Buch, Englisch, 284 Seiten, Format (B × H): 155 mm x 235 mm
Buch, Englisch, 284 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Lecture Notes in Networks and Systems
ISBN: 978-3-032-29557-6
Verlag: Springer
The book covers a wide range of highly qualified papers presented at the Computer Vision Conference 2026, held on May 21-22, 2026, in Amsterdam, Netherlands. The paper submissions for this conference were received from researchers, academicians, and industry practitioners from various countries. To ensure the high standards of originality, technical quality, and relevance to the conference themes, all papers were double-blind peer-reviewed to ensure that the final published papers meet high standards of originality, technical quality, and relevance to the conference theme.
Altogether, these contributions cover the current research trends, offer valuable insights, and address real-world problems. This inspires future investigations and developments in the field of Machine Vision and Deep Learning, Image Processing, Data Science, and Applications.
We hope that this volume of the proceedings serves as a valuable reference for researchers and practitioners and contribute to continued exploration, collaboration, and innovation in the rapidly evolving field of computing.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Building Trustworthy Computer Vision Models to Specification.- Computer Vision and Predictive Analytics Pipeline for Sustainable Fisheries: A Survey.- Automated Pineapple Fruit Detection and Counting System Using Machine Learning.- Integration of Rotational Constraints During Bundle Adjustment in OpenMVG.- A Comparative Study on the Accuracy and Constraints of Pose-Free Gaussian Splatting Methods.- A Web-Based Intelligent System for Automated Alzheimer’s Disease Classification from Brain MRI.- AI-Powered Study Planner Prototype System.- Visual MEMENTO: A Foundation Model Framework for Recording, Understanding, and Retrieving Screen-Share Sessions in Meetings.- Towards Region-Adapted Deep Learning Models for Date Palm Disease Detection in the UAE.- RAM-LightGlue: Radiometric Alignment for Robust RGB–Thermal Image Registration in Hazy Conditions.
