system for real-time object tracking in occlusion-prone environments, addressing critical challenges in achieving robustness, accuracy, and efficiency under dynamic and cluttered conditions. the proposed system integrates geometric analysis using Intersection over Union (IoU), probabilistic validation via a Hidden Markov Model (HMM), and an advanced object detection module to ensure consistent performance during both partial and complete occlusions. Evaluated on the KITTI dataset, the system achieves a Success Rate of 83%, Precision of 93%, Recall of 86%, and an F1 Score of 0.91, outperforming state-of-the-art methods such as Query-Guided Redetection Tracker (QRDT) and Siamese Occlusion-Aware Network (SiamON) in terms of accuracy and reaction time, with a low processing delay of 35 ms. While QRDT and SiamON offer competitive results, the proposed system demonstrates superior resilience to prolonged occlusions and enhanced suitability for time-critical applications like autonomous driving and surveillance. Limitations related to identity consistency during extended occlusions are acknowledged, with future directions focused on incorporating advanced trajectory prediction and multi-modal sensor fusion. this research establishes a robust foundation for occlusion-aware tracking and contributes significantly to the domains of autonomous systems, robotics, and computer vision. © 2025 IEEE.

关键词： Hidden Markov models

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Development of a computer-Assisted vision-Based Inspection Monitoring System for Quality Assurance 5th

Development of a Computer-Assisted Vision-Based Inspection M...

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5th international conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Bhide, Prajwal S. Manivannan, P.V. Department of Mechanical Engineering Indian Institute of Technology Madras Chennai600036 India

ISBN: (纸本)9789819767311

In a product-based manufacturing layout, the operator must inspect multiple similar products, making the process monotonous and strenuous. the quality control stage is mandatory in any manufacturing industry before the final product is dispatched. Of many quality inspection procedures, visual inspection is the most widely used. However, the visual inspection process becomes repetitive due to the vast number of products to be inspected daily. Hence, the operator carrying the inspection process is prone to human limitations like fatigue, which results in error, i.e. missing some of the inspection points. To mitigate this problem, a computer-Assisted Visual Inspection System (CAVIS) has been proposed in this work, which monitors and ensures inspection carried out by the operator. In CAVIS, a hand-tracking model detects and tracks the operator’s hand motion, i.e. the pointing finger used to touch the inspection markings to be checked. the present work is divided mainly into two parts: developing the inspection system algorithm to determine the coordinates of inspection marks in the image captured by a vision camera and subsequently tracking and ensuring the operator touches all the inspection markings using the pointing finger in real time, i.e. validating the developed system through experimental investigation. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inspection

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A Comparative Analysis of ResNet and MobileNet for Classifying MRI Images 5th

A Comparative Analysis of ResNet and MobileNet for Classifyi...

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5th international conference on Data Science, Machine Learning and Applications

作者： Padmaja, D. Lakshmi Nikhil, B. Akshaya, Banda Sai Deepak, G. Surya Anurag Univ Hyderabad India North Carolina A&T State Univ Greensboro NC USA Cognizant Technol Solut Chennai India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

this work represents a comprehensive analysis of the performance of two popular deep learning architectures, ResNet and MobileNet, with particular attention to their use in the classification of magnetic resonance imaging (MRI) pictures. Healthcare professionals need to accurately classify medical images in order to make precise diagnosis and develop successful treatment plans. In this paper authors have done thorough comparative research to clarify the quantitative performance indicators while also exploring qualitative elements, such as the subtle differences between each model's strengths and weaknesses. Beyond the technical assessment, the study investigates ResNet's and MobileNet's computational effectiveness and flexibility in response to the various features present in medical imaging data. the project aims to provide a sophisticated understanding of these deep learning systems in order to make a significant addition to the medical image analysis field as a whole. the ultimate goal is to promote improvements in diagnostic accuracy, which will enable healthcare providers to make better judgments and provide better patient care. the results of this study will be crucial in determining the direction of future advancements in this important field as deep learning and medical imaging continue to cross paths. they provide insightful information that goes beyond ResNet and MobileNet to affect the larger field of deep learning applications in medical diagnostics and treatment planning. the aforementioned study highlights the profound potential of deep learning technology to enhance healthcare procedures and further advance medical science.

关键词： ResNet MobileNet Advancements Deep Learning Algorithms computer vision

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Enhancing Online Learning: A Pose and Emotion-Based Approach for Student Attention Monitoring 5th

Enhancing Online Learning: A Pose and Emotion-Based Approac...

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5th international conference on Computing and Network Communications, CoCoNet 2023

作者： Chingapurathu, Sarath Krishna Jeganathan, L. Janaki Meena, M. School of Computer Science and Engineering Vellore Institute of Technology TN Chennai600127 India

ISBN: (纸本)9789819747108

With the increasing popularity and preference of online classes, teachers are at a disadvantage of losing face-time with students and are faced with the challenge of monitoring their students’ attention levels remotely. this research, VisioFocus, presents a non-invasive student monitoring system using deep learning and computer vision to measure and analyze the attention levels of students. It has two main components namely Pose Estimation and Emotion Recognition which processes the captured facial images of the student through the webcam at 15 frames per second to measure the attention level. the Pose Estimation model uses pure computer vision and the Emotion Recognition model uses a modified ResNet deep convolutional network that could classify emotions portrayed into 6 different labels at about 70% accuracy. this research proposes a novel and reliable approach to measuring attention levels, which will have a significant impact on the education field. the analytical survey of attention levels after each lecture will enable educators to tailor their teaching approach to specific students, resulting in better academic development. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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5th international conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

5th International Conference on Innovative Product Design an...

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5th international conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

ISBN: (纸本)9789819767311

the proceedings contain 57 papers. the special focus in this conference is on Innovative Product Design and Intelligent Manufacturing Systems. the topics include: Development of an Intuitive Autonomous Ground Robot for Indoor Delivery Applications;development of a computer-Assisted vision-Based Inspection Monitoring System for Quality Assurance;analysis of Challenges of IoT Adoption for Smart City Application;finite Element Modelling and Comparative Assessment of Single-Level Cervical Disc Replacement with Fusion Surgery;a Statistical Method-Based Auto-Fitted Genetic Algorithm for Damage Diagnosis;emovere Agnitio by Textual Tweets Using Machine Learning;government Bidding Security Management System Using Blockchain;resource Forecasting of Geographical Area Using Big Data Analytics;design Optimization and Aerothermodynamic Analysis Over a Supersonic Vehicle;Evaluation of Weldability and Optimization of TIG Welding Process Parameters for Maximized Yield Strength;terahertz-Frequency Propagation for Autonomous Transport Infrastructure;energy-Efficient Coffee Pulper;community Perception on Groundwater Quality Versus Analytical Quality in the Coastal Areas of Visakhapatnam, India—Opinion Versus Actuality;simulation Modeling and Analysis for Supply Chain Disruptions: A Case Study from a Process Industry in Kerala;integrating Hand Gesture Tracking with Physical and Digital Twins of Robotic Arm;performance Enhancement of Pump Parameters through Innovative Spring Design of Mechanical Seal for a Heat Exchanger;enhancing Biocompatibility and Performance of Multi-directionally Expanding Materials for Dental Applications: Design and Analysis;synthesis of Hybrid Nanofluids, Properties, and Its Applications—A Comprehensive Review;application of Hybrid Decision-Making Method in Ranking Polymer Biomaterials for Interim Dental Crowns;Analysis on Mechanical Properties of Aluminium MMC with Graphite and Boron Nitride Powder;textile Crafts of Bharat.

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