Showing 501 - 520 results of 520 for search '"Computer vision"', query time: 0.07s Refine Results
  1. 501

    MicroVi: A Cost-Effective Microscopy Solution for Yeast Cell Detection and Count in Wine Value Chain by Ismael Benito-Altamirano, Sergio Moreno, David M. Vaz-Romero, Anna Puig-Pujol, Gemma Roca-Domènech, Joan Canals, Anna Vilà, Joan Daniel Prades, Ángel Diéguez

    Published 2025-01-01
    “…The technology relies on the top of state-of-the-art computer vision pipelines to post-process the images and count the cells. …”
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  2. 502
  3. 503

    Toward Integrating ChatGPT Into Satellite Image Annotation Workflows: A Comparison of Label Quality and Costs of Human and Automated Annotators by Jacob Beck, Lukas Malte Kemeter, Konrad Durrbeck, Mohamed Hesham Ibrahim Abdalla, Frauke Kreuter

    Published 2025-01-01
    “…Since the emergence of large language models (LLMs), their popularity for generating automated annotations has grown, extending possibilities and complexity of designing an efficient annotation strategy. Increasingly, computer vision capabilities have been integrated into general-purpose LLMs like ChatGPT. …”
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  4. 504

    Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models by Imran Said, Vasit Sagan, Kyle T. Peterson, Haireti Alifu, Abuduwanli Maiwulanjiang, Abby Stylianou, Omar Al Akkad, Supria Sarkar, Noor Al Shakarji

    Published 2025-01-01
    “…To fully utilize the spectral and texture features of the full VNIR and SWIR spectral domains, a computer-vision-aided image co-registration methodology was implemented to seamlessly align the VNIR and SWIR bands. …”
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    Article
  5. 505

    Sistem Isyarat Bahasa Indonesia (SIBI) Metode Convolutional Neural Network Sequential secara Real Time by Oky Dwi Nurhayati, Dania Eridani, Muhammad Hafiz Tsalavin

    Published 2022-08-01
    “…The additional device in this study is developed using deep learning and computer vision technology to produce a hand signal translation tool. …”
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    Article
  6. 506

    Integrating Explainable Artificial Intelligence With Advanced Deep Learning Model for Crowd Density Estimation in Real-World Surveillance Systems by Sultan Refa Alotaibi, Hanan Abdullah Mengash, Mohammed Maray, Faiz Abdullah Alotaibi, Abdulwhab Alkharashi, Ahmad A. Alzahrani, Moneerah Alotaibi, Mrim M. Alnfiai

    Published 2025-01-01
    “…Crowd Density Detection in Smart Video Surveillance involves advanced computer vision (CV) techniques to improve the efficiency and accuracy of crowd monitoring. …”
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    Article
  7. 507

    Towards Understanding the Security of Modern Image Captchas and Underground Captcha-Solving Services by Haiqin Weng, Binbin Zhao, Shouling Ji, Jianhai Chen, Ting Wang, Qinming He, Raheem Beyah

    Published 2019-06-01
    “…However, the ever-advancing capabilities of computer vision have gradually diminished the security of image captchas and made them vulnerable to attack. …”
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    Article
  8. 508

    Annotated image dataset with different stages of European pear rust for UAV-based automated symptom detection in orchardsMendeley Data by Virginia Maß, Pendar Alirezazadeh, Johannes Seidl-Schulz, Matthias Leipnitz, Eric Fritzsche, Rasheed Ali Adam Ibraheem, Martin Geyer, Michael Pflanz, Stefanie Reim

    Published 2025-02-01
    “…Each leaf with pear rust symptoms was annotated with the drawing method by two points (bounding boxes) using the Computer Vision Annotation Tool (CVAT, v1.1.0) [1] and presented in YOLO 1.1 file format (.txt files). …”
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  10. 510

    cigFacies: a massive-scale benchmark dataset of seismic facies and its application by H. Gao, X. Wu, X. Sun, M. Hou, M. Hou, H. Gao, G. Wang, H. Sheng

    Published 2025-02-01
    “…Data-driven deep-learning approaches are highly promising for automating the seismic facies classification with high efficiency and accuracy, as they have already achieved significant success in similar image classification tasks within the field of computer vision (CV). However, unlike the CV domain, the field of seismic exploration lacks a comprehensive benchmark dataset for seismic facies, severely limiting the development, application, and evaluation of deep-learning approaches in seismic facies classification. …”
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    Article
  11. 511

