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List of courses (current and past) that I teach:

Course Title

Digital Image Processing - (CS 573)

Programe

M.Phil (Computer Science)

Couorse Description

The course of CS-573 – Digital Image Processing is particularly designed to introduce students with the concepts, tools and techniques of image processing. This course is designed as a graduate level elective for M. Phil. in Computer Science. It will teach students the foundations as well as emerging trends in the fields of image processing, including: visual perception, image acquisition, representation, spatial transformations, frequency domain image processing, image enhancement, color image representation and processing, edge detection, image compression, image segmentation, and morphological image processing.

Course Outline

Download Course Outline icona file pdf

Text Books

  1. Rafael C. Gonzalez, Digital image processing. Pearson Education, 3rd Ed., 2009.
  2. Simon JD. Prince, Computer vision: models, learning, and inference, Cambridge University Press, 2012.

 

Material

Lecture 01 Introduction to Image Processing and its applications in various fields Slides 1
Lecture 02 Human visual perception, Light and electromagnetic spectrum, Image acquisition, Sampling and Quantization

Slides 2, Handouts,
Video on Applications of EM Waves*
Lecture 03 Image Sensing and Acquisition, Image Sampling and Qunatization Slides 3
Lecture 04 Raster versus vector images, Progressive versus interlaced display, Popular image file formats, Why so many formats?, Basic Relationships Between Pixels Slides 3
Lecture 05 Point wise operations, Contrast Stretching, Bit-Plane Slicing

Slides 4, Home Work 1,
Quiz 1
Lecture 06 Histogram Processing, Histogram equalization, Histogram matching Slides 5
Lecture 07 Local Histogram processing, Enhancement using histogram statistics, Enhancement Using Arithmetic/Logic Operations Slides 5
Lecture 08 Image filtering in spatial domain, Smoothing filters, Order statistics filter, Sharpening filters Slides 6, Quiz 2,
Home Work 2
Lecture 09-11 Fourier Transform and its applications in image processing

Handouts (hard copy)
Lecture 12 Properties of Discrete Fourier Transform (DFT)

Handouts (hard copy)
Lecture 13 Fileting in frequency domain, smoothing and sharping revisited Slides (Prof. Bebis) , Home Work 3
Lecture 14 Selective Filtering, Homomorphic Filtering, Notch Filter Slides (Prof. Bebis)
Lecture 15 Introduction to image restoration and different noise models Slides (Liu)**, Quiz 3
Lecture 16 Image restoration filters, Periodic Noise Slides (Liu)**
Lecture 17 Color models, Color transformations, Color image processing

Slides (Farid), Slides (Liu)**,
The Chromaticity Diagram (Video)***
Lecture 18 Morphological Image Processing: Some Basic Concepts from Set Theory, Dilation and Erosion, Opening and Closing Slides (Liu)**, Quiz 4
Lecture 19 The Hit-or-Miss Transformation, Some Basic Morphological Algorithms, Some Applications of Gray-Scale Morphology Slides (Liu)**
Lecture 20 Fundamentals of image segmentation: Point, Line, and Edge Detection Slides
Lecture 21 Boundary Detection, Thresholding, Region-Based Segmentation  
Lecture 22 Segmentation by Morphological Watersheds, Motion-based Segmentation  
Lecture 23 Texture Synthesis Slides, Quiz 5
Lecture 24 Image Inpainting Slides
Lecture 25 Content-based Image Retrieval

Slides 1, Slides 2
Lecture 26 Wavelets and Multiresolution Processing: Image Pyramids, Subband Coding, The Haar Transform Slides
Lecture 27 Multiresolution Expansions, Wavelet Transforms in One Dimension Slides, Quiz 6
Lecture 28 Image Qualaity Assessment Slides
Lecture 29 Introdcution to Various IQA Technqiues Slides
Lecture 30 3D television technology: framework overview, display technologies Handouts/Slides
Lecture 31 3DV representation, compression, and quality assessment Handouts/Slides
Lecture 32 Course Conclusions  

 

Exam/ Sessional Instruments

  • Quizzes + Home Works: 20 percent
  • Programming Assignments: 20 percent
  • Mid Term: 30 percent
  • Final Term: 30 percent

 

Office hours

Wednesday: 1100 - 1300 hours

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* https://www.youtube.com/watch?v=CxVwdv76KBQ (SPM - Physics- Form 5 - SPM Malaysia IPTV)

** http://staffweb.ncnu.edu.tw/jcliu/course/dip2006.html

*** https://www.youtube.com/watch?v=O0nYJ0Mjx10 (SMC468 Graphic Design for Education)


Course Title

Analysis of Algorithms - (CS 310)

Programe

BS (Computer Science)

Couorse Description

The primary objective of this course is to promulgate Thinking and to teach students how to design and analyze algorithms. That is:

  1. To show how to design algorithms,
  2. How to make them efficient in terms of time and space
  3. How to improve and optimize the algorithms
  4. How to choose proper data structures
  5. How to attack a problem
  6. Recognize that not all problems are solvable and that many problems can be reduced to well-researched standard problems

 

Course Outline

Download Course Outline icona file pdf

Text Book

  1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein. “Introduction to Algorithms”, MIT Press, Third Edition

 

