![]() ![]() ![]() We will build on our previous knowledge of linear algebra and provide fundamentals of geometry for computer vision, computer graphics, augmented reality, image processing, and object recognition. We will demonstrate the theory in practical panorama construction tasks, finding the camera pose, adding a virtual object to a real scene, and reconstructing a 3D model of a scene from its images. We will show how to compute camera poses and the 3D scene geometry from images. We will explain Euclidean, Affine, and Projective geometry basics, introduce a model of the perspective camera, and explain how images change when moving a camera. Wherever research papers are necessary for a deeper understanding, we will make them available on this web page or in the Moodle course room.He who loves practice without theory is like the sailor who boards a ship without a rudder and compass and never knows where he may cast.Īnd since geometry is the right foundation of all painting, I have decided to teach its rudiments and principles to all youngsters eager for art.Īs for everything else, so for a mathematical theory: beauty can be perceived but not explained. They have a growing impact in many other areas of science and engineering, and increasingly, on commerce and society. ![]() The individual homeworks can be found in the src/ folder. Fall 2022 Lectures: Tu/Th 9:3011:00 am, Soda 306 Description Deep Networks have revolutionized computer vision, language technology, robotics and control. Most of the homeworks will use this repository. M., Effelsberg, W.: An automatic cameraman in a lecture recording system. We cover basic image manipulations, filtering, features, stitching, optical flow, machine learning, and convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. However, a good part of the material presented in this class is the result of very recent research, so it hasn't found its way into textbooks yet. The class has 6 homeworks where you will build out a computer vision library in C. Zisserman, Multiple View Geometry in Computer Vision, 2nd Edition, Cambridge University Press, 2004 Szeliski, Computer Vision - Algorithms and Applications, Springer, 2010 These lectures introduce the theoretical and practical aspects of computer vision from the basics of the image formation process in digital cameras, through. Ponce, Computer Vision - A Modern Approach, Prentice Hall, 2002 We will mainly make use of the following books: In the last decades, Computer Vision has evolved into a rapidly growing field with research going into so many directions that no single book can cover them all. The lecture is accompanied by programming exercises that will allow you to collect hands-on experience with the algorithms introduced in the lecture (there will be one exercise sheet roughly every two weeks). ![]() In addition, it will show current research developments and how they are applied to solve real-world tasks. This lecture will teach the fundamental Computer Vision techniques that underlie such capabilities. The goal of Computer Vision is to develop methods that enable a machine to "understand" or analyze images and videos. But how do we actually accomplish them? And how can we teach a machine to perform similar tasks for us? For us humans, those capabilities are natural. All students in both sections will have access to the lecture recordings and in-person lecture attendance is optional. All those applications are building on visual capabilities. in surveillance and security, on consumer devices, for video special effects, in mobile robotics and automotive contexts, and for medical image processing. Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures. There are of course many situations where Python perhaps is a poor. Python is now de facto scientific computing language. I have attempted to provide Python code examples that make computer vision theory tangible. And more and more commercial applications are coming up, e.g. The lecture notes included below are aimed at individuals who may benefit from seeing computer vision theory and methods in action. Large search engines are being created to make sense out of this data. Billions of images and massive amounts of video data are becoming available on the Internet. Students should register for the lectures on the RWTH Online system to get notifications via Moodle.Ĭameras and images form an ever-growing part of our daily lives. Corona: Online Teaching in Summer Semester 2020ĭue to the ongoing corona situation all lectures and exercises will be held online. ![]()
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