1 / 21

Optical Pattern Recognition

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.

gordon
Download Presentation

Optical Pattern Recognition

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Optical Pattern Recognition 2003. 7. 16 MAI LAB Kong Jae Hyun MAI-LAB Seminar

  2. Contents • Introduction • Overview • Optical processors • Paper review • Consider MAI-LAB Seminar

  3. 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

  4. Overview The typical pattern recognition system Feature Extraction Feature Compression Classification Test Input Class Decision Training or Learning Training Inputs MAI-LAB Seminar

  5. 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

  6. 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

  7. Practical processors • Geometric moments • Invariant moments • Monomial method • Moment from fourier transform Invariant moments Geometric moments Central moments MAI-LAB Seminar

  8. Practical processors Invariant moments Invariant moments MAI-LAB Seminar

  9. Practical processors Moment from fourier transform MAI-LAB Seminar

  10. Practical processors • Hough transform-based pattern recognition MAI-LAB Seminar

  11. 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

  12. 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

  13. Practical processors • Matched spatial filters • Joint transform correlator MAI-LAB Seminar

  14. Practical processors • Matched spatial filters • Performance measure • Peak-to-correlation energy(PCE)를 performance measure로 이용 MAI-LAB Seminar

  15. 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

  16. 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

  17. 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

  18. Paper review • DNC model을 바탕으로 image의 DC를 구한다. 여기서, CC는 cross correlation, AC는 autocorrelation이고, DC는 discrimination capability를 의미한다. • 실험 환경 MAI-LAB Seminar

  19. Paper review • 실험 결과 • Shape recognition MAI-LAB Seminar

  20. Paper review • Texture recognition MAI-LAB Seminar

  21. 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

More Related