On this page, I will post links that I believe can be relevant for this course. These are not necessarily part of the official curriculum, but often it is useful to research a topic from different sources.

General

• Official course home
• Teaching material
• INF4300 from Ole Marius
• Python 3 tutorial
• Anaconda home
• Numpy and scipy docs
• OpenCV home

Topic-specific

• UC Calgary GLCM tutorial
• Statistical Texture Measures from GLCM
• Notes on Laws’ texture energy
• Random Hough Transform
• Methods to estimate area and perimeter
• 3Blue1Brown: A collection of videos which beautifully illustrates math stuff. Very relevant for visualization of the topics in linear algebra that we touch upon (eigen-analysis, PCA, and LDA).
• A Geometric interpretation of the covariance matrix
• A Tutorial on Principal Component Analysis
• Cosma Roholla Shalizi, Advanced Data Analysis from an Elementary Point of View, Chapter 17
• Sergios Theodoridis and Konstantinos Koutroumbas, Pattern Recognition
  • Chapter 3
  • Chapter 4.17
  • Chapter 5.6 - 5.8 and
  • Chapter 6 - 6.3 and Appendix B.
  • Appendix C.
• I.T. Joliffe, Principal Component Analysis book
• Principal component analysis in python
• Linear discriminant analysis in python
• Morphology examples in skimage
• Morphology examples in openCV
• CS229 Support Vector Machines.