Module-ID: M304

Title: Automatic Image Analysis

Lecturer
Prof. Dr. Hauke Schramm

Teaching Method
Lectures, Practical Exercises 

Assessment
Written Examinations, Lab Assignments

Aims and Objectives
The aim of the course is to provide both fundamental understanding and practical knowledge of sophisticated image analysis techniques for independently applying research and development in this field. To this end, theoretical foundations will be encouraged by a strong practical focus with various appropriate examples in the lecture and laboratory. At the end of the course the student should be able to build up, run and evaluate an automatic image analysis system, e.g. for object detection, using Matlab. Beside the regular practical exercises each student will therefore be part of a small project group that develops and presents an image analysis system for a given task.

• Contact Hours:
  Presence in lecture and laboratory: 60 hours
• Self-Study:
  Preparation and wrap-up: 80 hours
  Exam preparation: 30 hours
  System development: 40 hours
  Final presentation: 15 hours

• Matlab basics
• Knowledge based image analysis
• Object detection using the Generalized Hough Transform
• Region-Growing
• Extrema and ridge detection
• Model-based Segmentation
• Markov Random Fields
• Industrial and medical applications of image analysis technique

1. Rafael C. Gonzales, Richard E. Woods: Digital Image Processing. Prentice-Hall Inc., 2001, ISBN 0-130-94650-8.
2. Yalit Amit: 2D Objekt Detection and Recognition Models, Algorithms, and Networks. MIT-Press, ISBN-10: 0-262-01194-8.
3. Dana H. Ballard, Christopher M. Brown: Computer Vision. Online book: homepages.inf.ed.ac.uk/rbf/BOOKS/BANDB/bandb.htm.

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