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Image Processing Training Lecture 1

Image Processing Training Lecture 1. by Suthep Madarasmi, Ph.D. Assistant Professor Department of Computer Engineering King Mongkut’s University of Technology Thonburi. Introduction. Presentation Overview . 1.0 Image Formation 1.1 Image Formats and Types 1.2 Image Formation Models

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Image Processing Training Lecture 1

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  1. Image Processing Training Lecture 1 by Suthep Madarasmi, Ph.D. Assistant Professor Department of Computer Engineering King Mongkut’s University of Technology Thonburi Image Processing for K.R. Precision

  2. Introduction Presentation Overview 1.0 Image Formation 1.1 Image Formats and Types 1.2 Image Formation Models 1.3 Coordinate Transformations 1.4 Camera Calibration 2.0 Binary Images 2.1 Histograms 2.2 Thresholding Techniques 2.3 Example: Rubber Inspection 2.4 Binary Image Features 2.5 Measurement and Accuracy 3.0 Image Filtering 3.1 Convolution Operation 3.2 Common Filters 3.3 Template Matching 3.4 Edge Detection 3.4 Example: Paper 3.6 Texture Features 3.7 Example: Food Image Processing for K.R. Precision

  3. Introduction What is Image Processing Image Precessing Output Image Input Image • Image Processing: • Image Enhancement • Edge Finding • Image Segmentation • Machine Vision: • Scene Description • Shape Information • Object Recognition Image Processing for K.R. Precision

  4. 1.0 Image Formation Image Formation Model Image Processing for K.R. Precision

  5. 1.0 Image Formation X f x Z x = Xf/Z; y = Yf/Z Image Projection Model Image Processing for K.R. Precision

  6. 1.0 Image Formation Digital Image Representation Image Processing for K.R. Precision

  7. 1.0 Image Formation Images Spatial Resolution Image Processing for K.R. Precision

  8. 1.0 Image Formation Gray Level Resolutions Image Processing for K.R. Precision

  9. 1.1 Image Formats and Types Digital Image Formats • RGB Images • CMYK Images • 256 Indexed Color Images • 16 Indexed Color Images • Gray Scale Images (8 bit) • Gray Scale Images (4 bit) • Black and White Images • Image Types: GIF, JPG, BMP, TIF, Multi-Page TIF, PDF, PS, RTF, etc. Image Processing for K.R. Precision

  10. 1.3 Coordinate Transformations Coordinate Transformations • 3-D Transformations: Rotation & Translation • Object Coordinates 3D • World Coordinates 3D • Camera Coordinates 3D • Image Coordinates 2D, Continuous Cartesian • Image Coordinates 2D, Discreet Cartesian (x, y) • Image Coordinates 2D, Device Independent (r, c) • Image Coordinates 2D, Device Coordinates (x, y) Image Processing for K.R. Precision

  11. 1.4 Camera Calibration Camera Calibration Image Processing for K.R. Precision

  12. 1.0 Binary Images Binary Images Image Processing for K.R. Precision

  13. 2.0 Binary Images Binary Image Assumed from 2 Sources with Gaussian Noise Image Processing for K.R. Precision

  14. 2.1 Histograms Image Histogram • A histogram is the count of each gray scale within the image • Histogray may represent P[i], where i is gray value between 0..255. • Histograms are used to look at gray scale distribution for thresholding to binary image • Examples of Histograms Image Processing for K.R. Precision

  15. 2.1 Histograms Histogram Equalization: Scaling Image Processing for K.R. Precision

  16. 2.1 Histograms Histogram Equalization Image Processing for K.R. Precision

  17. 2.2 Thresholding Techniques P-Tile Method for Threshold Image Processing for K.R. Precision

  18. 2.2 Thresholding Techniques Region Segmentation for Multiple Objects Image Processing for K.R. Precision

  19. 2.2 Thresholding Techniques Problem with Single Threshold Image Processing for K.R. Precision

  20. 2.2 Thresholding Techniques Automatic Threshold Method Image Processing for K.R. Precision

  21. 2.2 Thresholding Techniques Adaptive Thresholding by Regions Image Processing for K.R. Precision

  22. 2.3 Example: Rubber Inspection Rubber Sheet Inspection Image Processing for K.R. Precision

  23. 2.3 Example: Rubber Inspection Rubber: Multiple Threshold Image Processing for K.R. Precision

  24. 2.3 Example: Rubber Inspection Rubber: Example Output Image Processing for K.R. Precision

  25. 2.4 Binary Image Features Size Filter to Noisy Binary Image Image Processing for K.R. Precision

  26. 2.4 Binary Image Features Computing Position (Centroid) Image Processing for K.R. Precision

  27. 2.4 Binary Image Features Iterative Thinning Operations Image Processing for K.R. Precision

  28. 2.4 Binary Image Features Expanding and Shrinking Image Processing for K.R. Precision

  29. 2.4 Binary Image Features Horizontal and Vertical Projections Image Processing for K.R. Precision

  30. 2.4 Binary Image Features Projections for OCR Image Processing for K.R. Precision

  31. 3.0 Image Filtering, Correlation Operations Image Convolution: Filtering Image Processing for K.R. Precision

  32. 3.2 Common Filters Salt / Pepper and Gaussian Noise Image Processing for K.R. Precision

  33. 3.2 Common Filtes Applying the Mean Filter Image Processing for K.R. Precision

  34. 3.2 Common Filters Result of 3x3, 5x5, and 7x7 mean filter Image Processing for K.R. Precision

  35. 3.2 Common Filters Applying the Median Filter Image Processing for K.R. Precision

  36. 3.2 Common Filters The Gaussian Filter: Continuous Image Processing for K.R. Precision

  37. 3.3 Template Matching Template Matching • Template matching is the sum squared difference between image and template • Very similar to Convolution • Used for Recognition: OCR and Others Image Processing for K.R. Precision

  38. 3.2 Common Filters A Discreet (Digital) Gaussian Filter Image Processing for K.R. Precision

  39. 3.4 Edge Detection Edges: First & Second Derivatives Image Processing for K.R. Precision

  40. 3.4 Edge Detection LOG (Laplacian of Gaussian) Filter Image Processing for K.R. Precision

  41. 3.4 Edge Detection LOG Masks Image Processing for K.R. Precision

  42. 3.4 Example: Paper Inspection Paper Inspection Image Processing for K.R. Precision

  43. 3.4 Example: Paper Inspection Paper Inspection: Defects Image Processing for K.R. Precision

  44. 3.6 Texture Features Texture Feature Extraction • Texture: Statistical Distribution of Gray • Co-Occurence Matrix captures distribution • Texture Measures from Co-Occurence: • Entropy • Energy • Homogeneity Image Processing for K.R. Precision

  45. 3.7 Example: Food Inspection Food Inspection: Texture Image Processing for K.R. Precision

  46. 3.7 Example: Food Inspection หา Co-occurrencematrixที่ d[3,3]และ d[-3,3] กรองเฉลี่ย LoG Food Texture: Method & Results Image Processing for K.R. Precision

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