MSc: Computer Vision

Computer Vision

• Course name: Computer Vision
• Code discipline: R-03
• Subject area:

Short Description

This course covers the following concepts: Computer vision techniques; Classical and deep learning models.

Course Topics

Course Sections and Topics

Section	Topics within the section
Image Acquisition and Basic Image Processing	Computer vision in action The Human Vision System Optical Illusions Sampling and Quantization Image Representation Colour Spaces
Image Filtering and Binary Vision	Image noise Convolutions and kernels Smoothing and blurring Thresholding and histograms Morphological operations Gradients and Edge detection
Feature Extractors and Descriptors	Histogram of Gradients (HoG) Scale-invariant feature transform (SIFT) Harris corner detector Template matching Bag of visual words Face Detection and Recognition (Viola Johns)
Deep learning models for computer vision	You Only Look Once: Unified, Real-Time Object Detection (YOLO) Generative Adversarial Networks (GAN) Fully Convolutional Networks (FCN) for semantic segmentation Multi Domain Network (MDNet) for object tracking Generic Object Tracking Using Regression Networks (GOTURN) for object tracking

Intended Learning Outcomes (ILOs)

What is the main 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.

ILOs defined at three levels

Level 1: What concepts should a student know/remember/explain?

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

Level 2: What basic practical skills should a student be able to perform?

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

Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios?

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

Grading

Course grading range

Grade	Range	Description of performance
A. Excellent	90-100	-
B. Good	75-89	-
C. Satisfactory	60-74	-
D. Poor	0-59	-

Course activities and grading breakdown

Activity Type	Percentage of the overall course grade
Labs/seminar classes	20
Interim performance assessment	50
Exams	30

Resources, literature and reference materials

Open access resources

• Handouts supplied by the instructor
• Materials from the interment and research papers shared by instructor

Teaching Methodology: Methods, techniques, & activities

Activities and Teaching Methods

Activities within each section

Learning Activities	Section 1	Section 2	Section 3	Section 4
Development of individual parts of software product code	1	1	1	1
Homework and group projects	1	1	1	1
Midterm evaluation	1	1	1	1
Testing (written or computer based)	1	1	1	1
Discussions	1	1	1	1

Formative Assessment and Course Activities

Ongoing performance assessment

Section 1

Activity Type	Content	Is Graded?
Question	What are the color spaces and where it’s used?	1
Question	What are the primary and secondary colors?	1
Question	How image is formed into computers?	1
Question	How you will convert the RGB to grayscale images	1
Question	Loading and plotting the images in python environment	0
Question	Convertion of different color spaces	0
Question	How you find the skin in the images based on the color space models	0
Question	how to find red eye dot in face using color space models	0

Section 2

Activity Type	Content	Is Graded?
Question	What are the challenges to perform histogram task?	1
Question	Apply convolutional filter to calculate the response.	1
Question	What kind of parameters are required to apply different image filters?	1
Question	How you will compute the gradients of the image and its benefits?	1
Question	Implement Otsu Method	0
Question	Implement Sobel, Preweitt filters	0
Question	Implement Canny edge detector	0
Question	Perform analysis over the different filtering on the given images	0

Section 3

Activity Type	Content	Is Graded?
Question	How feature extractor works over the given image?	1
Question	What is the difference between the feature extraction and descriptors?	1
Question	Explain the examples of descriptors and feature extractors.	1
Question	Write down the pros and cons of SIFT, HOG and Harris.	1
Question	Implement template matching algorithm	0
Question	Implement histogram of gradient using CV2 library	0
Question	Implement of SIFT for the given task	0
Question	Implement Harris corner detection	0
Question	Analysis of different extractors for the given task	0

Section 4

Activity Type	Content	Is Graded?
Question	How classification task is different from detection task?	1
Question	Explain the transfer learning mechanism for object detection task.	1
Question	How many types of model exist for object tracking in videos.	1
Question	Write down the pros and cons of YOLO, FCN and MDNet.	1
Question	Implement YOLO using transfer learning mechanism	0
Question	Implement GAN for MNIST dataset	0
Question	Implement FCN and GOTURN	0
Question	Analysis of different models for the given task	0

Final assessment

Section 1

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.

Section 2

1. Calculate the kernels for the given images
2. Explain the difference between different filters
3. What is image noise and how it contributes to make the computer vision task difficult?
4. Apply different combination of the filters to achieve the required output of the given image.

Section 3

1. How you distinguish different feature extractors and descriptors?
2. What are the possible methods to detect the corners?
3. How corners are useful to help the robotic vision task?
4. How you will patch the different images to construct the map of the location?

Section 4

1. What are the loss functions used in YOLO?
2. What are the learnable parameters of FCN for semantic segmentation?
3. How semantic segmentation is different from instance segmentation?
4. Write the application areas for object tracking in robotics.

The retake exam

Section 1

Section 2

Section 3

Section 4