IU:TestPage
Computer Vision
- Course name: Computer Vision
- Course number: R-03
Course Characteristics
Key concepts of the class
- Computer vision techniques
- Classical and deep learning models
What is the purpose of this course?
This course provides an intensive treatment of a cross-section of the key elements of computer vision, with an emphasis on implementing them in modern programming environments, and using them to solve real-world problems. The course will begin with the fundamentals of image processing and image filtering, but will quickly build to cover more advanced topics, including image segmentation, object detection and recognition, face detection, content-based image retrieval, artificial neural networks, convolutional neural networks, generative adversarial networks and much more. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.
Course objectives based on Bloom’s taxonomy
- What should a student remember at the end of the course?
By the end of the course, the students should be able to
- Robots visual perception strategies
- Significant exposure to real-world implementations
- To develop research interest in the theory and application of computer vision
- What should a student be able to understand at the end of the course?
By the end of the course, the students should be able to
- Suitability of different computer vision models in different scenarios
- Ability to choose the right model for the given task
- What should a student be able to apply at the end of the course?
By the end of the course, the students should be able to
- Hands on experience to implement different models to know inside behavior
- Sufficient exposure to train and deploy model for the given task
- Fine tune the deployed model in the real-world settings
Course evaluation
| type | points |
|---|---|
| Labs/seminar classes | 20 |
| Interim performance assessment | 50 |
| Exams | 30 |
Grades range
| grade | low | high |
|---|---|---|
| A | 90 | 100 |
| B | 75 | 89 |
| C | 60 | 74 |
| D | 0 | 59 |
Resources and reference material
- Handouts supplied by the instructor
- Materials from the interment and research papers shared by instructor
- \bibentry{szeliski2010computer}
- \bibentry{vernon1991machine}
Course Sections
The main sections of the course and approximate hour distribution between them is as follows: