This course on Computer Vision aims to teach students the fundamentals of image formation, structure-from-motion, stereo reconstruction, probabilistic graphical models, applications of graphical models, learning in graphical models, and shape-from-X techniques. Students will learn about primitives and transformations, geometric and photometric image formation, image sensing pipeline, factorization, bundle adjustment, block matching, Siamese networks, spatial regularization, end-to-end learning, Markov random fields, belief propagation, conditional random fields, parameter estimation, deep structured models, shape-from-shading, photometric stereo, volumetric fusion, and more. The course utilizes lectures to deliver the content and is designed for individuals interested in computer vision and its applications.
Overview
Syllabus
Computer Vision - Lecture 1.1 (Introduction: Organization).
Computer Vision - Lecture 1.2 (Introduction: Introduction).
Computer Vision - Lecture 1.3 (Introduction: History of Computer Vision).
Computer Vision - Lecture 2.1 (Image Formation: Primitives and Transformations).
Computer Vision - Lecture 2.2 (Image Formation: Geometric Image Formation).
Computer Vision - Lecture 2.3 (Image Formation: Photometric Image Formation).
Computer Vision - Lecture 2.4 (Image Formation: Image Sensing Pipeline).
Computer Vision - Lecture 3.1 (Structure-from-Motion: Preliminaries).
Computer Vision - Lecture 3.2 (Structure-from-Motion: Two-frame Structure-from-Motion).
Computer Vision - Lecture 3.3 (Structure-from-Motion: Factorization).
Computer Vision - Lecture 3.4 (Structure-from-Motion: Bundle Adjustment).
Computer Vision - Lecture 4.1 (Stereo Reconstruction: Preliminaries).
Computer Vision - Lecture 4.2 (Stereo Reconstruction: Block Matching).
Computer Vision - Lecture 4.3 (Stereo Reconstruction: Siamese Networks).
Computer Vision - Lecture 4.4 (Stereo Reconstruction: Spatial Regularization).
Computer Vision - Lecture 4.5 (Stereo Reconstruction: End-to-End Learning).
Computer Vision - Lecture 5.1 (Probabilistic Graphical Models: Structured Prediction).
Computer Vision - Lecture 5.2 (Probabilistic Graphical Models: Markov Random Fields).
Computer Vision - Lecture 5.3 (Probabilistic Graphical Models: Factor Graphs).
Computer Vision - Lecture 5.4 (Probabilistic Graphical Models: Belief Propagation).
Computer Vision - Lecture 5.5 (Probabilistic Graphical Models: Examples).
Computer Vision - Lecture 6.1 (Applications of Graphical Models: Stereo Reconstruction).
Computer Vision - Lecture 6.2 (Applications of Graphical Models: Multi-View Reconstruction).
Computer Vision - Lecture 6.3 (Applications of Graphical Models: Optical Flow).
Computer Vision - Lecture 7.1 (Learning in Graphical Models: Conditional Random Fields).
Computer Vision - Lecture 7.2 (Learning in Graphical Models: Parameter Estimation).
Computer Vision - Lecture 7.3 (Learning in Graphical Models: Deep Structured Models).
Computer Vision - Lecture 8.1 (Shape-from-X: Shape-from-Shading).
Computer Vision - Lecture 8.2 (Shape-from-X: Photometric Stereo).
Computer Vision - Lecture 8.3 (Shape-from-X: Shape-from-X).
Computer Vision - Lecture 8.4 (Shape-from-X: Volumetric Fusion).
Taught by
Tübingen Machine Learning
