pattern recognition - yuntech

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PATTERN RECOGNITION (2014 Spring YunTech)

Instructor: Hsuan-Ting Chang, Ph.D.

Class hours: Tuesday. F,G,H;

Email: [email protected]

URL: http://teacher.yuntech.edu.tw/htchang/

Office: EN307

Phone: ext. 4263

Office hours: Thursday 10~12 AM

2014/2/18 YunTech EE Pattern Recognition

http://teacher.yuntech.edu.tw/htchang/

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Text Book

Pattern Classification

R.O. Duda, P.E. Hart and D.G. Stork

Wiley-Interscience Publication, 2001

2nd Edition


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Reference Books

Pattern Recognition: A Statistical

Approach

P.A. Devijver & J. Kittler

Prentice-Hall, 1982

Pattern Recognition: Statistical,

Structural and Neural Approaches

R. Schalkoff

John Wiley & Sons, 1992


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Reference Books

Pattern Recognition Using Neural

Networks

C.G. Looney

Oxford University Press, 1997

Pattern Recognition Principles

J.T. Tou and R.C. Gonzalez

Addison-Wesley, 1981


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Reference Books

Pattern Recognition and Image Analysis

E. Gose, R. Johnsonbaugh and S. Jost

Prentice-Hall, 1996

Numerical Recipes in C The Art of Scientific Computing (2nd)

W.H. Press, S.A. Teukolsky, W.T. Vetterling and B.P. Flannery

Cambridge, 1999


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Important Academic References (Journals)

Pattern Recognition (PR)

Pattern Recognition Letters (PRL)

IEEE Trans. Pattern Analysis and Machine

Intelligence (IEEE PAMI)

IEEE Trans. System, Man and Cybernetics

(IEEE SMC) (Part A, Part B)


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IEEE Trans. on Image Processing (IEEE IP)

IEEE Trans. on Circuits and Systems for Video Technology (IEEE CSVT)

IEEE Trans. on Information Theory (IEEE IT)

IEEE Trans. on Neural Networks (IEEE NN)


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Computer Vision and Image Understanding (CVIU)

Graphical Modeling (GM) (Graphical Modeling and Image Processing)

Image and Vision Computing (IVC)

International Journal of Computer Vision (IJCV)

Machine Vision and Applications (MVA)


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Neural Networks (NN)

影像與識別 (IPPR) 中華民國影像處理與圖形識別會刊


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Important Academic References (Conferences)

International Conference on Pattern Recognition

(ICPR)

International Conference on Image Processing

(ICIP)

IEEE International Conference on Computer

Vision (ICCV)

IEEE Computer Society Conference on Computer

Vision and Pattern Recognition

(CVPR)


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Important Academic References (Conferences)

International Joint Conference on Neural

Networks

(IJCNN)

IPPR Conference on Computer Vision,

Graphics and Image Processing

(Domestic)

International Computer Symposium

(ICS or NCS, Domestic)


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Tentative Grading

Midterm Exam (30%)

Homework, paper presentation, & class

activities (40%)

Term project (30%)

(Implementation and Presentation)


Paper Presentations

Read a magazine article from IEEE, ACM,

OSA, SPIE, or other related academic

societies.

Read at least 1~2 strongly related papers

Write a report/summary (5-page Word file)

about the paper

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Term Project

Goal

Identify the six classes of abnormal regions

about early gastric cancer (EGC) in magnified

NBI images in the stomach

Implementation of the method you use in

Matlab or C++ codes

More details will be described later


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Term Project

Report Content

Abstract

Survey of related work

Proposed methods

Experimental results

Discussions and Conclusions

References

Hand in

Paper sheet and electronic file

Example

Example_of_report.doc


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Term Project

Report

Electronic files

Experimental results

Power point file

The same format as lecture

Test images and results must be included

For presentation

Paper

Print of report


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Course Outline (I) – In textbook

(Ch 1) Introduction

(Ch 2) Bayesian Decision Theory

(Ch 3) Maximum-Likelihood and Bayesian Parameter Estimation

(Ch 4) Nonparametric Techniques

(Ch 5) Linear Discriminant Functions


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Course Outline (II) – Special topics

Support vector machine (SVM)

Principle component analysis (PCA)

Independent component analysis (ICA)

Back-propagation artificial neural network

Optical pattern recognition – architectures and algorithms


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Website for downloading PDF files

http://teacher.yuntech.edu.tw/htchang/PR

1022.htm


http://teacher.yuntech.edu.tw/htchang/PR1022.htm

http://teacher.yuntech.edu.tw/htchang/PR1022.htm

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Definition

Pattern recognition is the study of how

machines can

- observe the environment,

- learn to distinguish patterns of interest,

- make decisions about the categories of

the patterns.


