521466S:2 Machine Vision, Spring 2022

Contents:

  1. Introduction
  2. Imaging and image representation
  3. Color and shading
  4. Binary image analysis
  5. Texture analysis
  6. Local features
  7. Recognition
  8. Motion from 2D image sequences
  9. 2D models and transformations
  10. Perceiving 3D from 2D images
  11. 3D transformations and reconstruction

Learning activities and teaching methods:

  • Online lectures
  • Group works
  • Online exercises
  • Homework assignments (Python & Jupyter notebooks)

Schedule:

Spring 2022 (period 3).

Assessment methods and criteria:

The course is passed with a final exam and accepted homework assignments. Group works are not mandatory.

Grading:

Exam (40%), homework assignments (27%), and group works (33%)

Prerequisites:

  • Digital Image Processing (521467A) or an equivalent course.
  • Basic Python programming skills.