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

Computer and Robot Vision I Syllabus

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

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

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

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

Syllabus Homework Submission grade = max(2, 10-2(delay days)); 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

Outline 1.1 Introduction 1.2 Recognition Methodology

Computer and Robot Vision I 1.1 Introduction

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

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 ……

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) …….

1.1 Introduction Applications of Computer Vision Visual Inspection

1.1 Introduction Applications of Computer Vision Object Recognition

1.1 Introduction Applications of Computer Vision Image Indexing

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

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

Fingerprint Identification 1.1 Introduction Applications of Computer Vision Fingerprint Identification

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

Human Activity Recognition 1.1 Introduction Applications of Computer Vision Human Activity Recognition

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

1.1 Introduction

Computer and Robot Vision I 1.2 Recognition Methodology

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

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

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

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

1.2 Recognition Methodology Grouping

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

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

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

1.2 Recognition Methodology Matching (Detection / Classification) Pedestrian Detection

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

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