Difference between revisions of "MSc: Computer Vision"

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  +
 
= 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.
   
−
* <span>'''Course name:'''</span> Computer Vision
  
−
* <span>'''Course number:'''</span> R-03
  
 
== Prerequisites ==
  
== Prerequisites ==
   
  +
=== Prerequisite subjects ===
−
== Course Characteristics ==
  
   
−
=== Key concepts of the class ===
  
   
  +
=== Prerequisite topics ===
−
* Computer vision techniques
  
−
* Classical and deep learning models
  
   
−
=== What is the purpose of this course? ===
  
   
  +
== Course Topics ==
  +
{| class="wikitable"
  +
|+ 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.
  
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 ===
−
== 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 process the video
  
   
  +
==== 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
  
* Robots visual perception strategies
 
* Significant exposure to real-world implementations
  
* Significant exposure to real-world implementations
 
* To develop research interest in the theory and application of computer vision
  
* 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? ===
 +
==== 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 ...
−  
−
By the end of the course, the students should be able to choose the correct computer vision model.
  
−  
 
* Suitability of different computer vision models in different scenarios
  
* Suitability of different computer vision models in different scenarios
 
* Ability to choose the right model for the given task
  
* Ability to choose the right model for the given task
   
−
=== What should a student be able to apply at the end of the course? ===
 +
==== 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 ...
−  
−
By the end of the course, the students should be able to deploy and developed models.
  
−  
 
* Hands on experience to implement different models to know inside behavior
  
* Hands on experience to implement different models to know inside behavior
 
* Sufficient exposure to train and deploy model for the given task
  
* Sufficient exposure to train and deploy model for the given task
−
* Fine tune the deployed model in the real-world settings
 +
* Fine tune the deployed model in the real-world settings
  +
== Grading ==
   
−
=== Course evaluation ===
 +
=== Course grading range ===
  +
{| class="wikitable"
−  
−
{|
 +
|+
−
|+ Course grade breakdown
  
−
!
  
−
!
  
−
!align="center"| '''Proposed points'''
  
 
|-
  
|-
  +
! Grade !! Range !! Description of performance
−
| Labs/seminar classes
  
−
| 20
  
−
|align="center"| 20
  
 
|-
  
|-
  +
| A. Excellent || 90-100 || -
−
| Interim performance assessment
  
−
| 30
  
−
|align="center"| 50
  
 
|-
  
|-
  +
| B. Good || 75-89 || -
−
| Exams
  
−
| 50
 +
|-
  +
| C. Satisfactory || 60-74 || -
−
|align="center"| 30
  
  +
|-
  +
| D. Poor || 0-59 || -
 
|}
  
|}
   
  +
=== Course activities and grading breakdown ===
−
=== Grades range ===
  
  +
{| class="wikitable"
−  
−
{|
 +
|+
−
|+ Course grading range
  
−
!
  
−
!
  
−
!align="center"| '''Proposed range'''
  
 
|-
  
|-
  +
! Activity Type !! Percentage of the overall course grade
−
| A. Excellent
  
−
| 90-100
  
−
|align="center"| 90-100
  
 
|-
  
|-
  +
| Labs/seminar classes || 20
−
| B. Good
  
−
| 75-89
  
−
|align="center"| 75-89
  
 
|-
  
|-
  +
| Interim performance assessment || 50
−
| C. Satisfactory
  
−
| 60-74
  
−
|align="center"| 60-74
  
 
|-
  
|-
−
| D. Poor
 +
| Exams || 30
−
| 0-59
  
−
|align="center"| 0-59
  
 
|}
  
|}
   
  +
=== Recommendations for students on how to succeed in the course ===
−
=== Resources and reference material ===
  
  +
   
  +
== Resources, literature and reference materials ==
  +
  +
=== Open access resources ===
 
