Multimedia Computing and Computer Vision Lab












Student Theses


Source Code / Datasets




WS 10/11: Image Processing

From Multimedia Computing Lab - University of Augsburg

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Instructors: Dr. Beril Sirmacek, Deutsches Zentrum für Luft- und Raumfahrt (DLR)
Time Lecture: Friday, 10:00 - 11:30, Room: 2013 N
Time Exercise: Friday, 12:15 - 13:45, Room: 2013 N
Credits: 2 + 2 SWS, 5 LP


  • 17.01.2011: Slides of Chapter 7 are available now. See Section Material below.
  • 10.01.2011: The date of the 2. Midterm exam has been moved to 21.01.2011.
  • 10.01.2011: Slides of Chapters 5 and 6 are available now. See Section Material below.
  • 30.11.2010: Slides of Chapter 4 are available now.
  • 9.11.2010: Brief Project Description is available now.
  • 8.11.2010: Slides of Chapters 1-3 are available now.

Notes on the 2. Midterm exam

  • The date of the 2. Midterm exam has been moved to 21.01.2011.
  • Please write me an email if you have questions about midterm.

Phone: 08153 28 3564

Notes on the 1. Midterm exam

  • See the slides of Chapter 4 for more details.
  • Skip the slides that I have made a red cross mark, we didn't talk about them at class yet.
  • Please write me an email if you have questions about midterm.

Phone: 08153 28 3564


Bachelor of Science (B.Sc.)

Multimedia Teilbereiche:
  • Multimedia-Methoden
  • Systemnahe Aspekte von Multimedia
  • Multimedia Anwendungen

Course Description

This course presents the fundamentals of digital image processing with particular emphasis on object detection techniques. It covers principles and algorithms for processing binary, grayscale, and color images. Topics include introduction, usage of OpenCV library, binary image processing, gray-scale image processing in spatial and frequency domains, color image processing, segmentation, compression, coding, and brief introduction to object detection techniques. Examples about these topics will be explained using OpenCV. Course will require a final project and presentation of an image processing application.

Technical Requirements

Visual Studio and OpenCV Library installed computers are required for example sessions.


Ideally participants have attended “Multimedia Grundlagen I” last year. However “Multimedia Grundlagen I” is not required for successfully mastering this course.


25% First MidTerm Exam + 25% Second MidTerm Exam + 50% Final Project

Reference Books

  • R.C. Gonzalez, R.E. Woods, “Digital Image Processing”, Prentice Hall, 3rd Edition, 2007. ISBN: 013168728X
  • M. Sonka, V. Hlavac, “Image Processing, Analysis, and Machine Vision”, CLEngineering, 3rd Edition, 2007. ISBN: 049508252X
  • G. Bradski, A. Kaehler, “Learning OpenCV: Computer Vision with the Open CV Library”, O’Reilly Media, 1st Edition, 2008. ISBN: 0596516134

Contact Information

Email:, Phone: 08153 28 3564

Detailed Topics

(1) Introduction to Image Processing

  • Motivation
  • Application fields in real life
  • Digital image obtaining steps (sampling and quantization)
  • Spatial and gray level resolutions
  • Pixel term and pixel neighbourhoods
  • Distance metrics (Euclidean distance, city-block distance, chessboard distance),
  • Noise on images (impulse noise, salt and paper noise, Gaussian noise, periodic noise, motion noise)
  • Moire patterns
  • Entropy term
  • Image file formats (.jpeg, .tif, etc.)

(2) Introduction to Image Processing Using OpenCV Library:

  • Brief introduction to OpenCV and Visual Studio
  • Variables and matrices
  • Working with images, basic functions
  • Example Program

(3) Binary Image Processing:

  • Binary images
  • Morphological operations in binary images (dilation, erosion, opening, closing operations)
  • Distance transform
  • Skeletoning
  • Labelling
  • Measuring area and perimeter
  • Compactness of objects
  • Other binary image operations (Hit-Miss transformation, finding object boundaries, filling holes in the objects)
  • Examples.

(4) Gray-Scale Image Processing in Spatial Domain:

  • Histogram based processes (histogram equalization, gamma correction, thresholding),
  • Filtering based techniques in spatial domain (edge detection using first and second derivatives, well-known operators like Roberts, Sobel, Prewitt, Laplace, Laplacian of Gaussian), Canny edge detection
  • Sharpening, Morphological Operations on Gray-Scale Images
  • Mean and Median Filters
  • Examples.

(5) Gray-Scale Image Processing in Frequency Domain:

  • 2D Fourier tranform and frequency domain
  • Image filtering in frequency domain (smoothing, noise filtering, image sharpening in frequency domain)
  • Removing periodical noise
  • Fast Fourier Transform (FFT) and Discrete Cosine Transform (DCT)
  • Wavelet Transform
  • Examples

(6) Color Image Processing

  • Color spaces (RGB, HIS, CMY, CMYK)
  • Filtering color images
  • Obtaining gray-scale image from color image
  • Obtaining color image from gray-scale image (false coloring),
  • Examples

(7) Image Segmentation

  • Clustering based segmentation
  • Histogram based segmentation
  • Edge detection based segmentation
  • Segmentation using Watershed algorithm
  • Graph theory based segmentation

(8) Image Compression and Coding

  • Huffman coding system
  • Arithmetic coding system
  • JPEG and JPEG2000 coding systems
  • Advantages and disadvantages of several coding systems

(9) Brief Introduction to Object Detection:

  • Difficulties in object detection
  • Shape detection (chain codes, and Hough transform),
  • Texture detection using co-occurrence matrices
  • Feature detection algorithms (SIFT, Harris corner, Schmidt features)
  • Examples.


Date Content Slides Exercise
Chapter 1 - Introduction to Image Processing PDF
Chapter 2 - Introduction to Image Processing Using OpenCV Library PDF
Chapter 3 - Binary Image Processing PDF
Brief Project Description PDF
Chapter 4 - Grayscale Image Processing in Spatial Domain PDF
Chapter 5 - Grayscale Image Processing in Frequency Domain PDF
Chapter 6 - Color Image Processing PDF
Chapter 7 - Image Segmentation PDF
Chapter 8 - Image Compression and Coding PDF