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Course module: 800877-M-3
800877-M-3
Research Skills: Image Analysis
Course info
Course module800877-M-3
Credits (ECTS)3
CategoryMA (Master)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Humanities and Digital Sciences; TSH: Department Cognitive Science and AI; TSH: Department Cognitive Science and AI;
Is part of
M Communication and Information Sciences
M Data Science and Society
Lecturer(s)
Lecturer
dr. L.L. Ong
Other course modules lecturer
Academic year2019
Starting block
BLOK 1
Course mode
Full-time
Remarks-
Registration openfrom 20/08/2019 up to and including 18/10/2019
Aims
Upon successful completion of the requirements for this course, students will be familiar with basic image processing techniques for solving real problems. They will be able to describe the basic issues and scope of image processing and the role of image analysis in a variety of applications.  

Learning Objectives:
At the end of the course, students will be able to:
  • select appropriate methods for image analysis,
  • use existing functions and software packages to implement these methods and develop their own code for the problems,
  • evaluate and benchmark any image analysis task and communicate the results,
  • identify potential applications for image analysis in many disciplines.

 
Content
By 2022, there will be 45 billion cameras in the world. Many of these cameras are tiny, connected and yield enormous streams of images, allowing communication, observation and interaction.  There will be a rise in the need for many visual technologies for businesses or consumers, including technologies that capture, analyse, filter or display images.
 
This course provides an introduction to theoretical and practical aspects of digital image analysis including the fundamentals of image formation, image representation and a broad range of basic image processing techniques and algorithms. The course explores different types of image representations, how to enhance image characteristics, image filtering and how to reduce the effects of noise in an image. It also introduces different methods used to extract features and objects in an image.  There will be less focus on the machine learning aspect of image analysis as classification theory is best learned in a machine learning course. Lectures will be complemented with interactive demonstrations and hands-on exercises to provide participants with practical experience in processing images. 

Evaluation
Practical Sessions:
Students are required to submit at least 4 out of 6 resulting scripts from the practical session worksheets. These scripts will not be graded.
 
Individual take home assignment:
The individual final exam for 40% of the grade
 
Final Exam:
The final exam which counts for 60% of the grade

“Due to limited capacity, this course is currently not open for external students.”
Course available for exchange students
Master level, conditions apply
Contact person
dr. L.L. Ong
Timetable information
Research Skills: Image Analysis
Written test opportunities
DescriptionTestBlockOpportunityDate
Written test opportunities (HIST)
DescriptionTestBlockOpportunityDate
Schriftelijk (60%) / Written (60%)EXAM_01BLOK 1114-10-2019
Schriftelijk (60%) / Written (60%)EXAM_01BLOK 1207-01-2020
Required materials
-
Recommended materials
Literature
Gonzalez and Woods, “Digital Image Processing”, 4th Edition, 2018, Prentice Hall.
ISBN:9780133356724
Title:Digital Image Processing
Author:Gonzalez and Woods
Publisher:Prentice Hall.
Edition:4
Tests
Assignment (40%)

Written (60%)

Final Result

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Kies de Nederlandse taal