CLASS SEQUENCE (w adjustments in "real time")

- Intro and camera model

• Motivation & Intro
• Images & Point Clouds
• Camera Model 
  

-  Geometric Backg (2D transformations)

• LSE (homogeneous & Pseudoinverse) 
• Camera calibration
• Pose estimation (known K)

-Homography and 2D registration

• DLT
• Keypoints, 
• Matching, Outlier Removal
  

- 3D Registration, 

•  Procrustes Problem,
• 3D registration ICP
  

- Stereo Vision

•     Essential and Fundamental Matrix, 
•     Triangulation, Reconstruction
•     DLT and Robust estimation of F
  

-Structure From Motion

• Tracking (the LK tracker)
  
• Projective SfM pipeline and Tools (OpenSFM, Colmap)
• Factorization based SfM
  

- Image processing

• Linear/Nonlinear Filtering
• Edge Detection
•  Line Detection

- Recognition and Classification

• Artificial neural networks
• low level filters -> high level
• feature maps

-Detection 

• Region proposals
• RoI pooling
• Architectures
  

-Segmentation

• Architecture structure
• Semantic     segmentation andInstance segmentation

 - Special Topics
 - Seminar by a computer vision tech company (TBD)
Last class  quiz on project topics