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Computer and Robot Vision I

黃世勳 (Shih-Shinh Huang)

Email : poww@ccms.nkfust.edu.twOffice: B322-1Office 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

Classification

Tracking

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Syllabus

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

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));

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Computer and Robot Vision I

Chapter 1Computer 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 general-purpose computers.

emulate human vision with computersdual process of computer graphics

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1.1 Introduction

Journals1. 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 Processing7. ……

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1.1 Introduction

Conference1. 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

Intelligent Transportation SystemTraffic Monitoring

Daytime Nighttime

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1.1 Introduction

Applications of Computer Vision

Intelligent Transportation SystemLane/Vehicle Detection

Daytime Nighttime

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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

Grouping90 120 150105 135

(b) Lee Approach

(c) Our Approach

(a) Original Images

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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 three-

dimensional object or two-dimensional shape.

e.g. template matching

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1.2 Recognition Methodology

Matching (Detection / Classification)

Hierarchical Template Database

Template Matching

Matching Results

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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