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Course Information

Course Name
Turkish Bilgisayarla Görü
English Computer Vision
Course Code
BLG 453E Credit Lecture
Semester 1
2 2 - -
Course Language English
Course Coordinator Gözde Ünal
Course Objectives - Introduce students to the artificial vision topics that are a part of the artificial intelligence field
- Describe algorithms in the analysis of 2D and 3D data which are required for “making computers see” and interpret data
- Cover the basic image processing and computer vision techniques mathematically as well as with applications
- Implement basic image analysis algorithms in computer vision field using computers
Course Description The aim of the course is to study computer vision, which tries to “make computers see and interpret” using the observations in the form of multiple 2D (or 3D) images. In this undergraduate level course, the focus is on mainly 2D image processing fundamentals and basic computer vision concepts. The course will provide the participants with a background in Computer Vision both in practical aspects as being able to implement computer vision algorithms, and their mathematical understanding.
Course Outcomes 1. Learn and discuss the main problems of computer (artificial) vision, and the uses and applications of computer vision
2. Design and implement various image transforms: point-wise transforms, neighborhood operation-based spatial filters, and geometric (coordinate) transforms over images
3. Define, construct, and apply segmentation, feature extraction, and visual motion estimation algorithms to extract relevant information from 2D or higher dimensional images
4. Construct least squares solutions to problems in computer vision
5. Describe the idea behind dimensionality reduction for high-dimensional datasets and how it is used and applied in data processing
6. Know and apply object and shape recognition approaches to problems in computer vision
Pre-requisite(s) -
Required Facilities Personal laptop (also if possible smart phone and tablet)
Textbook 1. Concise Computer Vision: An Introduction into Theory and Algorithms, Springer, Series: Undergraduate Topics in Computer Science, by Reinhard Klette, 2014. ISBN 978-1-4471-6319-0 ;
2. Digital Image Processing, R.C. Gonzalez, R.E. Woods, Pearson Prentice Hall 2008.
3. Computer and Machine Vision (Theory, Algorithms, Practicalities), E.R. Davies, Academic Press, 4th Ed., 2012.
Other References 4. R. Szeliski’s Computer Vision Book (2010) can be downloaded from http://szeliski.org/Book/

5. http://www.computervisiononline.com/

6. Image Processing, Analysis, and Machine Vision, 2008, M. Sonka, V. Hlavac, R. Boyle

7. Digital Image Processing, B. Jahne, Springer, 2002.
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