Image and Video processing (IMP302)
Vietnamese title: Xử lý hình ảnh và video
Credits: 3
Prerequisites: CSD203, CPV301

Description:
Image & Video Processing is an advanced course that covers the fundamental principles, techniques, and applications of image and video processing. The course explores manipulating digital images and videos to extract useful information, enhance visual quality, and perform analysis. It covers various topics, including image acquisition and digitization, image enhancement and restoration, image compression and coding, image segmentation and feature extraction, and object recognition and tracking. The course also covers fundamental concepts in video processing, including video coding and compression, motion estimation, and video analysis. Students will learn to process images and videos using various tools and software platforms, including MATLAB and Python.

Learning outcomes:
Gain a comprehensive understanding of fundamental principles in image and video processing, digital signal processing in time domain and frequency domain
Develop expertise in applying the tradditional and machine learning-based techniques for image and video acquisition, digitization, enhancement, restoration.
Develop expertise in applying traditional and machine learning based methods to compress images and video
Acquire practical skills by using and implementing traditional and machine learning based tools to segment and extract features of an image
Develop expertise in applying traditional and machine learning based methods in object detection, motion estimation and video analysis.

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, assignments and labs 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 LMS at http://flm.fpt.edu.vn for up-to-date course information

Tools:
No

Materials:
description: Handbook of Image and Video Processing
author: Alan C. Bovik
publisher: Academic Press
published_date: 2005
isbn: 978-0121197902 (ISBN-13)
note: 24x7 Library
description: Computer Vision: Algorithms and Applications (Texts in Computer Science)
author: Richard Szeliski
publisher: Springer
published_date: 2022
isbn: 978-3030343712 (ISBN-13)
note: Textbook of CPV301
description: Computer Vision: Principles, Algorithms, Applications, Learning (5th Edition)
author: E. R. Davies
publisher: Academic Press
published_date: 2017
isbn: 978-0128092842 (ISBN-13)
description: Digital Image Processing: An Algorithmic Introduction (Texts in Computer Science)
author: Wilhelm Burger, Mark J. Burge
publisher: Springer
published_date: 2022
isbn: 978-3031057434 (ISBN-13)
description: Digital Image Processing
author: Rafael C. Gonzalez, Richard E. Woods
publisher: Pearson
published_date: 2017
isbn: 978-1-292-22304-9 (ISBN-13)
note: FPTU Library (hardcopy)

Assessments:
category: Assignment 1
type: on-going
weight: 20.0
duration: 135'
question_type: CLO1, CLO2, CLO3
no_question: Group presentation for the selected article
knowledge_and_skill: 1
grading_guide: cover part of all contents
note: in class, by group presentation
category: Assignment 2
type: on-going
weight: 20.0
duration: 135'
question_type: CLO1, CLO2, CLO3, CLO4, CLO5
no_question: Group presentation for the selected article
knowledge_and_skill: 1
grading_guide: cover part of all contents
note: in class, by group presentation
category: Progress test 1
type: on-going
weight: 10.0
duration: 20'-30'
question_type: CLO1, CLO2, CLO3
no_question: Multiple choicesMarked by Computer or a suitable format
knowledge_and_skill: 30
grading_guide: cover content Chapter 1 - Chapter 5
note: in class, by instructor
category: Progress test 2
type: on-going
weight: 10.0
duration: 20'-30'
question_type: CLO4, CLO5
no_question: Multiple choicesMarked by Computer or a suitable format
knowledge_and_skill: 30
grading_guide: cover content Chapter 6 - Chapter 12
note: in class, by instructor
category: Final exam
type: Final exam
weight: 40.0
duration: 60'
question_type: CLO1, CLO2, CLO3, CLO4, CLO5
no_question: Multiple choices
Marked by Computer
knowledge_and_skill: 50
grading_guide: concepts, algorithms;
all studied slides
note: by exam board,
using computer