* Handouts supplied by the instructor
  
* Handouts supplied by the instructor
 
* Materials from the interment and research papers shared by instructor
  
* Materials from the interment and research papers shared by instructor
−
*
 
−
*
 
   
−
== Course Sections ==
 +
=== Closed access resources ===
   
−
The main sections of the course and approximate hour distribution between them is as follows:
  
   
  +
=== Software and tools used within the course ===
−
{|
  
  +
−
|+ Course Sections
  
  +
= Teaching Methodology: Methods, techniques, & activities =
−
!align="center"| '''Section'''
  
  +
−
! '''Section Title'''
  
−
!align="center"| '''Teaching Hours'''
 +
== Activities and Teaching Methods ==
  +
{| class="wikitable"
  +
|+ Activities within each section
 
|-
  
|-
  +
! Learning Activities !! Section 1 !! Section 2 !! Section 3 !! Section 4
−
|align="center"| 1
  
−
| Image Acquisition and Basic Image Processing
  
−
|align="center"| 8
  
 
|-
  
|-
  +
| Development of individual parts of software product code || 1 || 1 || 1 || 1
−
|align="center"| 2
  
−
| Image Filtering and Binary Vision
  
−
|align="center"| 8
  
 
|-
  
|-
  +
| Homework and group projects || 1 || 1 || 1 || 1
−
|align="center"| 3
  
−
| Feature Extractors and Descriptors
  
−
|align="center"| 16
  
 
|-
  
|-
  +
| Midterm evaluation || 1 || 1 || 1 || 1
−
|align="center"| 4
  
  +
|-
−
| Deep Learning models for computer vision
  
  +
| Testing (written or computer based) || 1 || 1 || 1 || 1
−
|align="center"| 16
  
−
|}
 +
|-
  +
| Discussions || 1 || 1 || 1 || 1
  +
|}
  +
== Formative Assessment and Course Activities ==
   
−
=== Section 1 ===
 +
=== Ongoing performance assessment ===
−  
−
==== 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? ===
  
−  
−
<div class="tabular">
  
−  
−
<span>|a|c|</span> &amp; '''Yes/No'''<br />
  
−
Development of individual parts of software product code &amp; 1<br />
  
−
Homework and group projects &amp; 1<br />
  
−
Midterm evaluation &amp; 1<br />
  
−
Testing (written or computer based) &amp; 1<br />
  
−
Reports &amp; 0<br />
  
−
Essays &amp; 0<br />
  
−
Oral polls &amp; 0<br />
  
−
Discussions &amp; 1<br />
  
−  
−  
−  
−
</div>
  
−
=== Typical questions for ongoing performance evaluation within this section ===
  
−  
−
# What are the color spaces and where it’s used?
  
−
# What are the primary and secondary colors?
  
−
# How image is formed into computers?
  
−
# How you will convert the RGB to grayscale images
  
−  
−
=== Typical questions for seminar classes (labs) within this section ===
  
−  
−
# Loading and plotting the images in python environment
  
−
# Convertion of different color spaces
  
−
# How you find the skin in the images based on the color space models
  
−
# how to find red eye dot in face using color space models
  
−  
−
=== Test questions for final assessment in this section ===
  
   
  +
==== Section 1 ====
  +
{| class="wikitable"
  +
|+
  +
|-
  +
! 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 ====
  +
{| class="wikitable"
  +
|+
  +
|-
  +
! 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 ====
  +
{| class="wikitable"
  +
|+
  +
|-
  +
! 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 ====
  +
{| class="wikitable"
  +
|+
  +
|-
  +
! 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'''
 
# How you can distinguish different color spaces?
  
# How you can distinguish different color spaces?
 
# Explain and provide the reason for the blind spot creation in human eye.
  
# Explain and provide the reason for the blind spot creation in human eye.
 
# In what scenarios computer vision is better than human vision?
  
# In what scenarios computer vision is better than human vision?
 
# Write down different robotic application areas where computer vision is applied successfully.
  
# Write down different robotic application areas where computer vision is applied successfully.
  +
'''Section 2'''
−  
−
=== Section 2 ===
  
