July 27, 2002 Image Processing for K.R. Precision1 Image Processing Training Lecture 1 by Suthep Madarasmi, Ph.D. Assistant Professor Department of Computer Engineering King Mongkut’s University of Technology Thonburi

July 27, 2002 Image Processing for K.R. Precision2 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 Introduction

July 27, 2002 Image Processing for K.R. Precision3 Input Image Output Image Precessing Image Processing: Image Enhancement Edge Finding Image Segmentation Machine Vision: Scene Description Shape Information Object Recognition What is Image Processing Introduction

July 27, 2002 Image Processing for K.R. Precision4 Image Formation Model 1.0 Image Formation

July 27, 2002 Image Processing for K.R. Precision5 Image Projection Model Z X f x x = Xf/Z; y = Yf/Z 1.0 Image Formation

July 27, 2002 Image Processing for K.R. Precision6 Digital Image Representation 1.0 Image Formation

July 27, 2002 Image Processing for K.R. Precision7 Images Spatial Resolution 1.0 Image Formation

July 27, 2002 Image Processing for K.R. Precision8 Gray Level Resolutions 1.0 Image Formation

July 27, 2002 Image Processing for K.R. Precision9 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. 1.1 Image Formats and Types

July 27, 2002 Image Processing for K.R. Precision10 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) 1.3 Coordinate Transformations

July 27, 2002 Image Processing for K.R. Precision11 Camera Calibration 1.4 Camera Calibration

July 27, 2002 Image Processing for K.R. Precision12 Binary Images 1.0 Binary Images

July 27, 2002 Image Processing for K.R. Precision13 Binary Image Assumed from 2 Sources with Gaussian Noise 2.0 Binary Images

July 27, 2002 Image Processing for K.R. Precision14 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  Histograms are used to look at gray scale distribution for thresholding to binary image  Examples of Histograms 2.1 Histograms

July 27, 2002 Image Processing for K.R. Precision15 Histogram Equalization: Scaling 2.1 Histograms

July 27, 2002 Image Processing for K.R. Precision16 Histogram Equalization 2.1 Histograms

July 27, 2002 Image Processing for K.R. Precision17 P-Tile Method for Threshold 2.2 Thresholding Techniques

July 27, 2002 Image Processing for K.R. Precision18 Region Segmentation for Multiple Objects 2.2 Thresholding Techniques

July 27, 2002 Image Processing for K.R. Precision19 Problem with Single Threshold 2.2 Thresholding Techniques

July 27, 2002 Image Processing for K.R. Precision20 Automatic Threshold Method 2.2 Thresholding Techniques

July 27, 2002 Image Processing for K.R. Precision21 Adaptive Thresholding by Regions 2.2 Thresholding Techniques

July 27, 2002 Image Processing for K.R. Precision22 Rubber Sheet Inspection 2.3 Example: Rubber Inspection

July 27, 2002 Image Processing for K.R. Precision23 Rubber: Multiple Threshold 2.3 Example: Rubber Inspection

July 27, 2002 Image Processing for K.R. Precision24 Rubber: Example Output 2.3 Example: Rubber Inspection

July 27, 2002 Image Processing for K.R. Precision25 Size Filter to Noisy Binary Image 2.4 Binary Image Features

July 27, 2002 Image Processing for K.R. Precision26 Computing Position (Centroid) 2.4 Binary Image Features

July 27, 2002 Image Processing for K.R. Precision27 Iterative Thinning Operations 2.4 Binary Image Features

July 27, 2002 Image Processing for K.R. Precision28 Expanding and Shrinking 2.4 Binary Image Features

July 27, 2002 Image Processing for K.R. Precision29 Horizontal and Vertical Projections 2.4 Binary Image Features

July 27, 2002 Image Processing for K.R. Precision30 Projections for OCR 2.4 Binary Image Features

July 27, 2002 Image Processing for K.R. Precision31 Image Convolution: Filtering 3.0 Image Filtering, Correlation Operations

July 27, 2002 Image Processing for K.R. Precision32 Salt / Pepper and Gaussian Noise 3.2 Common Filters

July 27, 2002 Image Processing for K.R. Precision33 Applying the Mean Filter 3.2 Common Filtes

July 27, 2002 Image Processing for K.R. Precision34 Result of 3x3, 5x5, and 7x7 mean filter 3.2 Common Filters

July 27, 2002 Image Processing for K.R. Precision35 Applying the Median Filter 3.2 Common Filters

July 27, 2002 Image Processing for K.R. Precision36 The Gaussian Filter: Continuous 3.2 Common Filters

July 27, 2002 Image Processing for K.R. Precision37 Template Matching  Template matching is the sum squared difference between image and template  Very similar to Convolution  Used for Recognition: OCR and Others 3.3 Template Matching

July 27, 2002 Image Processing for K.R. Precision38 A Discreet (Digital) Gaussian Filter 3.2 Common Filters

July 27, 2002 Image Processing for K.R. Precision39 Edges: First & Second Derivatives 3.4 Edge Detection

July 27, 2002 Image Processing for K.R. Precision40 LOG (Laplacian of Gaussian) Filter 3.4 Edge Detection

July 27, 2002 Image Processing for K.R. Precision41 LOG Masks 3.4 Edge Detection

July 27, 2002 Image Processing for K.R. Precision42 Paper Inspection 3.4 Example: Paper Inspection

July 27, 2002 Image Processing for K.R. Precision43 Paper Inspection: Defects 3.4 Example: Paper Inspection

July 27, 2002 Image Processing for K.R. Precision44 Texture Feature Extraction  Texture: Statistical Distribution of Gray  Co-Occurence Matrix captures distribution  Texture Measures from Co-Occurence: Entropy Energy Homogeneity 3.6 Texture Features

July 27, 2002 Image Processing for K.R. Precision45 Food Inspection: Texture 3.7 Example: Food Inspection

July 27, 2002 Image Processing for K.R. Precision46 Food Texture: Method & Results หา Co- occurrence matrix ที่ d[3,3] และ d[-3,3] กรอง เฉลี่ย LoG 3.7 Example: Food Inspection