Computer Vision — Andreas Geiger
Master the Art of Computer Vision: Dive into Image Formation, Structure-from-Motion, and Recognition with Cutting-Edge Techniques and Real-world Applications!
5.0
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7 learners
What you'll learn
- Understand the foundational concepts and history of computer vision.
- Describe the processes involved in image formation and transformation.
- Explain the structure-from-motion techniques and their applications.
- Apply recognition techniques for image classification and object detection.
This course includes
- 21.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 46 lessons • 21.5 hours of video
Comprehensive Course on Computer Vision Techniques
46 lessons
• 21.5 hours
Comprehensive Course on Computer Vision Techniques
46 lessons
• 21.5 hours
- Computer Vision - Lecture 1.1 (Introduction: Organization) 05:46
- Computer Vision - Lecture 1.2 (Introduction: Introduction) 26:53
- Computer Vision - Lecture 1.3 (Introduction: History of Computer Vision) 01:03:40
- Computer Vision - Lecture 2.1 (Image Formation: Primitives and Transformations) 52:19
- Computer Vision - Lecture 2.2 (Image Formation: Geometric Image Formation) 34:46
- Computer Vision - Lecture 2.3 (Image Formation: Photometric Image Formation) 23:27
- Computer Vision - Lecture 2.4 (Image Formation: Image Sensing Pipeline) 12:53
- Computer Vision - Lecture 3.1 (Structure-from-Motion: Preliminaries) 26:18
- Computer Vision - Lecture 3.2 (Structure-from-Motion: Two-frame Structure-from-Motion) 33:55
- Computer Vision - Lecture 3.3 (Structure-from-Motion: Factorization) 22:58
- Computer Vision - Lecture 3.4 (Structure-from-Motion: Bundle Adjustment) 30:12
- Computer Vision - Lecture 4.1 (Stereo Reconstruction: Preliminaries) 44:15
- Computer Vision - Lecture 4.2 (Stereo Reconstruction: Block Matching) 22:19
- Computer Vision - Lecture 4.3 (Stereo Reconstruction: Siamese Networks) 17:10
- Computer Vision - Lecture 4.4 (Stereo Reconstruction: Spatial Regularization) 14:19
- Computer Vision - Lecture 4.5 (Stereo Reconstruction: End-to-End Learning) 15:36
- Computer Vision - Lecture 5.1 (Probabilistic Graphical Models: Structured Prediction) 20:00
- Computer Vision - Lecture 5.2 (Probabilistic Graphical Models: Markov Random Fields) 32:45
- Computer Vision - Lecture 5.3 (Probabilistic Graphical Models: Factor Graphs) 08:43
- Computer Vision - Lecture 5.4 (Probabilistic Graphical Models: Belief Propagation) 33:23
- Computer Vision - Lecture 5.5 (Probabilistic Graphical Models: Examples) 13:38
- Computer Vision - Lecture 6.1 (Applications of Graphical Models: Stereo Reconstruction) 17:04
- Computer Vision - Lecture 6.2 (Applications of Graphical Models: Multi-View Reconstruction) 37:01
- Computer Vision - Lecture 6.3 (Applications of Graphical Models: Optical Flow) 46:29
- Computer Vision - Lecture 7.1 (Learning in Graphical Models: Conditional Random Fields) 18:23
- Computer Vision - Lecture 7.2 (Learning in Graphical Models: Parameter Estimation) 47:18
- Computer Vision - Lecture 7.3 (Learning in Graphical Models: Deep Structured Models) 26:31
- Computer Vision - Lecture 8.1 (Shape-from-X: Shape-from-Shading) 56:06
- Computer Vision - Lecture 8.2 (Shape-from-X: Photometric Stereo) 20:35
- Computer Vision - Lecture 8.3 (Shape-from-X: Shape-from-X) 09:10
- Computer Vision - Lecture 8.4 (Shape-from-X: Volumetric Fusion) 37:51
- Computer Vision - Lecture 9.1 (Coordinate-based Networks: Implicit Neural Representations) 45:34
- Computer Vision - Lecture 9.2 (Coordinate-based Networks: Differentiable Volumetric Rendering) 28:18
- Computer Vision - Lecture 9.3 (Coordinate-based Networks: Neural Radiance Fields) 17:49
- Computer Vision - Lecture 9.4 (Coordinate-based Networks: Generative Radiance Fields) 20:41
- Computer Vision - Lecture 10.1 (Recognition: Image Classification) 57:54
- Computer Vision - Lecture 10.2 (Recognition: Semantic Segmentation) 16:29
- Computer Vision - Lecture 10.3 (Recognition: Object Detection and Segmentation) 42:09
- Computer Vision - Lecture 11.1 (Self-Supervised Learning: Preliminaries) 22:27
- Computer Vision - Lecture 11.2 (Self-Supervised Learning: Task-specific Models) 27:38
- Computer Vision - Lecture 11.3 (Self-Supervised Learning: Pretext Tasks) 25:21
- Computer Vision - Lecture 11.4 (Self-Supervised Learning: Contrastive Learning) 30:53
- Computer Vision - Lecture 12.1 (Diverse Topics in Computer Vision: Input Optimization) 36:23
- Computer Vision - Lecture 12.2 (Diverse Topics in Computer Vision: Compositional Models) 24:34
- Computer Vision - Lecture 12.3 (Diverse Topics in Computer Vision: Human Body Models) 34:06
- Computer Vision - Lecture 12.4 (Diverse Topics in Computer Vision: Deepfakes) 17:31
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