−  
−
==== Section title: ====
  
−  
−
Image Filtering and Binary Vision
  
−  
−
=== Topics covered in this section: ===
  
−  
−
* Image noise
  
−
* Convolutions and kernels
  
−
* Smoothing and blurring
  
−
* Thresholding and histograms
  
−
* Morphological operations
  
−
* Gradients and Edge detection
  
−  
−
=== What forms of evaluation were used to test students’ performance in this section? ===
  
−  
−
<div class="tabular">
  
−  
−
<span>|a|c|</span> &amp; '''Yes/No'''<br />
  
−
Development of individual parts of software product code &amp; 1<br />
  
−
Homework and group projects &amp; 1<br />
  
−
Midterm evaluation &amp; 1<br />
  
−
Testing (written or computer based) &amp; 1<br />
  
−
Reports &amp; 0<br />
  
−
Essays &amp; 0<br />
  
−
Oral polls &amp; 0<br />
  
−
Discussions &amp; 1<br />
  
−  
−  
−  
−
</div>
  
−
=== Typical questions for ongoing performance evaluation within this section ===
  
−  
−
# What are the challenges to perform histogram task?
  
−
# Apply convolutional filter to calculate the response.
  
−
# What kind of parameters are required to apply different image filters?
  
−
# How you will compute the gradients of the image and its benefits?
  
−  
−
=== Typical questions for seminar classes (labs) within this section ===
  
−  
−
# Implement Otsu Method
  
−
# Implement Sobel, Preweitt filters
  
−
# Implement Canny edge detector
  
−
# Perform analysis over the different filtering on the given images
  
−  
−
=== Test questions for final assessment in this section ===
  
−  
 
# Calculate the kernels for the given images
  
# Calculate the kernels for the given images
 
# Explain the difference between different filters
  
# Explain the difference between different filters
 
# What is image noise and how it contributes to make the computer vision task difficult?
  
# What is image noise and how it contributes to make the computer vision task difficult?
 
# Apply different combination of the filters to achieve the required output of the given image.
  
# Apply different combination of the filters to achieve the required output of the given image.
  +
'''Section 3'''
−  
−
=== Section 3 ===
  
−  
−
==== Section title: ====
  
−  
−
Feature Extractors and Descriptors
  
−  
−
==== Topics covered in this section: ====
  
−  
−
* Histogram of Gradients (HoG)
  
−
* Scale-invariant feature transform (SIFT)
  
−
* Harris corner detector
  
−
* Template matching
  
−
* Bag of visual words
  
−
* Face Detection and Recognition (Viola Johns)
  
−  
−
=== What forms of evaluation were used to test students’ performance in this section? ===
  
−  
−
<div class="tabular">
  
−  
−
<span>|a|c|</span> &amp; '''Yes/No'''<br />
  
−
Development of individual parts of software product code &amp; 1<br />
  
−
Homework and group projects &amp; 1<br />
  
−
Midterm evaluation &amp; 1<br />
  
−
Testing (written or computer based) &amp; 1<br />
  
−
Reports &amp; 0<br />
  
−
Essays &amp; 0<br />
  
−
Oral polls &amp; 0<br />
  
−
Discussions &amp; 1<br />
  
−  
−  
−  
−
</div>
  
−
=== Typical questions for ongoing performance evaluation within this section ===
  
−  
−
# How feature extractor works over the given image?
  
−
# What is the difference between the feature extraction and descriptors?
  
−
# Explain the examples of descriptors and feature extractors.
  
−
# Write down the pros and cons of SIFT, HOG and Harris.
  