Schedule:
session: Course introduction:
Introduce the materials of the courses and how to asses these materials
Introduce the assignments and how to pass the course
learning_outcomes: IT
materials: mat1
session: 1. Basic Gray-Level Image Processing
1.1 Basic gray-level image processing
1.2 Basic binary image processing
Assignment 1 introduction
learning_outcomes: T
materials: drive.google.com
session: 2. Image and Video Processing (Enhancement)
2.1 Basic Linear Filtering with Application to Image Enhancement
2.2 Nonlinear Filtering for Image Analysis and Enhancement
learning_outcomes: T
materials: drive.google.com
session: 2.3 Morphological Filtering for Image Enhancement and Feature Detection
learning_outcomes: T
materials: drive.google.com
session: 2.4 Spatial Filtering
2.5 Filtering in frequency domain
learning_outcomes: T
materials: drive.google.com
session: 3. Image Degradation, Restoration and Resconstruction
3.1 A model of image restoration procees
3.2 Noise models
learning_outcomes: T
materials: drive.google.com
session: 3.3 Spatial filtering and frequency domain filtering in noise reduction
3.4 Degradation and its function
learning_outcomes: T
materials: drive.google.com
session: 3.5 Wavelet Denoising for Image Enhancement
3.6 Minimum mean square error filtering
3.7 Constrained least square filtering
learning_outcomes: T
materials: drive.google.com
session: 3.8 Basic Methods for Image Restoration and Identification
3.9 Image reconstruction from projections
learning_outcomes: T
materials: drive.google.com
session: 3.10 Multichannel Image Recovery
learning_outcomes: T
materials: drive.google.com
session: Assignment 1 review
learning_outcomes: T
materials: Assignment 1 review
session: 3.11 Motion Detection and Estimation
3.12 Video Enhancement and Restoration
learning_outcomes: T
materials: drive.google.com
session: 4. Image and video analysis
4.1 Wavelet transform and other image transforms
4.2 Discrete Hartley transform
learning_outcomes: T
materials: drive.google.com
session: 4.3 Discrete Cosine transform
learning_outcomes: T
materials: drive.google.com
session: 4.4 Walsh-Hadamard Transforms
learning_outcomes: T
materials: drive.google.com
session: 4.5 Wavelet Transforms
learning_outcomes: T
materials: drive.google.com
session: 4.6 Multiscale Image Decompositions and Wavelets
learning_outcomes: T
materials: drive.google.com
session: 4.7 Image Noise Models (machine learning based Video analysis)
learning_outcomes: T
materials: drive.google.com
session: 5. Image compression
5.1 Lossless coding
5.2 Block Truncation Coding (BTC)
learning_outcomes: T
materials: drive.google.com
session: Assignment 1 review
learning_outcomes: T
materials: Assignment 1 review
session: 5.3 Fundamentals of Vector Quantlzation
5.4 Huffman coding
learning_outcomes: T
materials: drive.google.com
session: 5.5 Wavelet Image Compression
learning_outcomes: T
materials: drive.google.com
session: 5.6 Arithmetic coding
learning_outcomes: T
materials: drive.google.com
session: 5.7 Lossy Image Compression: JPEG and JPEG2000 Standards
learning_outcomes: T
materials: drive.google.com
session: 5.8 Multispectral Image Coding
learning_outcomes: T
materials: drive.google.com
session: 5.8 Recovery Methods for Postprocessing of Compressed Images
learning_outcomes: T
materials: drive.google.com
session: 5.9 Image compression based on machine learning/deep learning
learning_outcomes: T
materials: drive.google.com
session: 6. Video compression
6.1 Basic Concepts and Techniques of Video Coding and the H.261 Standard
learning_outcomes: T
materials: drive.google.com
session: 6.2 Interframe Subband/Wavelet Scalable Video Coding
learning_outcomes: T
materials: drive.google.com
session: Assignment 1 Evaluation
learning_outcomes: T
materials: Assignment 1 Evaluation
session: Progress Test 1
Assignment 2 Introduction
learning_outcomes: T
materials: Progress Test 1 Assignment 2 Introduction
session: 6.3 Digital Video Transcoding
learning_outcomes: T
materials: drive.google.com
session: 6.4 MPEG-1 and MPEG-2 Video Standards
learning_outcomes: T
materials: drive.google.com
session: 7. Image and Video Acquisition
7.1 Image Scanning, Sampling, and Interpolation
learning_outcomes: T
materials: drive.google.com
session: 7.2 Video Sampling and Interpolation
learning_outcomes: T
materials: drive.google.com
session: Assignment 2 Review
learning_outcomes: T
materials: Assignment 2 Review
session: 8. Image and Video Rendering and Assessment
8.1 Image Quantization, Halftoning, and Printing
learning_outcomes: T
materials: drive.google.com
session: 8.2 Perceptual Criteria for Image Quality Evaluation
learning_outcomes: T
materials: drive.google.com
session: 8.3 Structural Approaches to Image Quality Assessment
learning_outcomes: T
materials: drive.google.com
session: 9. Image and Video segmentation
9.1 Clustering and superpixels based segmentation
learning_outcomes: T
materials: drive.google.com
session: 9.2 Segmentation using graph cuts
9.3 The use of motion in segmentation
learning_outcomes: T
materials: drive.google.com
session: 9.4 Statistical Methods for Image Segmentation
learning_outcomes: T
materials: drive.google.com
session: 9.5 Video Segmentation
learning_outcomes: T
materials: drive.google.com
session: 9.6 2D and 3D Motion Tracking in Digital Video
9.7 Adaptive and Neural Methods for Image Segmentation
learning_outcomes: T
materials: drive.google.com
session: 9.8 Machine learning/deep learning based segmentation
learning_outcomes: T
materials: drive.google.com
session: Assignment 2 Review
learning_outcomes: T
materials: Assignment 2 Review
session: 10. Feature extraction
10.1 Boundary feature descriptors
learning_outcomes: T
materials: drive.google.com
session: 10.2 Region feature descriptors
learning_outcomes: T
materials: drive.google.com
session: 10.3 Principal Components as features
learning_outcomes: T
materials: drive.google.com
session: 10.4 Whole-image feature
learning_outcomes: T
materials: drive.google.com
session: 10.5 Machine learning based features
learning_outcomes: T
materials: drive.google.com
session: 11. Object detection
11.1 Traditional methods
learning_outcomes: T
materials: drive.google.com
session: 11.2 Machine learning based methods
learning_outcomes: T
materials: drive.google.com
session: Assignment 2 Evaluation
learning_outcomes: T
materials: Assignment 2 Evaluation
session: 12. Image pattern classification
12.1 Patterns and pattern classes
learning_outcomes: T
materials: drive.google.com
session: 12.2 Optimum statistical classifier
learning_outcomes: T
materials: drive.google.com
session: 12.3 Neural network and Deep learning based classifiers
learning_outcomes: T
materials: drive.google.com
session: 12.4 Deep convoluntional neural network based classifiers
learning_outcomes: T
materials: drive.google.com
session: Progress Test 2
learning_outcomes: T
materials: Progress Test 2
session: Summary All Slides, Tests, and Assignments
learning_outcomes: T
materials: drive.google.com