Spring, period 3.
Upon completion of the course the student1. understands the fundamentals of image acquisition, representation and modeling2. can utilize elementary methods of machine vision for image recognition problems3. can use 2D transformations in model fitting and image registration4. can explain the basics of 3D imaging and reconstruction
1. Introduction, 2. Imaging and image representation, 3. Color and shading, 4. Image features, 5. Recognition, 6. Texture, 7. Motion from 2D image sequences, 8. 2D models and transformations, 9. Perceiving 3D from 2D images, 10. 3D transformations and reconstruction.
Face-to-face teaching, homework assignments.
Lectures (20 h), exercises (16 h) and Matlab homework assignments (30 h), self-studying (67 h).
Computer Science and Engineering students and other Students of the University of Oulu.
521467A Digital Image Processing or an equivalent course
521289S Machine Learning. This course provides complementary knowledge on machine learning methods needed in machine vision.
Lecture slides and exercise material. The following books are recommended for further information: 1) Shapiro, L.G. & Stockman, G.C.: Computer Vision, Prentice Hall, 2001. 2) Szeliski, R.: Computer Vision: Algorithms and Applications, Springer, 2011. 3) Forsyth, D.A. & Ponce, J.: Computer Vision: A Modern Approach, Prentice Hall, 2002.
The course is passed with final exam and accepted homework assignments.
Read more about assessment criteria at the University of Oulu webpage.
Numerical grading scale 1-5. Zero stands for a fail.