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Comprehensive Course on Computer Vision Techniques

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 (16)
234 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
▶
  • Computer Vision - Lecture 1.1 (Introduction: Organization)05:45
  • Computer Vision - Lecture 1.2 (Introduction: Introduction)26:53
  • Computer Vision - Lecture 1.3 (Introduction: History of Computer Vision)01:03:39
  • Computer Vision - Lecture 2.1 (Image Formation: Primitives and Transformations)52:18
  • Computer Vision - Lecture 2.2 (Image Formation: Geometric Image Formation)34:45
  • 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)22:57
  • Computer Vision - Lecture 3.4 (Structure-from-Motion: Bundle Adjustment)30:12
  • Computer Vision - Lecture 4.1 (Stereo Reconstruction: Preliminaries)44:14
  • 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)14:18
  • Computer Vision - Lecture 4.5 (Stereo Reconstruction: End-to-End Learning)15:35
  • Computer Vision - Lecture 5.1 (Probabilistic Graphical Models: Structured Prediction)19:59
  • 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)33:22
  • 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)37:00
  • Computer Vision - Lecture 6.3 (Applications of Graphical Models: Optical Flow)46:28
  • Computer Vision - Lecture 7.1 (Learning in Graphical Models: Conditional Random Fields)18:22
  • Computer Vision - Lecture 7.2 (Learning in Graphical Models: Parameter Estimation)47:17
  • 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)09:09
  • 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)28:17
  • 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)57:53
  • Computer Vision - Lecture 10.2 (Recognition: Semantic Segmentation)16:29
  • Computer Vision - Lecture 10.3 (Recognition: Object Detection and Segmentation)42:08
  • Computer Vision - Lecture 11.1 (Self-Supervised Learning: Preliminaries)22:26
  • Computer Vision - Lecture 11.2 (Self-Supervised Learning: Task-specific Models)27:37
  • Computer Vision - Lecture 11.3 (Self-Supervised Learning: Pretext Tasks)25:21
  • Computer Vision - Lecture 11.4 (Self-Supervised Learning: Contrastive Learning)30:52
  • 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)17:30
Computer Vision
Image Formation
Structure-from-Motion
Object Detection
Image Classification

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