Computer Vision (CPV301)
Vietnamese title: Thị giác máy tính
Credits: 3
Prerequisites: PFP191, CSD203

Description:
-This course provides the basics of computer vision.
-Students will have access to knowledge from image representation, lighting, image acquisition through the camera, camera calibration techniques.
-Learn line and edge detection techniques in images, filters as well as Canny, RANSAC methods
-Students will learn advanced image processing knowledge such as Image segmentation, object detection, object recognition, and object tracking.
-Implement a computer vision project in recognizing objects in photos or videos

Learning outcomes:
Understand the basics of computer vision, its related fields, and its applications
Understand the concepts of image formation: image representation, lighting, image acquisition through the camera, camera calibration techniques.
Basic knowledge of image processing on points, lines, image filtering techniques and corresponding algorithm implementation
Understand the techniques of feature extraction and matching, implement the techniques of edges, lines extraction
Understand the basics of segmentation, techniques such as active contours, split and merge, mean shift and mode finding
Discuss feature-based alignments like 2D and 3D feature-based alignment
Discuss the principles and the implementation of Image stitching
Understand and implement the object detection techniques
Explain the principles of face recognition, object tracking

Student tasks:
- Students must attend at least 80% of contact slots in order to be accepted to the final examination.
- Student is responsible to do all exercises given by instructor in class or at home and submit on time.
- Use laptop in class only for learning purpose
- Promptly access to the FU CMS at http://cms.fpt.edu.vn for up-to-date course information

Tools:
Text Book
'Pycharm or Matlab
Internet

Materials:
description: Computer Vision: A Modern Approach
author: David Forsyth, Jean Ponce
publisher: Pearson
published_date: 10/26/2011 12:00:00 AM
edition: 2nd
isbn: 978-0136085928
description: Computer Vision: Algorithms and Applications
author: Richard Szeliski
publisher: Springer
published_date: 10/19/2010 12:00:00 AM
edition: 1st
isbn: 978-1848829343
note: https://szeliski.org/Book/
description: Course slide
description: Labs & assignment
description: Multiple View Geometry in Computer Vision
author: Richard Hartley
publisher: Cambridge University Press
published_date: 4/19/2004 12:00:00 AM
edition: 2nd
isbn: 978-0521540513
description: OpenCV Computer Vision with Python
author: Joseph Howse
publisher: Packt
published_date: 4/1/2013 12:00:00 AM
edition: 1st
isbn: 978-1782163923

Assessments:
category: Assignment
type: on-going
weight: 30.0
duration: 28 slots
question_type: LO1->LO9
no_question: Option 1: N/A
Option 2: Questions or Activities proposed by lecturer
knowledge_and_skill: Option 1: N/A
Option 2: Follow lecturer's proposal
grading_guide: All subjects in syllabus
note: in class, by teacher
category: Lab
type: on-going
weight: 10.0
duration: 90'/each
question_type: LO1->LO9
no_question: Option 1: N/A
Option 2: Questions or Activities proposed by lecturer
knowledge_and_skill: Option 1: N/A
Option 2: Follow lecturer's proposal
note: in class, by teacher
category: Practical Exam
type: on-going
weight: 20.0
duration: 90'/each
question_type: LO2->LO9
note: in class, by teacher
category: Progress Test
type: on-going
weight: 10.0
duration: 60'/each
question_type: Test: LO1, LO2, LO3, LO4, LO5, LO6
no_question: Option 1: Multiple choices
Marked by Computer or a suitable format
Option 2: Questions or Activities proposed by lecturer
knowledge_and_skill: Option 1: 50/each
Option 2: Follow lecturer's proposal
note: in class, by exam system
category: Final Exam
type: Final exam
weight: 30.0
duration: 60'/each
question_type: LO1->LO9
no_question: Multiple choicesMarked by Computer
knowledge_and_skill: 50
grading_guide: All subjects in syllabus
note: By Exam Board

Schedule:
session: Introduction:
1.1 Welcome to Computer Vision
learning_outcomes: I
materials: mat1
session: Introduction:

1.2 Computer vision overviews
learning_outcomes: I
materials: mat2
session: Image formation
2.1 Geometric primitives and transformations
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T
materials: mat3
session: Image formation
2.1 Geometric primitives and transformations
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session: Image formation
2.2 Photometric image formation
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session: Image formation
2.2 Photometric image formation
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session: Image formation
2.3 The digital camera
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session: Image formation
2.3 The digital camera
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session: Lab 1 assistant and interview
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session: Lab 1 assistant and interview
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session: Lab 1 assistant and interview
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session: Image processing
3.1 Point operators
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T
session: Image processing
3.1 Point operators
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session: Image processing
3.2 Linear filtering
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session: Image processing
3.2 Linear filtering
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session: Image processing
3.3 Fourier transforms
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session: Image processing
3.3 Fourier transforms
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session: Image processing
3.4 Geometric transformations
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session: Image processing
3.4 Geometric transformations
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session: Feature detection and matching
4.1 Points and patches
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session: Feature detection and matching
4.1 Points and patches
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session: Feature detection and matching
4.2 Edges
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session: Feature detection and matching
4.2 Edges
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session: Feature detection and matching
4.3 Lines
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session: Feature detection and matching
4.3 Lines
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session: Lab 2 assistant and interview
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session: Lab 2 assistant and interview
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session: Lab 2 assistant and interview
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session: Segmentation
5.1 Active contours
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session: Segmentation
5.1 Active contours
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session: Segmentation
5.2 Split and merge
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session: Segmentation
5.2 Split and merge
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session: Segmentation
5.3 Mean shift and mode finding
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session: Segmentation
5.3 Mean shift and mode finding
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session: Feature-based alignment
6.1 2D and 3D feature-based alignment
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session: Feature-based alignment
6.1 2D and 3D feature-based alignment
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session: Progress Test 1
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session: Lab 3 assistant and interview
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session: Lab 3 assistant and interview
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session: Lab 3 assistant and interview
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session: Image stitching
7.1 Motion models
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session: Image stitching
7.2 Global alignment
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session: Detection
8.1 Object detection
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session: Detection
8.1 Object detection
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session: Detection
8.1 Object detection
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session: Recognition
9.1 Face recognition
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session: Recognition
9.1 Face recognition
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session: Recognition
9.2 Object tracking
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session: Recognition
9.2 Object tracking
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session: Recognition
9.2 Object tracking
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session: Lab 4 assistant and interview
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session: Lab 4 assistant and interview
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session: Lab 4 assistant and interview
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session: Review
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session: Review
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session: Assignment Evaluation
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session: Assignment Evaluation
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session: Assignment Evaluation
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session: Practical Exam
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session: Review course
Results
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