1. Lecture 3 Template Matching Edge Detection

2. 2 Processes for Assignment 1  Understand Image Format  Pre Processing - Gaussian, Mean Filter to clean up the image  Thresholding - Binary Image  Segmentation - Blob Coloring 8-neighbor  Thinning  Template Matching - Similar to convolution Template - “Filter” - Sum Square Difference skeleton กุ้ง

3. 3 Template Matching  You may have a template  Put a template, do subtraction low numbe = good match Number = 0

4. 4  Not only documents  Chips - need numbers  bank checks  in industrial - fixed font is enough  Template Matching - Train your machine for fixed font OCR- Optical Character Recognition

5. 5  Try matching a segment with every template  The lowest score is the best match  Preserve Aspect Ratio width/height Template Matching  Rule of Thumb 8 12 Scale down Template Matching Templates

6. 6  When people say edges, they means object contour Edge Detection - finding edge contours Contour Edge (something to do with shape) Texture Edge

7. 7 Edge Detector Edge - Points in image with a lot of change in intensity Scan Line Edge Points x y Intensity Bluring (Smoothing) Step Edge

8. 8 Simple Edge Detector edge else no edge Edge if  I /  X > 30 First Difference Image

9. 9 First Difference We should blur the image first Use Gaussian Filter to reduce noise

10. 10 First Difference

11. 11 Second Derivative 0 = Edge First Derivative Blurring Second Derivative edge

12. 12 Second Derivative = Edge Detector Using 2nd Derivative

13. 13 Edge Detector Using 2nd Derivative Any zero Crossing Edge Zero-Crossing Change + to -, - to + In 2D

14. 14 Edge Detector by Photoshop (1) 1. Open peppers.jpg 2. Change to Grayscale

15. 15 Edge Detector by Photoshop(2) 3. Custom Filter

16. 16 Edge Detector by Photoshop(2) 3. Custom Filter