Intro to CV and Python (slides)

Basic image processing (slides)

Filtering and resampling (slides)

  • Noise and filtering (notebook)
  • Frequency representation
  • Decimation
  • Interpolation

Edge Detection (slides)

Curve fitting (slides)

Hough transform (slides)

Image formation (slides)

  • BRDF
  • Pinhole camera
  • Digital camera
  • The human eye

Geometric transformation (slides)

  • 2D->2D transformations (notebook)
  • 3D->3D transformations
  • 3D->2D transformations (3D projections)
    • Perspective projection
    • Orthographic projection

Camera calibration (slides)

  • What is camera calibration?
  • Camera extrinsics
  • Perspective projection
  • Camera intrinsics
  • Full camera matrix
  • Calibration methods and distortions (notebook)

Features (slides)

  • What and why we need features detection?
  • Feature detection
  • Feature description
    • Template matching
    • HOG
    • SIFT descriptor
  • SIFT feature matching (SIFT notebook)
  • Panoramas

Stereo (slides)

  • Structure from motion
  • Triangulation
  • Stereo matching
  • Camera rectification
  • Epipolar geometry
    • Essential matrix
    • Fundamental matrix
    • Estimating the fundamental matrix
  • Other 3D sensors

Neural networks basics (slides)

  • The classification problem- again
  • NN history
  • Perceptron
    • Hyperplanes
    • Activation
  • Dense layer
  • Multi-layer perceptron (MLP)
  • Optimization
    • Softmax + cross entropy + loss
    • Gradient descent
  • Basic data preprocessing
    • Data normalization
    • Train, validation and test splits
  • Fully connected net

Neural networks 2 (slides)

  • ConvNets
    • Convolution layer
    • Pooling layer
  • Overfitting