Technical Outline Vision-based measurement is already widely used across scientific and industrial applications, but recent progress in computer vision, machine learning, and artificial intelligence is changing how these systems are designed. In many emerging approaches, useful measurement information is learned directly from data rather than relying only on predefined physical models or handcrafted processing pipelines.

This Special Session focuses on data-driven models for vision-based measurement systems and aims to bring together researchers working across measurement science, image processing, computer vision, and artificial intelligence. The session will explore how learning-based methods can extract, interpret, or improve quantitative information from images and videos while preserving the accuracy, reliability, robustness, and validation expected in measurement applications. Topics include learning-based image processing, detection, segmentation and tracking, 2D and 3D vision, stereo vision, depth estimation, photogrammetry, 3D reconstruction, point-cloud processing, calibration, uncertainty evaluation, robustness and domain adaptation, explainability, benchmark datasets, and multimodal sensor fusion. Applications may include industrial inspection, geometric and dimensional measurement, structural monitoring, robotics, autonomous systems, healthcare, and digital twins.

Topics

• Data-driven and learning-based models for vision-based measurement
• Machine learning and deep learning for measurement applications
• Image processing for quantitative measurement
• Object detection, segmentation, and tracking
• 2D and 3D computer vision
• Stereo vision, depth estimation, and photogrammetry
• 3D reconstruction and point-cloud processing
• Camera and multi-camera calibration
• Measurement uncertainty and reliability in vision-based systems
• Explainable and trustworthy AI for measurement
• Multimodal and multi-sensor fusion in vision-based systems
• Benchmark datasets and validation methodologies in vision-based systems
• Vision-based structural and infrastructure monitoring
• Robotics and autonomous measurement in vision-based systems