Instructor: Alper Yilmaz, PhD
Office: Bolz Hall 214C
Lecture Day&Time: (tentative) Tuesday-Friday 4:30pm-6:00pm

Cource Book:
T. Schenk, Digital Photogrammetry Vol. I, TerraScience, 1999.
For ordering contact Kyoungjin Park: park.764 at osu.edu

Cource Objective: GS629 provides students with both introductory theoretical and practical aspects of digital photogrammetry.

Cource Syllabus

Lecture Slides:

• Lecture 1 (3/25/2008)
  • Introduction and Course Layout
• Lecture 2 (3/28/2008)
  • Signals
  • Systems
• Lecture 3 (4/1/2008)
  • Frequency representation
  • Fourier transform
• Lecture 4 (4/4/2008)
  • Discrete Fourier Transform
  • Digital Filters
• Lecture 5 (4/8/2008)
  • Digital Images
• Lecture 6 (4/11/2008)
  • Image Enhancement
• Lecture 7 (4/15/2008)
  • Image Smoothing
  • Image Sharpening
  • High boost filtering
• Lecture 8 (4/18/2008)
  • Derivatives
  • Edge Detection
• Lecture 9 (4/22/2008)
  • Image Transformations
  • Image segmentation
• Lecture 10 (4/25/2008)
  • Human Vison
  • Computer Vision
• Lecture 11 (4/29/2008)
  • Radiometry
  • Photometry
• Lecture 12 (5/2/2008)
  • Electronic imaging systems
• Lecture 13 (5/6/2008)
  • DWS
• Lecture 14 (5/9/2008)
  • Image matching
• Lecture 15 (5/13/2008)
  • Image matching
• Lecture 16 (5/16/2008)
  • Area based matching
• Lecture 17 (5/20/2008)
  • Feature matching
  • Hough transform
• Lecture 18 (5/23/2008)
  • Generalized Hough transform
• Lecture 19 (5/27/2008)
  • Relational matching
  • Image rectification
• Lecture 20 (5/30/2008)
  • Project discussion

Lecture 3 Slides 15 and 19 due date 4/11/2008

Lecture 4 Slide 10 due date 4/15/2008

Lecture 9 Slide 11 due date 4/29/2008

Lecture 5 Slide 28 due date 4/15/2008

Lecture 7 Slide 20 due date 4/25/2008

Lecture 8 Slide 26 due date 4/29/2008: Implementation of Sobel operator

Various Lecture Slides due date 4/29/2008: Implementation of Convolution operation

Lecture 9 Slide 18 due date 5/9/2008: Implementation of Histogram based image segmentaion

Lecture 16 due date 6/3/2008: Implementation of least squares based area matching

• Will be graded as your final project
• No extensions
• 70% of grade is on the implementation of translational area matching
• 30% of grade is on the implementation of affine area matching
• Test images
• Image viewer for windows

More information on Discrete Fourier Transform