Professor: Marek A. Perkowski, Electrical Engineering.

ROBOT VISION AND PERCEPTION


This class starts from easy robot vision and continues with advanced topics such as Fast Fourier Transforms and Radon Transforms. All these theories are used in practical robot vision projects.

Goal of the class.


Students should learn elementary, medium and some advanced topics in robot vision and perception to be used in software, hardware and integration/robotic projects. This is the second class in robotics but can be taken with no prerequisities in case that the students knows programming. Students should get practical understanding how robot vision algorithms are used in robot systems.

Where is this knowledge useful?


Previous students who have done projects found this class useful to find industrial positions in the following areas: robot vision, flexible automation systems with vision, medical image processing, hardware design of image processors, bank image processing, general software design, industrial board-level design, design automation.

Software.


Software used in class depends on every year projects. This quarter the projects will use OpenCV, Visual Basic and C++.

Mandatory Lectures for year 2012.

  1. 0001. Introduction to Morphological Operators. In PPT format.
  2. 0002. Mathematics of Binary Morphology. ppt
  3. 0002_A. Binary Morphology and Use in Robotic Path Planning. ppt
  4. 0003.A. Big Example of morpology and measurements on image. ppt
  5. 0004. Introduction to edge detection feature extraction. ppt
  6. 0005. Labeling and sequential algorithms. Robot Soccer. ppt
  7. 0006. Quad Trees and Octtrees. ppt
  8. 0007. Partitioning and Thresholding. ppt
  9. 0008. Shape Representation1. ppt
  10. 0009. Image Processing for Melanoma Cancer. ppt
  11. 0010. Histogramming socccer vision. ppt
  12. 0011. Matlab Image Processing. ppt
  13. 0012. Introduction to color image processing. ppt
  14. 0014. Matlab Image Examples with color processing. ppt
  15. 0015. Matlab hit and miss binary. ppt
  16. 0020. Edge Detection Algorithm by Canny. pptx
  17. 0021. Edge Detection with Sobel Algorithm. ppt
  18. 0022. Edge Enhancement. ppt
  19. 0024. Convolution low pass filtering. ppt
  20. 1001. B. Introduction to Fourier Transform, 1D continuous. pptx
  21. 1001. C. Sampling and frequencies. ppt
  22. 1002. A. Continuous Fourier Transform Notations and theorems. pptx
  23. 1002. B. Sampling Fourier Transform. ppt
  24. 1003. B. Shift Invariant Linear Systems. ppt
  25. 1004. A. Orthogonal, Separable and Fourier Mathematics. ppt
  26. 1006. 2D spectral transforms. Examples. ppt
  27. 1007. Combination of Classifiers for Indoor Room Recognition. pptx
  28. 1008. Introduction to Generalized Hough Transform.ppt
  29. 1009 Generalized Hough Transform. 1. ppt
  30. 1009. A. Generalized Hough Transform 2. ppt
  31. 1010. Radon Transform. Easy principles. ppt
  32. 1011. Medical Imaging - acquisition and Radon Transform. ppt
  33. 1012. Biometry and terrorists. pdf