1. Computer and Robot Vision I
黃世勳 (Shih-Shinh Huang)
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 Homework Submission grade = max(2, 10-2(delay days));
All homework are submitted through ftp.
Ftp IP:
Port: 21
User Name: cv2010
Password: cv2010
Scoring Rule:
grade = max(2, 10-2(delay days));

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)
IEEE Trans. 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. Intelligent Transportation System
1.1 Introduction
Applications of Computer Vision
Daytime
Nighttime
Intelligent Transportation System
Traffic Monitoring

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

19. Fingerprint Identification
1.1 Introduction
Applications of Computer Vision
Fingerprint Identification

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

21. Human Activity Recognition
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.
Media Filtering
Histogram Adjustment

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

35. Thank You !