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CPE 631

CPE 631. Image Processing and Computer Vision. Syllabus - Overview. To introduce students to the concepts of computer vision touching on areas of computer graphics image processing artificial intelligence biological vision neural networks pattern recognition robot vision.

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CPE 631

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  1. CPE 631 Image Processing and Computer Vision

  2. Syllabus - Overview To introduce students to the concepts of computer vision touching on areas of • computer graphics • image processing • artificial intelligence • biological vision • neural networks • pattern recognition • robot vision

  3. Syllabus - Application • Most applications involve us to use eyes • Good robot should be able to see • Engineering wise control environment • General environment unconstraint • Simulation - Driving on a highway • Millions of Research Topic

  4. Syllabus - Grading • Programming Assignments 25% • Term Project 15% • Best 8 of 10 Quiz       60%

  5. Syllabus – Term Project • The term project will be on a computer vision topic of choice requiring: • Proposal: 1-2 pages and 5-10 min presentation • Project Report: 4-8 pages and 20-25 min presentation

  6. Syllabus – Assignment 1. Simple Thai OCR Competition (10%) 2. Three Dimensional Digitizer Application: Shape from Stereograms (15%)

  7. Syllabus – Reference Books Jain R., R. Kasturi, and B.G. Schunck, Machine Vision, McGraw-Hill.

  8. Syllabus – Topics 1. Overview of Computer Vision Course and Assignments Overview Image Formation and Sensing 3-D Computer Graphics and Visual Realism Digital Images: bw, grayscale, and color 2. Binary Image Processing: Low-level Image Filtering and Edge Detection 3. Regions, Image Segmentation, Texture Segmentation Blob Coloring Contours and Boundary Detection General Hough Technique and Applications

  9. Syllabus – Topics 4. 3-D Computer Graphics Models Revisited Optics, Shading, Curves and Surfaces Energy Minimization and Relaxation Techniques Bayesian Probability, The Pixel Lattice 5. Depth & Shape from X Texture, Shading, and Stereo 6. Calibrations Depth from Binocular Stereo 3-D Volume Rendering from cross-section images

  10. Syllabus – Topics 7. Dynamic Vision Motion Field and Flow: Gradient vs. Matching Methods 8. Structure from Motion, Object Tracking 9. Object Recognition Models

  11. Computer Vision • Divided to 3 Levels: • Low Level • Mid Level • High Level

  12. Low-Level • Edge Detection • Image Processing (Pre-Processing) Image Processing Image Image

  13. Mid-Level (Perception) • Segmentation/Grouping Satellite Images OCR กุ้ง

  14. Mid-Level (Perception) • Finding Depth, Shape, Light, Materials • “Inverse of Computer Graphics” Computer Graphics Computer Vision CG Scence -shape -material – color, shiny, transparency, texture, etc. -light -camera Image CV Image 3D Scence

  15. High-Level (Use Knowledge) • Pattern Recognition • Obstacle Avoidance • Grasping • Object Recognition • Etc.

  16. 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 Image Formation • Intensity Image (with value 0..255) • 0 – black, 100 – gray, 255 - white • For example, an ideal white paper

  17. 222 219 220 200 218 222 220 220 221 200 200 200 220 219 198 198 198 200 210 190 Noise in Sensor • Scanned white papermight not be all 200 • It can be, for example:

  18. 222 219 220 200 218 222 220 220 221 200 200 200 220 219 198 198 198 200 210 190 Gaussian Additive Noise G(20,5) • G(m,s)

  19. Salt/Pepper Noise • Random Noise • Black – pepper • White - Salt

  20. To Get Rid of Noise • Gaussian Noise • Use Gaussian Filter • Salt/Pepper Noise • Use Median Filter

  21. Filter F I’ I Filter I x F = I’ Input Image Output Image Convolution

  22. 50 45 47 52 54 49 49 50 52 49 52 50 50 51 1/4 1/4 51 51 49 48 1/4 1/4 51 52 51 45 48 50 51 49 48 50 47 54 47 52 Filter- Example (50*1/4)+(45*1/4)+ (49*1/4)+(52*1/4) 2 x 2 Mean Filter Input Image Output Image

  23. 50 50 45 47 52 54 50 50 45 47 52 54 1/9 1/9 1/9 49 48 49 51 1/9 1/9 1/9 49 49 52 50 50 51 50 50 49 50 1/9 1/9 1/9 51 51 52 51 45 46 50 50 50 50 50 50 47 54 47 52 Filter- Example (50*1/9)+(50*1/9)+(45*1/9)+ (50*1/9)+(50*1/9)+(45*1/9)+ (49*1/9)+(49*1/9)+(52*1/9) 3 x 3 Mean Filter Input Image Output Image Mirror

  24. 1/4 1/4 1/4 1/4 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 Mean Filter (Average) 2 x 2 Mean Filter 3 x 3 Mean Filter

  25. 1 1 2 2 2 1 1 1 2 2 4 2 2 1 2 2 4 8 4 2 2 2 4 8 16 8 4 2 2 2 4 8 4 2 2 1 2 2 4 2 2 1 1 1 2 2 2 1 1 Gaussian Filter

  26. Gaussian Filter • Highest in the middle • Reducing factored by s s high – reduce slow s low – reduce fast

  27. Median Filter • No convolution • Arrange all values, then use the middle one • For example, • 20, 21, 32, 23, 17, 19 , 20 • Order by value • 17 • 19 • 20 • 20 • 21 • 23 • 32 • Use 20 • 20, 22, 32, 23, 17, 19 , 20,24 • Order by value • 17 • 19 • 20 • 20 • 22 • 23 • 24 • 32 • Use 21 Average the middle (20+22)/2 = 21 Use the middle

  28. 20 23 22 71 22 22 22 22 24 24 22 21 70 22 24 22 23 22 22 21 24 56 100 21 20 20 20 20 23 20 22 22 21 32 20 20 Median Filter - Example • 2 x 2 Median Filter

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