2026 The 9th International Conference on Machine Vision and Applications (ICMVA 2026) will be held in Nanjing, China during April 24-26 . This conference is dedicated to the presentation and discussion of the latest application and findings in the field of Machine Vision. Machine Vision technologies in their essence leverage advanced digital inventions that enhance efficiency, accuracy, and decision-making across various sectors such as industrial automation, quality control, inspection, medical diagnostics, security, and robotics. While the focus of the ICMVA is directed to the presentation of current applications of Machine Vision, the conference is also aimed at those experts who deal with new principles and methods in that emerging technology. Scope: Machine Vision: Practice and Applications, Methods, Principles, and Algorithms.

The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. Topics of interest for submission include, but are not limited to:

Practice and Applications of Machine Vision

Industrial Automation; Digital Production; Digital Metrology; Quality Control and Inspection; Non-Destructive Testing and Fault Detection; Medical Imaging; Robotics; Autonomous Vehicles; Inspection of Micro and Nano Structures; Automation in Agriculture and Food Production; Surveillance and Security; Motion Analysis; Environmental Monitoring; Protection of Cultural Heritage; Remote Sensing

Principles, Methods and Algorithms of Machine Vision

Image Formation (Image Acquisition, Optical Basics, Photometric Basics, Vision Models, Color Spaces, Sensors)
Image Modeling (Image Representation, Systems and Signals, 2D-, 3D-Models, Projections, Rendering, Modalities, Parameters)
Image Processing (Image Preprocessing, Image Enhancement, Image Filtering, Image Analysis, Image Restoration, Multiresolution Analysis, Image Alignment and Stitching, Geometric Transformations)
Image Classification (Feature Space, Feature Extraction, etches and Contours, Segmentation, Texture Analysis, Morphology, Pattern Recognition, Computer Vision, Machine Vision)
Image Reconstruction (Depth Estimation and Reconstruction, Monocular Depth Estimation, Stereo Vision, Multi-View Stereo, Shape from Shading, 3D Reconstruction, Volumetric Representations)
Machine Learning, Deep Learning and Neural Networks (Supervised Learning, Unsupervised Learning, Deep Neural Networks, Convolutional Neural Networks)