Convolutional Neural Networks in Computer Vision

Professor

Jochen Lang

Contact
  • jlang@uottawa.ca
  • Office hours: Wednesdays 11:30-12:30
  • Office: STE-5098, 800 King Edward Ave, Ottawa, On., Canada

General and Specific Objectives of the Course

The recent surge in machine learning and in particular deep learning using neural network has revolutionized many fields including speech processing, data mining and medicine. Arguably one of the greatest impacts of this revolution is in computer vision. Since the success of AlexNet at the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) where the deep neural network solution outperformed, by a significant margin, arguably much more advanced classical computer vision systems, deep neural networks can now be found everywhere in visual processing. This revolution has created enormous economic interest (Facebook, Google, …). The OCICS offering contains various courses that cover neural networks from a machine learning or data mining perspective but there is no dedicated course of machine learning in computer vision. This topics course is to fill this gap. While it necessarily includes machine learning background, it specifically looks at (convolutional) neural networks and their applications to standard problems in computer vision. It will also contrast the deep-learning based approaches to classical computer vision approaches and how classical approaches inform the design of these deep-learning based solutions.


Calendar Description

Introduction to deep-learning in computer vision; statistical learning background, linear regression and classification; neural networks basics of feed forward networks, backpropagation and stochastic gradient descent; image processing and filtering primer; convolutional neural networks (CNNs), network layers, visualizing networks; (supervised) training and computer vision data sets and competitions; software for machine learning in computer vision; computer vision problems, in particular, image classification, detection and recognition, problem-specific detectors, multi-view problems and video object segmentation and tracking.

Course Prerequisites: None

Teaching Methods and Student Expectations

The course covers the fundamentals of deep neural networks in computer vision in lectures using multimedia support including program demonstrations and videos. The fundamentals are reviewed in the final exam. The active participation of students is encouraged through discussions and the student project presentations at the end of the course. The students are encouraged to apply their knowledge through three programming assignments.


Recommended Textbooks and Additional Resources


Course Topics and Readings

Course notes will be made available through Virtual Campus.

Student Evaluation

Student evaluation will be based on assignments, a project and a final exam.

Marking Scheme

The maximum is 100 marks*) with the following breakdown:

3 Programming assignments (using Tensorflow, Jupyter notebooks)
  • Linear and logistic regression
  • Image recognition
  • Transfer learning for small data-sets
24 marks
Project including oral presentation(s)

Project can be done in groups of up to 3. For group projects two presentations are required.

36 marks
Final exam

The final exam will be closed book.

40 marks

Reminder: Academic Regulations

Class attendance is mandatory. As per academic regulations, students who do not attend 80% of the class may not be allowed to write the final examinations.

All components of the course (i.e. assignments, projects, etc.) must be fulfilled otherwise students may receive an INC as a final mark (equivalent to an F). This also holds for a student who is taking the course for the second time.


Academic Fraud and Plagiarism

Any form of plagiarism or fraud including on an assignment or the project will be reported.

For any plagiarism or fraud the university regulation on academic fraud applies. The plagiarism rules explains the University of Ottawa rules. Please familiarize yourself with them.