Course Information


This course provides a comprehensive overview of fundamental topics in image processing for graduate students. The goal is to give a deeper understanding of the state-of-the-art methods in image processing literature and to study their connections. In this context, the course makes the students gain knowledge and skills in key topics and provides them the ability to employ them in their advanced-level studies. The course is taught by Erkut Erdem.

Instruction style: During the semester, students are responsible for studying and keeping up with the course material outside of class time. These may involve reading particular book chapters, or papers. After the first three lectures, each week a student will present a paper related to the topics of the week.

Time and Location

Lectures: Thursday at 09:30-12:30 (Room D8)


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


CMP717 is open to all graduate students in our CENG department. Prospective senior undergraduate students may sit in on the class. Non-CENG graduate students, however, should ask the course instructor for approval before the add/drop period. The prerequisites for this course are:

  • Programming (you should be a proficient programmer to work out the practicals and to implement your course project.)
  • Calculus (differentiation, chain rule) and Linear Algebra (vectors, matrices, eigenvalues/vectors)
  • Basic Probability and Statistics (random variables, expectations, multivariate Gaussians, Bayes rule, conditional probabilities)
  • Image Processing (you can still survive this course without an introductory level image processing course such as BBM413 Fundamentals of Image Processing before, but it is highly recommended.)
  • Machine Learning (you can still survive this course without a machine learning course before, but it is highly recommended. Some introductory ML courses are BBM406 Fundamentals of Machine Learning and CMP712 Machine Learning.)
  • Optimization (cost functions, taking gradients, regularization)


Grading for CMP717 will be based on

  • Midterm Exam (20%).
  • Practicals (24%) (3 practicals x 8% each)
  • Course Project (presentations and reports) (32%),
  • Paper Presentations (14%),
  • Weekly Quizzes (10%),


Date Topic Assignments
Feb 15 Introduction
Feb 22 Linear Filtering, Edge/Boundary Detection, Image Segmentation Practical 1 out
Mar 1 Nonlinear Filtering, Snakes, Variational Segmentation Models
Mar 8 Modern Image Filtering Practical 1 due
Mar 15 Sparse coding Practical 2 out
Mar 22 Graphical Models Project proposals due
Mar 29 Deep Learning Basics Practical 2 due, Practical 3 out
Apr 5 Convolutional Neural Networks
Apr 12 Semantic Segmentation Practical 3 due
Apr 19 Image Deblurring
Apr 26 Visual Saliency Project progress reports due
May 3 Midterm Exam
May 10 Deep Generative Networks
May 17 Final Project Presentations
Detailed Syllabus and Lectures


Reference Books

  • Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, 2010 (draft available online)
  • Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations, G. Aubert and P. Kornprobst, 2nd Edition, Springer-Verlag, 2006.
  • Markov Random Fields for Vision and Image Processing, Andrew Blake, Pushmeet Kohli, Carsten Rother, The MIT Press, 2011.
  • Deep Learning, Ian Goodfellow, Aaron Courville, and Yoshua Bengio, book in preparation for MIT Press (draft available online)

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)
  • International Conference on Learning Representations (ICLR)
  • IEEE International Conference on Pattern Recognition (ICPR)
  • IEEE International Conference on Image Processing (ICIP)

Reference 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)
  • Pattern Recognition (PR)

MATLAB Resources

Linear Algebra

Deep Learning

Resources for scientific writing and talks