Module Specification

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
1. Module Title Computer Vision
2. Module Code COMP338
3. Year Session 2023-24
4. Originating Department Computer Science
5. Faculty Fac of Science & Engineering
6. Semester First Semester
7. CATS Level Level 6 FHEQ
8. CATS Value 15
9. Member of staff with responsibility for the module
Dr G Cheng Computer Science Guangliang.Cheng@liverpool.ac.uk
10. Module Moderator
11. Other Contributing Departments  
12. Other Staff Teaching on this Module
Mrs J Birtall School of Electrical Engineering, Electronics and Computer Science Judith.Birtall@liverpool.ac.uk
13. Board of Studies
14. Mode of Delivery
15. Location Main Liverpool City Campus
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
16. Study Hours 30

    10

    40
17.

Private Study

110
18.

TOTAL HOURS

150
 
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other
19. Timetable (if known)            
 
20. Pre-requisites before taking this module (other modules and/or general educational/academic requirements):

COMP122 Object-Oriented Programming; COMP116 Analytic Techniques for Computer Science
21. Modules for which this module is a pre-requisite:

 
22. Co-requisite modules:

 
23. Linked Modules:

 
24. Programme(s) (including Year of Study) to which this module is available on a mandatory basis:

25. Programme(s) (including Year of Study) to which this module is available on a required basis:

26. Programme(s) (including Year of Study) to which this module is available on an optional basis:

27. Aims
 

To provide an introduction to the topic of Computer Vision.
To present fundamental problems in both 2D and 3D vision, and to explain a variety of classical and emerging approaches to overcome them.
To develop the practical skills necessary to build computer vision applications.

 
28. Learning Outcomes
 

(LO1) Demonstrate an understanding of the theoretical and practical aspects of image representations.

 

(LO2) Describe state-of-the-art techniques for image classification, image search, image segmentation, object detection, and object tracking.

 

(LO3) Describe the foundation of image formation with the pinhole camera model and how they project the 3D world to 2D images.

 

(LO4) Apply the principles of deep neural networks to various vision problems such as classification, detection, and semantic segmentation.

 

(LO5) Demonstrate and apply the practical skills necessary to build computer vision applications.

 
29. Teaching and Learning Strategies
 

Teaching Method 1 - Lecture
Description:
Attendance Recorded: Not yet decided
Notes: 3 lectures per week for 10 weeks

Teaching Method 2 - Laboratory Work
Description:
Attendance Recorded: Not yet decided

Standard on-campus delivery
Teaching Method 1 - Lecture
Description: Mix of on-campus/on-line synchronous/asynchronous sessions
Teaching Method 2 - Laboratory Work
Description: On-campus synchronous sessions

 
30. Syllabus
   

2D vision:
1. Course introduction, Computer vision overview
2. Linear Algebra for Computer Vision
3. Pixels and image representation
4. Image filters and edge detection
5. Local features and fitting (Local features, Harris corner detection, Scale invariant feature
transform, Image stitching and RANSAC)
6. Local features and fitting (Local features, Harris corner detection, Scale invariant feature
transform, Image stitching and RANSAC) (cont’d)
7. Python for Computer Vision
8. Segmentation: tree-based segmentation, spectral clustering, other superpixel methods.
9. Segmentation: tree-based segmentation, spectral clustering, other superpixel methods (cont’d)
10. Image classification overview and Bag of Features
11. Nearest neighbor and logistic regression (for image search and image classification)
12. Image classification and image search using advanced feature coding (First and second order
local feature aggreg ation, sparse coding).
13. Image classification and image search using advanced feature coding (First and second order
local feature aggregation, sparse coding) (cont’d)
14. Object detection (e.g., face and pedestrian) with sliding window approach, object detection
using part-based models.
15. Object detection (e.g., face and pedestrian) with sliding window approach, object detection
using part-based models. (cont’d)
16. Object detection (e.g., face and pedestrian) with sliding window approach, object detection
using part-based models. (cont’d)
17. Motion and tracking
18. Motion and tracking (cont’d)

3D vision
19. Image formulation, camera calibration
20. Image formulation, camera calibration (cont’d)
21. Multi-view geometry
22. Multi-view geometry (cont’d)
23. Multi-view geometry (cont’d)

Computer Vision in the era of deep learning
24. Neural Networks (perceptro n, multi-layer perceptron, activation functions, loss functions,
gradient descent, back-propagation).
25. Neural Networks (perceptron, multi-layer perceptron, activation functions, loss functions,
gradient descent, back-propagation). (cont’d)
26. Deep learning (principles, convolutional neural network, auto-encoder neural network,
recurrent neural network) and state-of-the-art deep network architectures for image
classification.
27. Deep learning (principles, convolutional neural network, auto-encoder neural network,
recurrent neural network) and state-of-the-art deep network architectures for image
classification. (cont’d)
28. Deep learning (principles, convolutional neural network, auto-encoder neural network,
recurrent neural network) and state-of-the-art deep network architectures for image
classification. (cont’d)
29. Deep learning for object detection and semantic segmentation
30. Deep learning for objec t detection and semantic segmentation (cont’d)

 
31. Recommended Texts
  Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.
 

Assessment

32. EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
  (338) Final exam 2.5 70
33. CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
  (338.1) Programming Assignment on Image Alignment 0 15
  (338.2) Programming Assignment on Deep Neural Networks 0 15