Computer Vision

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Free Online Course: Computer Vision provided by YouTube is a comprehensive online course, which lasts for 15 hours worth of material. The course is taught in English and is free of charge. Computer Vision is taught by Tübingen Machine Learning.

Overview
  • 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).