BBM 413: Fundamentals of Image Processing

Fall 2017

Mark I Perceptron
Draughtsman Drawing a Lute, Albrecht Drer, 1525

Course Information

Course Description

The subject matter of this advanced undergraduate course is about the fundamentals of image processing. The course is structured around key topics in image processing, including image formation, point operations and histogram processing, spatial filtering techniques, frequency domain approaches, image smoothing, edge detection and image segmentation. The main aim of this course is to provide an introduction to students who wish to specialize in interrelated disciplines like image processing, computer vision and computational photography. The students are expected to develop a foundational understanding and knowledge of concepts that underly image processing and related fields. The students will also be expected to gain hand-on experience via a set of programming assignments supplied in the complementary BBM 415 Image Processing Practicum.

Time and Location

Lectures: Tuesdays at 09:00-11:50 (Room D8)

Practicum: Thursdays at 15:00-17:00 (Room D10)

Course Instructor

Erkut Erdem's avatar

Erkut Erdem

Office Hour:Tuesday 14:00-15:00

Teaching Assistants

Aysun Kocak's avatar

Aysun Kocak



Office Hour: To be announced..

Efsun Sefa Sezer's avatar

Efsun Sefa Sezer


Office Hour: To be announced..


The course webpage will be updated regularly throughout the semester with lecture notes, programming and reading assignments and important deadlines. All other course related communications will be carried out through Piazza. Please enroll it by following the link


Basic probability, linear algebra and calculus. Good programming skills.

Course Requirements and Grading

Grading for BBM 413 will be based on

  • a set of written assignments (5%),
  • pop-up quizzes (9%),
  • a course project (done in pairs) (16%),
  • a midterm exam (30%), and
  • a final exam (40%).
In BBM 415, the grading will be based on
  • five programming assignments (done individually).


All work on assignments must be done individually unless stated otherwise. You are encouraged to discuss with your classmates about the given assignments, but these discussions should be carried out in an abstract way. That is, discussions related to a particular solution to a specific problem (either in actual code or in the pseudocode) will not be tolerated.

In short, turning in someone else’s work, in whole or in part, as your own will be considered as a violation of academic integrity. Please note that the former condition also holds for the material found on the web as everything on the web has been written by someone else.

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Reference Books

  • (S) Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, 2010 (draft available online).

  • (GW) Digital Image Processing, R. C. Gonzalez, R. E. Woods, 3rd Edition, Prentice Hall, 2008

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Week Date Topic From the book Notes
1 Sep 26 Introduction S1 Slides: (pdf, 4pp)
Reading: D. Marr, Vision, The Philosophy and the Approach, 1982
Lab material: (zip)
2 Oct 3 Image formation and color S2.1-2.3.1, S2.3.2 Slides: (pdf, 4pp)
Reading: Beau Lotto's TED Talk: Optical illusions show how we seeLab material: (zip-file)
3 Oct 10 Point operations S3.1, GW3.1-3.3 PA1 out: (github repository)
Slides: (pdf, 4pp)
Lab material: (zip-file)
4 Oct 17 Spatial filtering S3.2-3.3 Slides: (pdf, 4pp)
5 Oct 24 Frequency Domain Techniques S3.4, GW4.1-4.10 PA1 due, PA2 out: (github repository)
Slides: (pdf, 4pp)
Lab material: (zip-file)
6 Oct 31 Frequency Domain Techniques (cont'd.) S3.4, GW4.1-4.10 Slides: (pdf, 4pp)
7 Nov 7 Image pyramids and wavelets S3.5, GW7.1-7.5 PA2 due
Slides: (pdf, 4pp)
Reading: A. Oliva, A. Torralba, P.G. Schyns, Hybrid Images, ACM Transactions on Graphics, ACM SIGGRAPH, 25-3, 527-530, 2006
Applications: Eulerian Video Magnification, Phase-Based Video Motion Processing
Lab material: (zip)
8 Nov 14 Midterm Exam PA3 out: (github repository)
9 Nov 21 Gradients, edges, contours S4.2,4.3.1-4.3.2 Slides: (pdf, 4pp)
Reading: D. Marr and E. Hildreth, Theory of Edge Detection, Proc. R. Soc. Lond. B, 1980
10 Nov 28 Image segmentation S5.1-S5.2 PA3 due, PA4 out: (github repository)
Slides: (
pdf, 4pp)
Reading: E. Borenstein and S. Ullman, Class-Specific, Top-down Segmentation, ECCV 2002
11 Dec 5 Image segmentation (cont'd) S5.3-5.5 Slides: (pdf, 4pp)
12 Dec 12 Image smoothing - revisited S3.2 PA4 due, PA5 out: (zip)
Slides: (pdf, 4pp)
13 Dec 19 Advanced topics (Visual Saliency) Slides: (pdf, 4pp)
14 Dec 26 Advanced topics PA5 due

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Course Project

The students taking the course are required to complete a project. The students can work individually or in pairs to apply their newly acquired skills towards developing a photo editing tool with a selection of filters they devise.

For a detailed description of the course project and the related schedule, see this page. In preparing your progress and final project reports, you should use the provided LaTeX template and submit them electronically in PDF format.

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Related Conferences

  • IEEE International Conference on Computer Vision (ICCV)
  • European Conference on Computer Vision (ECCV)
  • IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • IEEE Winter Conference on Applications of Computer Vision (WACV)
  • British Machine Vision Conference (BMVC)
  • Advances in Neural Information Processing Systems (NIPS)
  • IEEE International Conference on Pattern Recognition (ICPR)
  • IEEE International Conference on Image Processing (ICIP)

Related Journals

  • IEEE Transactions on Image Processing (IEEE TIP)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI)
  • ACM Transactions on Graphics (ACM TOG)
  • International Journal of Computer Vision (IJCV)
  • Computer Vision and Image Understanding (CVIU)
  • Image and Vision Computing (IMAVIS)

Matlab Resources

Linear Algebra

Resources for scientific writing and talks

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