    Enhancement of convolutional neural network for urban environment parking space classification by S. Rahman, M. Ramli, F. Arnia, R. Muharar, M. Ikhwan, S. Munzir

    Published 2022-07-01
    “…Modification of the Convolutional Neural Networks architecture is assumed to increase the work efficiency of the smart parking system in processing parking availability information.METHODS: Research is focusing on developing parking space classification techniques using camera sensors due to the rapid advancement of technology and algorithms in computer vision. The input image has 3x3 dimensions. The first convolution layer accepts the input image and converts it into 56x56 dimensions. …”
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  12. 512

    Perbandingan Kinerja Inception- Resnetv2, Xception, Inception-v3, dan Resnet50 pada Gambar Bentuk Wajah by Fitriana Masruroh, Bayu Surarso, Budi Warsito

    Published 2023-02-01
    “…Deep learning is becoming a trend in the field of computer vision because it gives the best results than the previous method. …”
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  13. 513

    An Open-Source Multi-Robot Framework System for Collaborative Environments Based on ROS2 by Francisco Yumbla, Marcelo Fajardo-Pruna, Anthonny Piguave, Diego Ronquillo, Ricardo Ortiz, Jongseong Brad Choi, Gabriel Diaz, Xabiel G. Paneda, Hyungpil Moon

    Published 2025-01-01
    “…A centralized architecture was obtained with an autonomous navigation module for the planning and robot routes monitoring, a computer vision module for the location and management of uncertainties, and a task controller module to assign mobilization mission objects. …”
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    Article
  14. 514

    Can ChatGPT4-vision identify radiologic progression of multiple sclerosis on brain MRI? by Brendan S. Kelly, Sophie Duignan, Prateek Mathur, Henry Dillon, Edward H. Lee, Kristen W. Yeom, Pearse A. Keane, Aonghus Lawlor, Ronan P. Killeen

    Published 2025-01-01
    “…However, in absolute terms, in a simplified “spot the difference” medical imaging task, GPT4V was inferior to state-of-the-art computer vision methods. GPT4V’s performance metrics were more similar to the ViT than the U-net. …”
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  15. 515

    Klasifikasi Aktivitas Manusia Menggunakan Algoritme Computed Input Weight Extreme Learning Machine dengan Reduksi Dimensi Principal Component Analysis by M. Sofyan Irwanto, Fitra A. Bachtiar, Novanto Yudistira

    Published 2022-12-01
    “…Based on various approaches that have been used to recognize human activity, sensor-based techniques are known to be superior to other techniques such as computer vision-based techniques. Sensor-based technique can also be performed using smartphones, but smartphone has disadvantages in performing complex alghorithmic computation. …”
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  16. 516

    Automated Video Assistant Referee in Lead Climbing by Eliane Künzler, David Roder, Urs Stöcker, Peter Wolf

    Published 2025-01-01
    “…RTMO: Towards high-performance one-stage real-time multi-person pose estimation. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1491–1500. https://doi.org/10.1109/CVPR52733.2024.00148 …”
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  18. 518

    Knowledge Distillation in Object Detection for Resource-Constrained Edge Computing by Arief Setyanto, Theopilus Bayu Sasongko, Muhammad Ainul Fikri, Dhani Ariatmanto, I. Made Artha Agastya, Rakandhiya Daanii Rachmanto, Affan Ardana, In Kee Kim

    Published 2025-01-01
    “…Object detection (OD) is an essential task in computer vision. Although state-of-the-art deep learning-based OD methods achieve high detection rates, their large model size and high computational demands often hinder deployment on resource-constrained edge devices. …”
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  19. 519

    Convolutional neural networks for sea surface data assimilation in operational ocean models: test case in the Gulf of Mexico by O. Zavala-Romero, O. Zavala-Romero, A. Bozec, E. P. Chassignet, J. R. Miranda, J. R. Miranda

    Published 2025-01-01
    “…<p>Deep learning models have demonstrated remarkable success in fields such as language processing and computer vision, routinely employed for tasks like language translation, image classification, and anomaly detection. …”
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