Course Information


This graduate course is about the fundamentals of computer vision, which is a field of artificial intelligence (AI) that applies machine learning to images and videos to understand media and make decisions about them. With computer vision, we can, in a sense, give vision to software and technology.

The aim of this course is to demonstrate students a broad understanding of computer vision, its applications and theoritical background. Moreover, our another goal is to enable them implement their simple applications in this highly popular field. The students are expected to gain a foundational understanding in machine learning. The students, moreover, will be expected to gain hand-on experience via a term project.

The course is taught by Dr. Ahmet Selman Bozkır.

Exam Dates

First Midterm: 14.4.2023 - Friday at 19:40 (Online)
Second Midterm: 26.5.2023 - Friday at 19:00
Final: Will be announced - Friday at 18:40

Time and Location

Lectures: Friday at 18:30-20:30 (Online)

Reference Books

  • Computer Vision: Algorithms and Applications, Richard Szeliski, Second Edition, 2022 (pdf available online).
  • Photography (10th edition), Barbara London, Jim Stone, and John Upton, Pearson, 2010
  • Computer Vision: A Modern Approach (2nd Edition), David Forsyth and Jean Ponce, Prentice Hall, 2012.

Policies: All work on project must be done individually unless stated otherwise. You are encouraged to discuss with your classmates about the given project, 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. The conducted study must be reported in a suitable format and be sent through email.


The course webpage will be updated regularly throughout the semester with lecture notes, presentations, and important deadlines. Since this class wil be taught online, it is obligatory to register Piazza VBM686 communication group

Course Requirements and Grading

Grading for VBM686 will be based on

  • Midterm exam 1 (20%),
  • Midterm exam 2 (20%),
  • Project (25%),
  • Final exam (35%).


Date Topic Notes
Mar 3 Introduction [slides] Welcome, Introduction to Computer Vision, General Concepts
Mar 10 Image Acquasition [slides] In Camera Pipeline, White Balancing, Color, Denoising, RAW Imaging
Mar 17 Image Filtering [slides] Image Filtering,Filter Types, Convolution, Correlation, Image Gradients, LoG and DoG
Mar 24 Bilateral Filtering [slides] Bilateral Filtering, Flash/No Flash Photography
Mar 31 Guided Image Filtering [slides] Guided Image Filtering with Examples
Apr 7 Color Spaces and Features [slides] Color Spaces, Global Image Features, Histogram Representations
Apr 14 1st Midterm No class
Apr 21 Official Holiday No class
Apr 28 Local Features: SIFT [slides] SIFT Features, Scale Space, Use of DOG, Image Matching
May 5-12 Convolutional Neural Networks [slides] MLP, CNNs, Fully Convolutional Networks, Parameter Computatation, Applications


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 International Conference on Computational Photography (ICCP)
  • Advances in Neural Information Processing Systems (NeurIPS)
  • International Conference on Learning Representations (ICLR)

Reference Journals

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

Python Resources

Linear Algebra