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
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Computer and Robot Vision I
Syllabus
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Syllabus
 Textbook
 Title: Computer and
Robot Vision, Vol. I
 Authors: R. M.
Haralick and L. G.
Shapiro
 Publisher: Addison
Wesley
 Year: 1992
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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
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Syllabus
 Course Outline
 Advanced Computer Vision
• Statistical Pattern Recognition
•
Adaboost
•
SVM (Support Vector Machine)
•
HMM (Hidden Markov Model)
•
Kalman Filtering
•
Particle Filtering
Tracking
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Classification
Syllabus
 Course Requirements
 Homework Assignment (about 4) (40%)
 Midterm Exam (Nov 21) (20 %)
 Paper Reading (20 %)
 Term Project (30%)
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Syllabus
 Homework Submission
 All homework are submitted through ftp.
• Ftp IP: 163.18.59.110
• Port: 21
• User Name: cv2010
• Password: cv2010
 Scoring Rule:
grade = max(2, 10-2(delay days));
Computer and Robot Vision I
Chapter 1
Computer Vision: Overview
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Outline
 1.1 Introduction
 1.2 Recognition Methodology
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Computer and Robot Vision I
1.1 Introduction
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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 generalpurpose computers.
emulate human vision with computers
dual process of computer graphics
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1.1 Introduction
 Journals
1. International Journal of Computer Vision (IJCV)
2. IEEE Trans. on Pattern Recognition and Machine Intelligence
(PAMI).
3. IEEE Trans. on Image Processing (IP)
4. IEEE Trans. on Circuit Systems for Video Technology (CSVT)
5. Computer Vision and Image Understanding (CVIU)
6. CVGIP: Graphical Models and Image Processing
7. ……
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1.1 Introduction
 Conference
1. International Conference on Computer Vision (ICCV)
2. IEEE Conference on Computer Vision and Pattern
Recognition (CVPR)
3. European Conference on Computer Vision (ECCV)
4. Asian Conference on Computer Vision (ACCV)
5. IEEE Conference on Image Processing (ICIP)
6. IEEE Conference on Pattern Recognition (ICPR)
7. …….
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1.1 Introduction
 Applications of Computer Vision
Visual Inspection
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1.1 Introduction
 Applications of Computer Vision
Object Recognition
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1.1 Introduction
 Applications of Computer Vision
Image Indexing
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1.1 Introduction
 Applications of Computer Vision
Daytime
Nighttime
Intelligent Transportation System
Traffic Monitoring
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1.1 Introduction
 Applications of Computer Vision
Daytime
Nighttime
Intelligent Transportation System
Lane/Vehicle Detection
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1.1 Introduction
 Applications of Computer Vision
Fingerprint Identification
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1.1 Introduction
 Applications of Computer Vision
Face Detection/Recognition
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1.1 Introduction
 Applications of Computer Vision
Human Activity Recognition
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1.1 Introduction
 Challenge Factors
 Object Category
 Object Appearance or Pose
 Background Scene
 Image Sensor
 Viewpoint
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1.1 Introduction
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Computer and Robot Vision I
1.2 Recognition Methodology
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1.2 Recognition Methodology
 Six Steps
 Image Formation
 Conditioning
 Labeling
 Grouping
 Feature Extraction
 Matching (Detection / Classification)
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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
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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
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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
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1.2 Recognition Methodology
 Grouping
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105
120
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(a) Original Images
(b) Lee Approach
(c) Our Approach
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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
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1.2 Recognition Methodology
 Matching (Detection / Classification)
 Determines the interpretation of some related set
of image events
 Associate these events with some given threedimensional object or two-dimensional shape.
e.g. template matching
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1.2 Recognition Methodology
 Matching (Detection / Classification)
Template Matching
Matching
Results
Hierarchical Template
Database
Pedestrian Detection
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1.2 Recognition Methodology
 Matching (Detection / Classification)
Pedestrian Detection
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1.2 Recognition Methodology
 Matching (Detection / Classification)
License Plate Recognition
Traffic Sign Recognition
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www.themegallery.com
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