CS 5650 Image Processing, Pattern Recognition, and Computer Vision
Fall 2008
Section 1 TR 1:30 p.m. - 2:45 p.m. Old Main 119
Office Hours: TR 10:30 a.m. - 12:00 p.m. and W 2:00 p.m. - 3:30 p.m.
| Email: | Xiaojun.Qi@usu.edu |
| Phone: | (435)797-8155 |
| Office: | Main 401C |
Syllabus
Course Topics
Chapter 1 Introduction to IPPRCV and Their Potential Applications
(Class Notes), (Enlarged Class Notes)
Chapter 2 Digital Image Fundamentals
(Class Notes), (Enlarged Class Notes)
2.1 Digital Image Definitions and Concepts
2.2 Images as Surfaces
2.3 Multi-channel Images and Color
2.4 Matlab for Image Processing
Chapter 3 Basic (Low-Level) Digital Image Processing
3.1 Image Enhancement
3.1.1 Image Enhancement in the Spatial Domain
(Class Notes), (Enlarged Class Notes)
3.1.2 Image Enhancement in the Frequency Domain -- Fourier Transform and Wavelet Transform
(Class Notes), (Enlarged Class Notes), (Supplements), (Enlarged Supplements Class Notes), (FFT Example)
3.2 Others (Class Notes), (Enlarged Class Notes)
3.2.1 Noise Removal Techniques for Image Restoration
3.2.3 Geometric Operations
Chapter 4 Advanced (Mid-Level) Digital Image Processing
(Class Notes), (Enlarged Class Notes)
4.1 Image Morphology
4.2 Introduction to Dilation and Erosion
4.3 Introduction to Opening and Closing
4.4 Introduction to Some Basic Morphological Algorithms
Chapter 5 Advanced (High-Level) Digital Image Processing
(Class Notes), (Enlarged Class Notes)
5.1 Basic Image Segmentation Strategies
5.2 Data Structures for Segmentation
5.3 Watersheds and Region-Based Algorithms
Chapter 6 Basic Pattern Recognition
6.1 Object Representation and Recognition
(Class Notes), (Enlarged Class Notes)
6.2 Patterns and Pattern Classes
(Class Notes), (Enlarged Class Notes)
Chapter 7 Advanced Pattern Recognition
7.1 Expert (Model)-based Pattern Recognition and Classification
(Class Notes), (Enlarged Class Notes)
7.2 Distance-Based Pattern Recognition and Classification
(Class Notes), (Enlarged Class Notes, Fuzzy C-means Algorithm))
7.3 Statistics (Distribution)-Based Pattern Recognition and Classification
(Class Notes), (Enlarged Class Notes)
7.4 Syntactic-Based Pattern Recognition and Classification
(Class Notes), (Enlarged Class Notes)
7.5 Feature Selection
(Class Notes), (Enlarged Class Notes)
Chapter 8 Computer Vision
8.1 Introduction
8.2 Applications
Chapter 9 The Presentation of the Final Project
Potential Final Project (Class Notes)
Assigments
1. [Total 20 points] Assignment 1 (Pepper Image)
Due by 11:59 p.m. on Sept. 5, 2008
2. [Total 35 points] Assignment 2 (Food Image)
Due by 11:59 p.m. on Sept. 20, 2008
3. [Total 30 points] Assignment 3 (Circuit Image, Moon Image, Rice Image, Text Image)
Due by 11:59 p.m. on Oct. 4, 2008
4. [Total 50 points] Assignment 4 (Lena Image, Sample Image, NewCapitol Image,
City Image, Boy_Noisy Image)
Due by 11:59 p.m. on Oct. 18, 2008
5. [Total 50 points] Assignment 5
(Ball Image,
City Image,
SmallSquares Image,
Wirebond Image,
Shapes Image,
Dowels Image)
Due by 11:59 p.m. on Nov. 1, 2008
6. [Total 65 points] Assignment 6
(Ball Image,
Elephant1 Image,
Elephant2 Image,
Horse1 Image,
Horse2 Image,
Lena Image)
Due by 11:59 p.m. on Nov. 15, 2008
Candidate Project Topics
7. Preliminary Paper Summary Due by 11:59 p.m. on Nov. 10, 2008
8. Final Project Check List Due by 11:59 p.m. on Dec. 7, 2008
9. Final Take-Home Exam Due by 11:20 a.m. on Dec. 9, 2008
10. Final Presentation Schedule
Tests Related
1. Study Guide for the midterm exam
2. Guide for the final project
3. Final Take-Home Exam
Miscellaneous
1. Tutorial 1: Mastering Matlab -- Matrix Notation
2. Tutorial 2: Mastering Matlab -- General Information and Several Programming
Examples (Testing Data is: tools.ascii)
3. A Matlab Example on Various Matrices
4. Mini Matlab in class exercises: Sample Solutions (Problem 1, Problem 2, and Problem 3). Note: By no means that the posted solution is the best one.`
5. My seminar on IPPRCV research