−  
−
==== Typical questions for seminar classes (labs) within this section ====
  
−  
−
# Implement template matching algorithm
  
−
# Implement histogram of gradient using CV2 library
  
−
# Implement of SIFT for the given task
  
−
# Implement Harris corner detection
  
−
# Analysis of different extractors for the given task
  
−  
−
==== Test questions for final assessment in this section ====
  
−  
 
# How you distinguish different feature extractors and descriptors?
  
# How you distinguish different feature extractors and descriptors?
 
# What are the possible methods to detect the corners?
  
# What are the possible methods to detect the corners?
 
# How corners are useful to help the robotic vision task?
  
# How corners are useful to help the robotic vision task?
 
# How you will patch the different images to construct the map of the location?
  
# How you will patch the different images to construct the map of the location?
  +
'''Section 4'''
  +
# What are the loss functions used in YOLO?
  +
# What are the learnable parameters of FCN for semantic segmentation?
  +
# How semantic segmentation is different from instance segmentation?
  +
# Write the application areas for object tracking in robotics.
   
−
=== Section 4 ===
 +
=== The retake exam ===
  +
'''Section 1'''
   
−
==== Section title: ====
 +
'''Section 2'''
   
  +
'''Section 3'''
−
Deep learning models for computer vision
  
   
  +
'''Section 4'''
−
==== Topics covered in this section: ====
  
−  
−
* 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
  
−  
−
=== What forms of evaluation were used to test students’ performance in this section? ===
  
−  
−
<div class="tabular">
  
−  
−
<span>|a|c|</span> &amp; '''Yes/No'''<br />
  
−
Development of individual parts of software product code &amp; 1<br />
  
−
Homework and group projects &amp; 1<br />
  
−
Midterm evaluation &amp; 1<br />
  
−
Testing (written or computer based) &amp; 1<br />
  
−
Reports &amp; 0<br />
  
−
Essays &amp; 0<br />
  
−
Oral polls &amp; 0<br />
  
−
Discussions &amp; 1<br />
  
−  
−  
−  
−
</div>
  
−
=== Typical questions for ongoing performance evaluation within this section ===
  
−  
−
# How classification task is different from detection task?
  
−
# Explain the transfer learning mechanism for object detection task.
  
−
# How many types of model exist for object tracking in videos.
  
−
# Write down the pros and cons of YOLO, FCN and MDNet.
  
−  
−
==== Typical questions for seminar classes (labs) within this section ====
  
−  
−
# Implement YOLO using transfer learning mechanism
  
−
# Implement GAN for MNIST dataset
  
−
# Implement FCN and GOTURN
  
−
# Analysis of different models for the given task
  
−  
−
==== Test questions for final assessment in this section ====
  
−  
−
# What are the loss functions used in YOLO?
  
−
# What are the learnable parameters of FCN for semantic segmentation?
  
−
# How semantic segmentation is different from instance segmentation?
  
−
# Write the application areas for object tracking in robotics.
  

Latest revision as of 11:43, 29 August 2022

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.

Prerequisites

Prerequisite subjects

Prerequisite topics

Course Topics

Course Sections and Topics
Section Topics within the section
Image Acquisition and Basic Image Processing
  1. Computer vision in action
  2. The Human Vision System
  3. Optical Illusions
  4. Sampling and Quantization
  5. Image Representation
  6. Colour Spaces
Image Filtering and Binary Vision
  1. Image noise
  2. Convolutions and kernels
  3. Smoothing and blurring
  4. Thresholding and histograms
  5. Morphological operations
  6. Gradients and Edge detection
Feature Extractors and Descriptors
  1. Histogram of Gradients (HoG)
  2. Scale-invariant feature transform (SIFT)
  3. Harris corner detector
  4. Template matching
  5. Bag of visual words
  6. Face Detection and Recognition (Viola Johns)
Deep learning models for computer vision
  1. You Only Look Once: Unified, Real-Time Object Detection (YOLO)
  2. Generative Adversarial Networks (GAN)
  3. Fully Convolutional Networks (FCN) for semantic segmentation
  4. Multi Domain Network (MDNet) for object tracking
  5. 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

Recommendations for students on how to succeed in the course

Resources, literature and reference materials

Open access resources

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

Closed access resources

Software and tools used within the course

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

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