1. An Introduction to Computer Vision George J. Grevera, Ph.D. http://www.sju.edu/~ggrevera/csc2151-5155/index.html

2.  How does the Sony AIBO dog find its way “home” (to its charging stations)?  How does the yellow, virtual first-down line work?  science of analyzing images and videos in order to recognize or just model 3D objects, persons, and environments  How do cameras perform image stabilization?  In this class, we study the underlying principles and produce working examples. Computer Vision

3. Visualization  Computer graphics  Computer / Machine vision  Image understanding  Database and communications  Computer games  Medical Imaging  Image processing  Pattern recognition

4. Ansel Adams: El Capitan

5. Bill Brandt: Lambeth Walk

6. George Grevera: Horse Fishing

8. Segmentation

9. Segmentation  recognition  delineation

10. Models from CT (Computed Tomography) head data

11. Model buiding

12. 3D visualization of CT head data

14. MRI Diffusion Tensor Imaging

15. Registration

16. Registration  A.K.A.:  alignment  warping  mosaicing  morphing  fusion

17. Simple MRI Example (rigid)

18. Deformable

19. prepost (no reg) post (after Thirion’s Demons registration) diff

20. Non-medical visualization

21. What is a distance transform?  Input: an binary image  Output: a grey image  for all points...  assign the minimum distance from that particular point to the nearest point on the border of an object

22. Applications of distance transforms:  skeletonization/medial axis transform  interpolation  registration  efficient ray tracing  classification of plant cells  measuring cell walls  characterize spinal cord atrophy

23. Experimental Results binary input image distance transform result

25. Application areas: Object recognition Tracking Registration Fusion Intelligence, industrial and medical projects FBI Automatic Fingerprint Identification System FOCUS: Monitor change in satellite images FBI Facial Reconstruction Software: Target Junior Image Understanding

30. Textbook  L.G. Shapiro, G.C. Stockman, Computer Vision, Prentice-Hall, 2001.

31. Topics  Imaging and image representation  Sensors  Problems (including noise)  Image file formats  Color representation and shading  Binary image analysis  Connected components  Morphology  Region properties

32. Topics  Pattern recognition concepts  Classifiers and classification  Filtering (enhancing) images  Segmentation  Registration (matching)

33. Topics  Registration  Texture representation and segmentation  Motion from sequences of 2D images

34. Homework  Read chapter 1.  Hand in 1.1, 1.2, and 1.3.

35. Survey questions… 1. Do you have access to a digital camera?

36. 2. Write a function that, given a 2D array, returns a 1D array of the sum of the rows in the 2D array. In Java: int[] sumOfRows ( int m[][], int rows, int cols ) { …} In C++: #define Rows 150 #define Cols 50 int* sumOfRows ( int m[Rows][Cols] ) { …}or int* sumOfRows ( int* m ) { …}