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Optical Pattern RecognitionPowerPoint Presentation

Optical Pattern Recognition

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## PowerPoint Slideshow about ' Optical Pattern Recognition' - gordon

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- 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

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The typical pattern recognition system

Feature

Extraction

Feature

Compression

Classification

Test Input

Class

Decision

Training or

Learning

Training Inputs

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- 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)

- Optical pattern recognition using fourier transforms

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- Diffraction pattern Sampling
- 2-D FT(2-dimensional fourier transform)에서 input image의 shift, scale change, rotation 영향을 줄이기 위해서
- Wedge-ring detector 제안

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- Geometric moments
- Invariant moments
- Monomial method
- Moment from fourier transform
Invariant moments

Geometric moments

Central moments

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- 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.

- Basic Theory

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- Matched spatial filters
- Optical correlators
- The 2-D correlation of observed image r(x, y) and reference image s(x, y) is given by

- Optical correlators

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- Matched spatial filters
- Performance measure
- Peak-to-correlation energy(PCE)를 performance measure로 이용

- Performance measure

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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

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- Dual nonlinear correlation(DNC) model을 이용한 Optical pattern recognition
- DNC model
- 다음과 같이 정의된 power-law nonlinear operater를 이용한 모델
- DNC는 다음과 같이 정의된다.

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- 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.

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- DNC model을 바탕으로 image의 DC를 구한다.
여기서, CC는 cross correlation, AC는 autocorrelation이고, DC는 discrimination capability를 의미한다.

- 실험 환경

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- 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?

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