Proceedings of the Computer Vision Conference (CVC) 2026, Volume 2 : DE (Lecture Notes in Networks and Systems)

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Proceedings of the Computer Vision Conference (CVC) 2026, Volume 2 : DE (Lecture Notes in Networks and Systems)

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  • 製本 Paperback:紙装版/ペーパーバック版
  • 商品コード 9783032262103

Description

This 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, this book covers the current research trends, offers valuable insights, and addresses 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 book 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.

Dynamics of Intelligent Communication: From Specialization to Superintelligence.- A Transformer-Based Multimodal On-Device AI System for Vulnerable Elderly Care in Aging Societies.- A Study on Roadkill Prevention Using Super-Resolution and Real-Time Object Detection Models.- Preventing Forest Fire Reignition Using Thermal Object Detection: A Lightweight Approach Based on YOLOv8-Nano and MobileNetV3.- Adaptive Multi-Style Transfer with Hybrid Neural Encoding.- Adaptive Task Execution for Robots Using Scene Understanding and LLMs.- YO-SAM: YOLO-Guided Segment Anything Model.- Dynamic Human-Aware Navigation for Mobile Robotic Platforms in Crowded Environments.- PH-SAM2: Persistent Homology-Guided Prompting of SAM2 for Zero-Shot Medical Image Segmentation.- Crop Disease Classification Using Lightweight Deep Learning Models.


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