Content, progress and pedagogy of the module

Purpose:
The purpose of this course is to equip the student with knowledge and skills about how to analyse the content of data, especially images and video, and how to make decisions based on the analysis.

Learning objectives

Knowledge

• Must have knowledge about the building blocks in a generic classification system
• Must have knowledge about different colour representations
• Must be able to understand the principles of point- and neighbourhood processing
• Must be able to understand what a BLOB is and  how it can be extracted
• Must be able to understand how moving objects can be segmented in a video sequence
• Must be able to understand the concept of a multidimensional feature-space.
• Must be able to understand the principle behind Bayes rule and how a classifier can be derived here from
• Must be able to understand how to assess a classification system

Skills

• Must be able to apply point processing methods like grey-level mapping, histogram stretching, thresholding and image arithmetic
• Must be able to apply neighbourhood processing methods like median filter, mean filter and edge detection
• Must be able to apply morphologic operations like erosion, dilation opening and closing
• Must be able to suggest/select relevant features and methods for extracting these
• Must be able to apply Mahalanobis distance
• Must be able to apply dimensionality reduction methods to a feature space

Competences

• Must be able to design and implement processing methods to solve a give problem
• Must be able to design and implement a simple classification system

Type of instruction

See the general description of the types of instruction described in the introduction to Chapter 3.

Exam

Exams