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What is a pattern?

Watanabe defines a pattern “as opposite of

chaos; it is an entity, vaguely defined, that

could be given a name”.

Examples:

- fingerprint,

- handwritten cursive word,

- a human face,

- a speech signal...


examples.pdf

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Types of recognition

Supervised classification: input pattern is

identified as a member of a pre-defined

class.

Unsupervised classification: input pattern

is assigned to a hitherto unknown class.


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Applications (1) Problem

Domain

Application Input Pattern

Classes

Bioinformatics Sequence Analysis DNA/Protein

Sequence

Known types

of genes

Data Mining Search for

meaningful

patterns

Points in

multi-dimensional

space

Compact and

well-separated

clusters

Document

Classification

Internet Search Text document Semantic

categories

Document Image

Analysis

Reading machine

for the blind

Document image Alphanumeric

characters,

words

Industrial

Automation

PCB inspection Intensity or range

image

Defective/non-

defective


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Applications (2)

Problem

Domain

Application Input Pattern

Classes

Multimedia

database

retrieval

Internet Search Video clip Video genres

Biometrics

recognition

Personal

identification

Face, iris,

fingerprint

Authorized users

for access control

Remote sensing Forecasting crop

yield

Multi-spectral

image

Land use

categories

Speech

recognition

Telephone

directory inquiry

Speech

waveform

Spoken words


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Components of a PR System

Data acquisition and pre-processing

Data representation

Decision making


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Pattern Recognition Methods

Template matching

Statistical approach

Syntactic approach

Neural networks


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Template Matching

A template (typically a 2D shape) or a

prototype of the pattern to be recognized is

available.

Compute the similarity between the

template and the pattern to be matched.

Take into account pose (rotation,

translation) and scale changes.


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Issues of concern

Choice of template

Computational complexity

Rigidity assumption (use deformable

template models)


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Statistical Approach

E ach pattern is represented in terms of d

features, and is viewed as a point in a d-

dimensional space

The goal is to choose those features that

allow pattern vectors belonging to different

categories to occupy compact and disjoint

regions in a d-dimensional feature space.


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Syntactic Approach

Use hierarchical structures to represent

complex patterns.

The simplest unit is called: primitives

Complex pattern is represented in terms of

the interrelationships (grammars) between

the primitives.

Grammatical rules can be learned by

training.


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Issues of concern

Can be used in situations where the

patterns have a definite structure such as

EKG waveforms, shape analysis of contours.

However, it’s usually difficult to segment

noisy patterns and infer grammar from the

training set.

May yield a combinatorial explosions of

possibilities to be investigated.


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Neural Networks

Massively parallel computing systems

consisting of an extremely large number of

simple processors with many interconnections.

Can learn complex non-linear input-output

relationships.

Feed-forward networks such as multilayer

perceptron and Radial Basis Function network

are useful for pattern classification.


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Pattern Recognition Models

Approach Representation Recognition

Function

Typical

Criterion

Template

Matching

Samples, pixels,

curves

Correlation,

distance

measure

Classification

error

Statistical Features Discriminant

function

Classification

error

Syntactic or

structural

Primitives Rules, grammar Acceptance

error

Neural Nets Samples, pixels,

features

Network

function

Mean square

error


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Reference

A.K. Jain, R.P.W. Duin and J. Mao,

“Statistical Pattern Recognition: A Review”,

IEEE Transactions on Pattern Analysis and

Machine Intelligence (PAMI), Vol. 22, No. 1,

pp. 4-37, Jan. 2000.


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Homework assignment

Mathematical functions in Appendix A of

the text book

Notation

Linear algebra

Lagrange optimization

Probability theory


pattern recognition - yuntech

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