Computer and Robot Vision I

1. Computer and Robot Vision I 黃世勳(Shih-Shinh Huang) Email : poww@ccms.nkfust.edu.tw Office: B322-1 Office Hour: (三)9:10 ~ 12:00

2. Computer and Robot Vision I • Syllabus

3. Syllabus • Textbook • Title: Computer and Robot Vision, Vol. I • Authors: R. M. Haralick and L. G. Shapiro • Publisher:Addison Wesley • Year: 1992

4. Syllabus • Course Outline • Basic Computer Vision • Computer Vision Overview • Binary Machine Vision: Thresholding and Segmentation • Binary Machine Vision: Region Analysis • Mathematical Morphology • Representation and Description • 3D Computer Vision

5. Syllabus • Course Outline • Advanced Computer Vision • Statistical Pattern Recognition • Adaboost • SVM (Support Vector Machine) • HMM (Hidden Markov Model) • Kalman Filtering • Particle Filtering Classification Tracking

6. Syllabus • Course Requirements • Homework Assignment (about 4) (40%) • Midterm Exam (Nov 21) (20 %) • Paper Reading (20 %) • Term Project (30%)

7. Syllabus grade = max(2, 10-2(delay days)); • Homework Submission • All homework are submitted through ftp. • Ftp IP: 163.18.59.110 • Port: 21 • User Name: cv2010 • Password: cv2010 • Scoring Rule:

8. Computer and Robot Vision I • Chapter 1 • Computer Vision: Overview

9. Outline • 1.1 Introduction • 1.2 Recognition Methodology

10. Computer and Robot Vision I • 1.1 Introduction

11. 1.1 Introduction • Definition of Computer Vision • Develop the theoretical and algorithmic basis to automatically extract and analyze useful information from an observed image, image set, or image sequence made by special-purpose or general-purpose computers. emulate human vision with computers dual process of computer graphics

12. 1.1 Introduction • Journals • International Journal of Computer Vision (IJCV) • IEEETrans. on Pattern Recognition and Machine Intelligence (PAMI). • IEEE Trans. on Image Processing (IP) • IEEE Trans. on Circuit Systems for Video Technology (CSVT) • Computer Vision and Image Understanding (CVIU) • CVGIP: Graphical Models and Image Processing • ……

13. 1.1 Introduction • Conference • International Conference on Computer Vision (ICCV) • IEEE Conference on Computer Vision and Pattern Recognition (CVPR) • European Conference on Computer Vision (ECCV) • Asian Conference on Computer Vision (ACCV) • IEEE Conference on Image Processing (ICIP) • IEEE Conference on Pattern Recognition (ICPR) • …….

14. 1.1 Introduction • Applications of Computer Vision Visual Inspection

15. 1.1 Introduction • Applications of Computer Vision Object Recognition

16. 1.1 Introduction • Applications of Computer Vision Image Indexing

17. 1.1 Introduction • Applications of Computer Vision Daytime Nighttime Intelligent Transportation System Traffic Monitoring

18. 1.1 Introduction • Applications of Computer Vision Daytime Nighttime Intelligent Transportation System Lane/Vehicle Detection

19. 1.1 Introduction • Applications of Computer Vision Fingerprint Identification

20. 1.1 Introduction • Applications of Computer Vision Face Detection/Recognition

21. 1.1 Introduction • Applications of Computer Vision Human Activity Recognition

22. 1.1 Introduction • Challenge Factors • Object Category • Object Appearance or Pose • Background Scene • Image Sensor • Viewpoint

23. 1.1 Introduction

24. Computer and Robot Vision I • 1.2 Recognition Methodology

25. 1.2 Recognition Methodology • Six Steps • Image Formation • Conditioning • Labeling • Grouping • Feature Extraction • Matching (Detection / Classification)

26. 1.2 Recognition Methodology • Conditioning • Observed image is composed of an informative pattern modified by uninteresting variations that typically add to or multiply the informative pattern. Histogram Adjustment Media Filtering

27. 1.2 Recognition Methodology • Labeling • Suggest that the informative pattern has structure as a spatial arrangement of events. • Each spatial event is a set of connected pixels. • Label pixels with the kinds of primitive spatial events. e.g. thresholding, edge detection, corner finding

28. 1.2 Recognition Methodology • Grouping • Identify the events by collecting together or identifying maximal connected sets of pixels participating in the same kind of event. e.g. segmentation, edge linking

29. 1.2 Recognition Methodology • Grouping

30. 1.2 Recognition Methodology • Feature Extraction • Compute for each group of pixels a list of properties. • Area • Orientation • …. • Measure relationship between two or more groups • Topological Relationship • Spatial Relationship

31. 1.2 Recognition Methodology • Matching (Detection / Classification) • Determines the interpretation of some related set of image events • Associate these events with some given three-dimensional object or two-dimensional shape. e.g. template matching

32. 1.2 Recognition Methodology • Matching (Detection / Classification) Matching Results Hierarchical Template Database Pedestrian Detection

33. 1.2 Recognition Methodology • Matching (Detection / Classification) Pedestrian Detection

34. 1.2 Recognition Methodology • Matching (Detection / Classification) License Plate Recognition Traffic Sign Recognition

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