Lecture: Computer Vision

The goal of computer vision is to compute geometric and semantic properties of the three-dimensional world from digital images. Problems in this field include reconstructing the 3D shape of an object, determining how things are moving and recognizing objects or scenes. This course will provide an introduction to computer vision, with topics including image formation, camera models, camera calibration, feature detection and matching, motion estimation, geometry reconstruction, object detection and tracking, and scene understanding. Applications include building 3D maps, creating virtual avatars, image search, organizing photo collections, human computer interaction, video surveillance, self-driving cars, robotics, virtual and augmented reality, simulation, medical imaging, and mobile computer vision. Modern computer vision relies heavily on machine learning in particular deep learning and graphical models. This course therefore assumes prior knowledge of deep learning (e.g., deep learning lecture) and introduces the basic concepts of graphical models and structured prediction where needed. The tutorials will deepen the understanding of deep neural networks by implementing and applying them in Python and PyTorch. A strong emphasis of this course is on 3D vision.

Students gain an understanding of the theoretical and practical concepts of computer vision including image formation, camera models, feature detection, multiple view geometry, 3D reconstruction, motion estimation, object recognition, scene understanding and structured prediction using deep neural networks and graphical models. A strong emphasis of this course is on 3D vision. After this course, students should be able to understand and apply the basic concepts of computer vision in practice, develop and train computer vision models, reproduce research results and conduct original research in this area.

• Course number: ML-4360
• Credits: 6 ECTS, from 2023: 9 ECTS 
• Recommended for: Master, 2nd semester
• Total Workload: 270h
• This lecture is taught as flipped classroom. Lectures will be held asynchronously via YouTube (see sidebar for link). We will provide all lectures before the respective interactive live sessions for self-study. Please watch the relevant videos before participating in the interactive live sessions.
• Each week, we host an interactive live session where questions regarding the lecture and exercises are discussed together (see sidebar for details).
• We also offer a weekly zoom helpdesk where students may ask questions or share their screen to obtain individual feedback and support for solving the exercises (see sidebar for details).
• Exercises will not be graded. Instead, we will discuss the solution together.
• Students may obtain bonus points for the exam by answering questions about the lectures and exercises in weekly quizzes. The questions also serve as a measure for self-assessment and self-motivation. All quizzes are provided via our Lecture Quiz Server (see sidebar for details).

• Basic Computer Science skills: Variables, functions, loops, classes, algorithms
• Basic Python and PyTorch coding skills
• Basic Math skills: Linear algebra, probability and information theory (eg., Math for ML lecture
  https://www.tml.cs.uni-tuebingen.de/teaching/2020_maths_for_ml/index.php)
  As a refresher we recommend reading Chapters 1-4 of: http://www.deeplearningbook.org
• Experience with Deep Learning (eg., through participation our Deep Learning lecture)

• To participate in this lecture, you must enroll via ILIAS (see sidebar for link)
• Registration via ILIAS will open on 30.03. at 12:00
• Information about exam registration can be found here

The exercises play an essential role in understanding the content of the course. There will be 6 assignments in total. The assignments contain pen and paper questions as well as programming problems. For some of the exercises, the students will use PyTorch, a state-of-the-art deep learning framework which features GPU support and auto-differentiation. If you have questions regarding the exercises or the lecture, please ask them during the interactive sessions, at the zoom helpdesk or in our ILIAS forum.

• Richard Szeliski: Computer Vision: Algorithms and Applications
• Hartley and Zisserman: Multiple View Geometry in Computer Vision
• Nowozin and Lampert: Structured Learning and Prediction in Computer Vision
• Goodfellow, Bengio and Courville: Deep Learning
• Deisenroth, Faisal and Ong: Mathematics for Machine Learning
• Computer Vision Lecture Notes written by students in summer 2021
• Articles and papers mentioned in the lecture slides