Optical pattern recognition
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Optical Pattern Recognition. 2003. 7. 16 MAI LAB Kong Jae Hyun. Contents. Introduction Overview Optical processors Paper review Consider. Introduction. Pattern recognition is the classification of the observed data into one of previously determined classes.

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Optical Pattern Recognition

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Optical pattern recognition

Optical Pattern Recognition

2003. 7. 16

MAI LAB

Kong Jae Hyun

MAI-LAB Seminar


Optical pattern recognition

Contents

  • Introduction

  • Overview

  • Optical processors

  • Paper review

  • Consider

MAI-LAB Seminar


Optical pattern recognition

Introduction

  • Pattern recognition is the classification of the observed data into one of previously determined classes.

  • Three major paradigms for pattern recognition

    • Statistical

    • Syntactic

    • Neural

  • Pattern recognition is usually a computationally demanding problem

    • Optical processing can be an ideal tool for implementing some of the necessary operation

MAI-LAB Seminar


Optical pattern recognition

Overview

The typical pattern recognition system

Feature

Extraction

Feature

Compression

Classification

Test Input

Class

Decision

Training or

Learning

Training Inputs

MAI-LAB Seminar


Optical pattern recognition

Practical processors

  • Two distinct approach

    • Optical pattern recognition using fourier transforms

      • Diffraction pattern sampling

      • Geometric moments

      • Hough transform-based pattern recognition

    • Correlation-based optical pattern recognition

      • Matched Spatial Filters

      • Partial Information Filters

      • Synthetic discriminant functions (SDFs)

MAI-LAB Seminar


Optical pattern recognition

Practical processors

  • Diffraction pattern Sampling

    • 2-D FT(2-dimensional fourier transform)에서 input image의 shift, scale change, rotation 영향을 줄이기 위해서

    • Wedge-ring detector 제안

MAI-LAB Seminar


Optical pattern recognition

Practical processors

  • Geometric moments

    • Invariant moments

    • Monomial method

    • Moment from fourier transform

      Invariant moments

      Geometric moments

      Central moments

MAI-LAB Seminar


Optical pattern recognition

Practical processors

Invariant moments

Invariant moments

MAI-LAB Seminar


Optical pattern recognition

Practical processors

Moment from fourier transform

MAI-LAB Seminar


Optical pattern recognition

Practical processors

  • Hough transform-based pattern recognition

MAI-LAB Seminar


Optical pattern recognition

Practical processors

  • Matched spatial filters

    • Basic Theory

      • Threshold T에 따라 H1, 또는 H0선택

      • Signal to noise ratio 계산

      • 가능한 한 높은 SNR을 얻기 위한 Transfer function H(u)를 구한다.

MAI-LAB Seminar


Optical pattern recognition

Practical processors

  • Matched spatial filters

    • Optical correlators

      • The 2-D correlation of observed image r(x, y) and reference image s(x, y) is given by

MAI-LAB Seminar


Optical pattern recognition

Practical processors

  • Matched spatial filters

    • Joint transform correlator

MAI-LAB Seminar


Optical pattern recognition

Practical processors

  • Matched spatial filters

    • Performance measure

      • Peak-to-correlation energy(PCE)를 performance measure로 이용

MAI-LAB Seminar


Optical pattern recognition

Paper review

Optical pattern recognition with adjustable sensitivity to shape and texture

Elisabet Perez*, Maria Sagrario Millan*, Katarzyna Chalasinska-Macukow**

*Department of Optics and Optometry, University Plitecnica de Catalunya, 08222 Terrassa, Spain

**Division of Information Opics, Institute of Geophysics, Warsaw University, 02-093, Poland

Opics Communications 202 (2002) 239-255

MAI-LAB Seminar


Optical pattern recognition

Paper review

  • Dual nonlinear correlation(DNC) model을 이용한 Optical pattern recognition

  • DNC model

    • 다음과 같이 정의된 power-law nonlinear operater를 이용한 모델

    • DNC는 다음과 같이 정의된다.

MAI-LAB Seminar


Optical pattern recognition

Paper review

  • fs(x), fr(x)를 respected shape로 gs(x), gr(x)를 texture로 정의하면 s(x)와 r(x)는 다음과 같이 나타낼 수 있다.

  • Convolution Theorem에 따라 Forier Transform은 다음과 같이 되고

  • DNC는 다음과 같이 나타낼 수 있게 된다.

MAI-LAB Seminar


Optical pattern recognition

Paper review

  • DNC model을 바탕으로 image의 DC를 구한다.

    여기서, CC는 cross correlation, AC는 autocorrelation이고, DC는 discrimination capability를 의미한다.

  • 실험 환경

MAI-LAB Seminar


Optical pattern recognition

Paper review

  • 실험 결과

    • Shape recognition

MAI-LAB Seminar


Optical pattern recognition

Paper review

  • Texture recognition

MAI-LAB Seminar


Optical pattern recognition

Consider

  • 적당한 feature를 찾아내고, 유용한 filter를 선택하는 과정에서 사람의 직관을 배제할 수는 없는가?

  • Feature나 filter 선택에 대한 A priori는 존재하지 않는가?

  • Sampling을 통한 pattern recognition에서 정보의 왜곡을 줄일 수 있는 방법은 없는가?

MAI-LAB Seminar


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