Image Analysis and Pattern Recognition

Description

This course provides students with a sound background in digital image processing and also in pattern recognition. Topics include image processing and analysis in the spatial and frequency domains, image restoration and compression, image segmentation, morphological image processing, representation and description. Fundamentals of pattern recognition are also covered like: Bayes decision theory, parametric and non-parametric classifiers, feature extraction and ion techniques. rnMain concepts used in structural and statistical pattern recognition are also covered. Many applications in image analysis are also presented during the course like: object recognition, signature verification, face recognition, document analysis and many others.

Program

Master in Computer Science

Objectives

• After this course, the student should be able to:
  • Manipulate different types of images and extract the main features for his applications.
  • Design digital filter algorithms and their mathematical morphology equivalents for image processing.
  • Understand and recognize different models for handling the problem of data analysis starting from feature extraction, reduction and representation to building complex algorithms for efficient processing of different data types.