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41580 - Digital Image Processing (Graduate Course)

Academic Year 2009/2010

  • Teaching Mode: Traditional lectures
  • Campus: Bologna
  • Corso: Second cycle degree programme (LS) in Computer Engineering (cod. 0234)

Learning outcomes

Fundamentals of Digital Image Processing and Analysis. Design and implementation of simple systems inspired by real-world applications.

Course contents

  1. Introduction - Image processing and computer vision. An overview of the major application areas: automated inspection and quality control, robotics, OCR, ITS applications, videosurveillance, biometric systems, medical image analysis.  
  2. Image Formation and Acquistion - Geometry of image formation. Perspective and orthographic projection. Vanishing points. imaging with a lens. Field of view. Depth of field. Radiometry of image formation. Image digitalizazion.  
  3. Technologies for Image Acquisition  - The CCD camera. Camera parameters. Video standards.  RS-170 and CCIR. Color spaces. NTSC and PAL. Frame-grabbers.
  4. Point Operators  - Gray-levels histogram. Contrast stretching. Gamma correction. Histogram Equalization.  
  5. Local Operators - Linear shift-invariant operators. Convolution and impule response. Correlation. 2D Fourier Transform. Box filter. Gausian filter. Median filter. Sharpening filter. 
  6. Image Segmentation -  Binarization by global thresholding. Adaptive and local threshoding.  Intensity slicing. Automatic slicing. Region growing. Split-and-Merge.
  7. Binary Morpholgy  - Dilation and Erosion. Opening and Closing. Hit-and-Miss Trasform. Morphological thinning. Pruning.
  8. Gray Scale Morpholgy  - Top Surface and Umbra. Dilation and Erosion. Opening and Closing. Top-Hat Trasform. Smoothing and morphological gradient.
  9. Image Trasforms for Binary Images - Medial Axis Transform (MAT). Discrete metrics.  Distance Transform (DT). Thinning algorithms.  
  10. Blob Analysis  - Connected components labeling. Labeling algorithms. Area, baricenter, perimeter. Compactness, Circularity.  MER (Minimum Enclosing Rectangle).  Rectangularity. Orientation. Major and minor axis. Length and width. Eccentricity. Euler number. Moments. Invariant moments. 
  11. Edge Detection  - Step-edge 1D - 2D. Gradient-based edge detection. Roberts operator. Smooth derivatives. Prewitt operator. Sobel operator. Frei-Chen operator.. Mask-matching (Prewitt, Sobel e Kirsch. NMS (Non Maxima Suppression). LOG (Laplacian of Gaussian). Canny edge detector.
  12. Shape Recognition  - Template matching. Similarity measures (SSD, SAD, NCC). Fast template matching.  Hough transform. Hough transform for lines. Hough transform for circles. Generalized Hough transform. Circle detecion by wave propagation. 

 

 

 


Readings/Bibliography

Suggested Readings:

 

  • Gonzales R., Woods R. : “Digital Image Processing”, Second Edition, Prentice-Hall, New-Jersey, USA, 2002.
  • Nalwa V. : “A Guided Tour of Computer Vision”, Addison-Wesley, Mass., USA, 1993.
  • Jain R,, Kasturi R., Schunk B “Machine Vision”, Mc Graw-Hill, 1995
  • Trucco E., Verri A.: “Introductory Techniques for 3D Computer Vision”, Prentice-Hall, 1998.
  • CVonline: Vision Related Books including Online Books and Book Support Sites (http://homepages.inf.ed.ac.uk/rbf/CVonline/books.htm).

Teaching methods

Theory during class hours and practise during lab hours.

Assessment methods

Students must develop a project and write a report. The examination is oral and consists in a discussion on the project/report plus some questions on the theory.

Teaching tools

Course notes can be dowloaded from:

 

http://didattica.arces.unibo.it/index.php?dbName=ldistefano

 

PC and Projector.

SW Tools for practise in lab.

 

 

Links to further information

http://didattica.arces.unibo.it/index.php?dbName=ldistefano

Office hours

See the website of Luigi Di Stefano