Material

Week Lecture Topic Source/Material Learning Activities
1 1 Introduction to Course, Review of Data Structure and some Discrete Mathematics concepts Role of Algorithms in Computing Ch – 1 Prerequisite Quiz: - to evaluate prerequisite knowledge
2 Insertion Sort and its line by line analysis and its correctness A – 2.1, 2.2  
2

 

 3 Asymptotic Notations: Big O, Little O, Big Omega, little omega etc A – 2.3, 2.3  
4 Introduction to Divide and Conquer Approach ,Merge Sort and its analysis. A 3.1  
3 5 Recurrences and methods to solve recurrence equations:The Substitution Method,Recursion Tree Method,The Master Method A – 4.1, 4.2, 4.3  
6 Description of Quick sort, Performance of Quick sort A – 7.1, 7.2, A – 7.3, 7.4 Quiz 1 from Chapter 2, 3 and 4.
4 7 Heaps, Maintaining a Heap Property, Building a Heap A – 6.1 – 6.3  
8 Heap sort AlgorithmPriority Queues A – 6.4 – 6.5 Quiz 2 from chapter 6 and 7Assignment 1
5 9 Balanced trees (B-Tree or Red-Black Tree) and its insertion, searching algorithms A – 18.1 – 18.2  
10 Deletion from a balanced tree A – 18.3  
6 11 Counting Sort, Radix Sort, Bucket Sort A – 8.2 – 8.4 Quiz 3 from chapter 18
  12 Minimum and maximum, Selection in expected linear time, Selection in worst-case linear time A – 9.1 – 9.3  
7 13 Introduction to Dynamic Programming, Matrix Chain Multiplication A – 15.2 Quiz 4 from chapter 8, 9
14 Assembly-line Scheduling A – 15.1  
8 15 Longest common subsequence A – 15.4  
16 Optimal binary search trees A – 15.5  
9 17 Greedy Algorithm, An activity-selection problem A – 16.1 Quiz 5 from chapter 15
18 Elements of greedy Strategy, Knapsack Problem, Variant of Knapsack problem A – 16.2  
10 19 Huffman Code A – 16.3  
20 Some Concepts about Graph, Representation of Graph A – 22.1, 22.2 Quiz 6 from chapter 16, Assignment 3
11 21 Breadth First Search and its applications , A – 22.2  
22 Depth First Search and its applications A – 22.3  
12 23 Topological Sort and Strongly Connected Components A – 22.4, 22.5  
24 Minimum Spanning Tree, Kruskal Algorithm A – 23.1, 23.2 Quiz 7 from chapter 22
13 25 Prims Algorithm A – 23.2  
26 Single-source Shortest Paths, Bellman Ford Algorithm A – 24.1  
14 27 Single-source Shortest Path in DAGs, Dijkstra's Algorithm A – 24.2, 24.3 Quiz 8 from chapter 23
28

All-Pairs Shortest Path Problem, Floyd Warshall Algorithm

 A – 25.1, 25.2 Assignment 4
15 29 Introduction to Flow Networks A – 26.1 Quiz 9 from chapter 24 and 25
30 The Ford-Fulkerson Method A – 26.2  
16 31 NP Completeness, Polynomial time and solution and verification A – 34.1, 34.2 Quiz 10 from chapter 26
31 Final discussion on P vs. NP and course conclusions    

 

Exam/ Sessional Instruments

  • Quizzes + Home Works: 25 percent
  • Mid Term: 35 percent
  • Final Term: 40 percent

 

Office hours

Wednesday: 1100 - 1300 hours

 

Session Courses Taught Program
Fall 2021 Analysis of Algorithms BS
Advanced Analysis of Algorithms MPhil
Spring 2021 Theory of Automata BS
Analysis of Algorithms BS
Advanced Analysis of Algorithms MPhil
Fall 2020 Digital Image Processing M.Phil/PhD
Theory of Automata BS
Analysis of Algorithms BS
Spring 2020 Theory of Automata BS
Fall 2019 Analysis of Algorithms BS
Spring 2019 Digital Image Processing M.Phil/PhD
Analysis of Algorithms BS
Fall 2018 Theory of Automata BS
Analysis of Algorithms BS
Spring 2018 Digital Image Processing M.Phil/PhD
Analysis of Algorithms BS
Fall 2017 Theory of Automata BS
Spring 2017 Digital Image Processing M.Phil/PhD
Analysis of Algorithms BS
Fall 2016 Theory of Automata BS
Spring 2016 Analysis of Algorithms BS
Fall 2012 Analysis of Algorithms BS
Spring 2012 Theory of Automata BS
Fall 2011 Analysis of Algorithms BS
Spring 2011 Theory of Automata BS
Analysis of Algorithms BS
Fall 2010 Analysis of Algorithms BS
Spring 2010 Analysis of Algorithms BS
Theory of Automata BS
Multimedia System Development BS
Fall 2009 Analysis of Algorithms BS
Spring 2009 Analysis of Algorithms BS
Software Project Management BS
Fall 2008 Analysis of Algorithms BS
Spring 2008 Theory of Automata BS
Fall 2007 Analysis of Algorithms BS
Software Quality Assurance BS
Spring 2007 Software Quality Assurance BS
Software Project Management BS