Can we make machines look, understand and interpret the world around them? Can we make cars that can autonomously navigate in the world, robots that can recognize and grasp objects and, ultimately, recognize humans and communicate with them? How do search engines index and retrieve billions of images? This course will provide the knowledge and skills that are fundamental to core vision tasks of one of the fastest growing fields in academia and industry: visual computing. Topics include introduction to fundamental problems of computer vision, mathematical models and computational methodologies for their solution, implementation of real-life applications and experimentation with various techniques in the field of scene analysis and understanding. In particular, after a recap of basic image analysis tools (enhancement, restoration, color spaces, edge detection), students will learn about feature detectors and trackers, fitting, image geometric transformation and mosaicing techniques, texture analysis and classification using unsupervised techniques, face analysis, deep learning based object classification, detection and tracking, camera models, epipolar geometry and 3D reconstruction from 2D views.
Prerequisites
None.
Desired prior knowledge: Basic knowledge of Python, linear algebra and machine learning. This course offers the basics on image processing although prior knowledge is also a plus.
Recommended reading
“Digital Image Processing”, Rafael C. Gonzalez & Richard E. Woods, Addison-Wesley, “Computer Vision: Models, Learning and Inference”, Simon J.D. Prince 2012.
More information at: https://curriculum.maastrichtuniversity.nl/meta/466719/computer-vision
