UBI621 – Digital Image Processing

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Course Unit Title Digital Image Processing
Course Unit Code UBI 621
Course Time: Friday [09:00-12:00]
Course Room: UBE, Room 103
Level of Course Unit PhD
Number of ECTS Credits Allocated 7,5
Theoretical 3
Semester Fall
Lecturer Assoc. Prof. Dr.Muhammed Cinsdikici,
Contact Info cinsdikici@gmail.com, office phone: 3205
Mode of Delivery Face-To-Face
Language Turkish
Assistant
Prerequisities Matlab Coding, C# coding, Linear Algebra,  No prior knowledge of vision is assumed.
Objectives of the Course – to make the student identify image processing methods,- comprehend importance of using computer in image processing,- construct basic image implementations with MATLAB program packet- develop image processing algorithms.
Textbook(s)
  1. Computer Vision: A modern Approach (2nd edt), David A. Forsyth, Jean Ponce, Prentice Hall, ISBN: 978-0136085928, 2011
  2. Computer Vision: Algorithms and Applications , Richard Szeliski, ISBN: 978-1-84882-934-3, Springer, 2011
  3. Digital Image Processing 3rd Edition (DIP/3e), Rafael C. Gonzalez, Richard E. Woods, Prentice Hall, ISBN:978-0135052679, 2008.
  4. Practical Image and Video Processing, Oge Marques, Wiley, ISBN: 978-0470048153 ,2011
Learning Outcomes At the end of this course, a student should be able to:

  1. Calculate image histograms and gray-scale transformation for image enhancement, explain histogram equilazation method
  2. Identify filtering methods in spatial and frequency domains and develop algorithms
  3. Calculate 2-dimensional Fourier Transformation of an image; calculate and analyse 2-dimensional convolution and apply it for the filtering purpose
  4. Comprehend mathematical morphology fundamentals and apply them for image processing purposes
  5. Recall basic MATLAB instructions and functions for basic image processing operations
  6. Apply image processing algorithms for real life problems
  7. Have the motivation to propose new solutions for image processing problems
  8. Realize the advantages of MATLAB program packet in algorithm development and develop image processing algorithms in MATLAB environment
Course Contents Objectives of this course are; to make the student identify image processing methods, comprehend importance of using computer in image processing, construct basic image implementations with MATLAB program packet and develop image processing algorithms.
Grading & Projects %30 HomeWorks + %20 MidTerm + %20 Final Exam + %30 Final ProjectÂLate submission of the homeworks and projects are not going to be accepted.ÂThe assessment principals are;————————————-On time submission are scored as max.100/100One week late submissions are scored as max. 80/100Two weeks late submissions are scored as max. 60/100Others are scored as 0.

 

ekly Detailed Course Contents
WEEK NAME Â SUBJECTS Â
 Theoretical Slides Laboratory Suplementary Materials
1 Introduction /Fundementals  Image Representation, Image Reading/Writing, Indexing Images, Converting Image Classes  Research Allignment  PinHole Camera (Obscura)
2 Intensity Transformations & Spatial Filtering  Image Intensity Transformations, Histogram Processing, Equalization/Matching, Linear/NonLinear Filtering a. Lecture-2-Filtering_Derivative_Noise

 

b. Intensity Transformations

 

c1. Histogram Equalization

 

c2. Histogram Equalization with Step by Step Exp

 Base Paper Definition + Literatur Searching  a. Histogram Equalization Open Matlab Code

 

b. Local Histogram Equalization Open Matlab Code

3 Edge Detectors Primitive Detectors, Marr Hilderith, Canny Edge Detectors  Lecture-3-EdgeDetection Hmw#1: Histogram Matching a.  Marr Hilderith MatlabCode_Mohamed Athiqb. Canny Edge Detector Code Explanationc. Canny Edge Detector with Javad. Canny Edge Detector Tutorial Pagee. Canny Edge Detector without Matlabs functions
4 Image Features  Harris Corners, SIFT Features, SURF FEatures a.Lecture-4-Harris Corner Detection_Interest Points

 

b. Lecture-5-SIFT

 Hmw#2: Detecting Corners with Harris Operator  a. Harris Corner Detector Logic

 

b. Edge & Corner Detection Lecture

 

c. Original SIFT code

 

d. SIFT Tutorial by Utkarsh Sinhae. ASIFT Method

 

f. SIFT Matlab Code

5 Frequency Domain Processing  Fundemental Concepts, BandPass (Low, High, Specified Intervals) Filters, Fourier Transforms a.Chapter 5 – Fourier Transformb. FFT Tutorial Video

 

c. Fourier for Beginners.

 Hmw#3: SIFT Feature detection  a. Fourier Series and Transformation – Part 2

 

b. Fourier Series and Transformation – Part 3

 

c. Fourier Series Simpler Explanation Part I

 

d. Fourier Series Simpler Explanation Part II

6 Image Restoration Adding Noise, Noise Characteristics, Noise Filtering, Wiener Filtering, Geometric Transformations
7  Midterm  Â
8 Color Image Processing  Color Spaces (RGB, HSI, HSV, Lab)
9 Wavelet  Wavelet Transform, Inverse Wavelet
10 Image Compression  Huffman Codes (Encoding/Decoding), Color Compression, JPEG Compression
11 Morphological Image Procesing  Dilation, Erosion, Opening, Closing, Mophological Reconstruction
12 Image Segmentation  Point/Line/Edge Detection, Hough Transform, Thresholding, Region-Based Segmentation
13 Object Recognition  Distance Measure, Shape/Pattern Matching
14 Case Studies
15  Final Exam  Â

 

Additional  Reading Video & Image Processing Tutorial Using C#/VS.Net, http://www.cogitance.com/files/videoprocessing/videoprocessing.htm

 

 

 

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