The course aims to give students the theoretical and practical skills for the design and development of algorithms for the processing of digital images.
Course Prerequisites
Knowledge acquired in previous courses in mathematics.
Teaching Methods
The image processing course is based on lectures, examples, exercises and analysis of case studies of digital image processing applications
Lectures (hours/year in lecture theatre): 16 Practical classes (hours/year in lecture theatre): 12 Workshops (hours/year in the lab): 0
The lectures are given using slides. The practical classes consist in the solution of MATLAB/Python exercises related to the couse content.
Assessment Methods
The final exam is a written test devoted to score the student knowledge by means of a mixture of open-ended and closed-ended questions, covering the course program.
Some, non mandatory, assignments will be provided. Submitting them will provide extra points on the final evaluation.
The minimum score to pass the exam is 18, the top score is 30 cum laude.
PDF of the slides will be provided by the professors.
Contents
1 A background on visual perception, human vision vs. artificial vision, color perception. Image sampling and quantization. 2 Image enhancement using intensity transformation functions. 3 Spatial image filtering using liner and non-liner filters. 4 Color spaces. Color image processing. 5 Texture analysis 6 Supervised and unsupervised pixel classification
