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

Course grade breakdown

type	points
Labs/seminar classes	20
Interim performance assessment	50
Exams	30

Grades range

Course grading 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:

Section 1

Section title

Image Acquisition and Basic Image Processing

Topics covered in this section

• Computer vision in action
• The Human Vision System
• Optical Illusions
• Sampling and Quantization
• Image Representation
• Colour Spaces

What forms of evaluation were used to test students’ performance in this section?

Form	Yes/No
Development of individual parts of software product code	1
Homework and group projects	1
Midterm evaluation	1
Testing (written or computer based)	1
Reports	0
Essays	0
Oral polls	0
Discussions	1

Typical questions for ongoing performance evaluation within this section

1. What are the color spaces and where it's used?
2. What are the primary and secondary colors?
3. How image is formed into computers?
4. How you will convert the RGB to grayscale images

Typical questions for seminar classes (labs) within this section

1. Loading and plotting the images in python environment
2. Convertion of different color spaces
3. How you find the skin in the images based on the color space models
4. how to find red eye dot in face using color space models

Test questions for final assessment in this section

1. How you can distinguish different color spaces?
2. Explain and provide the reason for the blind spot creation in human eye.
3. In what scenarios computer vision is better than human vision?
4. Write down different robotic application areas where computer vision is applied successfully.