Description
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, this book covers 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 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.
BAD BRAINS (British Automated Diagnosis Blueprint Radiological Artificial Intelligence Neurology Service).- Computer Vision-Based Optical Mark Recognition for Automatic Survey Evaluation.- A Camera Zoom-Based Paper-Pencil Cipher Encryption Scheme Atop Merkle Hellman Knapsack Cryptosystem (Preliminary).- Mobile Application Utilizing YOLO for Analysis and Monitoring of PPE Usage in a Manufacturing Plant in Lima.- Validation of Human Gait Trajectories Using BNO055 Inertial Sensors and AI-Based Markerless Motion Capture.- EcoSurve: AI-Powered Surveillance Video Summarization and Retrieval for Sustainable Smart Cities.- Underwater Video Enhancement Using White Balancing and Contrast-Limited Adaptive Histogram Equalization.- Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising.- Discrete Cosine Transform Convolutional Neural Networks.- Development of an Efficient Deep Learning Neural Network for Human Activity Recognition Using Multi-Sensor Data Fusion.
