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Computer Vision I Introduction

Computer Vision I Introduction. Raul Queiroz Feitosa. Content. What is CV? CV Applications Fundamental Steps From DIP to CV Course Content. What is Computer Vision.

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Computer Vision I Introduction

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  1. Computer Vision IIntroduction Raul Queiroz Feitosa

  2. Content • What is CV? • CV Applications • Fundamental Steps • From DIP to CV • Course Content Introduction

  3. What is Computer Vision • “Computer Vision is the science that develops the theoretical and algorithmic basis by which useful information about the world can be automatically extracted and analyzed from an observed image, image set, or image sequence from computations made by a ... computer.” R. B. Haralick, L.G. Shapiro Introduction

  4. Applications • Medical Image Analysis • Analysis of Remote Sensing Data • Biometrics • Security • Microscopy • Industrial Inspection • … Introduction

  5. Microscopy Robot Vision Remote Sensing Medical Images Biometrics Industry Security Applications much more Introduction

  6. LVC Topics: Face Recognition Introduction

  7. LVC Topics: Face Recognition Registro Único de Identidade Civil RIC Controle de Passaportes Controle de Acesso Aplicações Criminais Introduction

  8. LVC Topics: Face Recognition from Video Frontal View Tracking Suspect Behavior Recognition Introduction

  9. LVC Topics: Medical Image Analysis Introduction

  10. LVC Topics: Remote Sensing Introduction

  11. SAR R99B (SIPAM) Illegal runways Alves et al., 2009 LVC Applications: Remote Sensing Geometric features are used to distinguish landing lanes from other targets in the forest. Introduction

  12. digital image (pixels) physical image gray level (quem / o que) Physical image digital image Post- processing Feature extraction Enhancement Segmentation Recognition Acquisition Fundamental Steps Image Acquisition: digitizes the electromagnetic energy Introduction

  13. Post- processing Feature extraction Enhancement Segmentation Recognition Acquisition Fundamental Steps Image Enhancement: improves image quality digital image digital image Introduction

  14. Post- processing Feature extraction Enhancement Segmentation Recognition Acquisition Fundamental Steps • Segmentation: partitions the image into meaningfull objects digital image segments Introduction

  15. Post- processing Feature extraction Enhancement Segmentation Recognition Acquisition Fundamental Steps Post-Processing: support segmentation/description segments segments Introduction

  16. x1=(x11 … x1n)T · · · xi=(xi1 … xin)T · · · xp=(xp1 … xpn)T Post- processing Feature extraction Enhancement Segmentation Recognition Acquisition Fundamental Steps Description: converts the data into a form suitable for processing segments description Introduction

  17. paprika pepper cabbage · · · · · · Post- processing Feature extraction Enhancement Segmentation Recognition Acquisition Fundamental Steps Recognition: assigns a label to the image objects x1=(x11 … x1n)T · · · xi=(xi1 … xin)T · · · xp=(xp1 … xpn)T description label Introduction

  18. DIP Post- processing Feature extraction Enhancement Segmentation Recognition Acquisition From DIP to CV Digital Image Processing • Input and output are images! • From image up to recognition! DIP Introduction

  19. Image Analysis Post- processing Feature extraction Enhancement Segmentation Recognition Acquisition From DIP to CV Image Analysis/Understanding • From segmentation up to recognition. Introduction

  20. Post- processing Feature extraction Enhancement Segmentation Recognition Acquisition From DIP to CV Computer Vision • Tries to emulate human intelligence. • Emphasis on 3D analysis. Computer Vision Introduction

  21. Post- processing Feature extraction Enhancement Segmentation Recognition Acquisition From DIP to CV Process Levels • Low-level: input and outputs are images • Mid-level: image as input and attributes as output. • High-level: “making sense” of an ensemble of objects High Low Mid Introduction

  22. Image Analysis develops methods and algorithms able to extract automatically useful information about the world. Image Analysis Introduction

  23. Computer Graphics develps techniques for visualization and manipulation of ideas that exist only conceptually or in form of mathematical description, but not as concrete object. Computer Graphics Introduction

  24. Course Content Main: • Introduction • Digital Image Fundamentals • Image Enhancement in Spatial Domain • Image Enhancement in Frequency Domain • Morphological Image Processing • Segmentation • Representation and Description • Object Recognition Appendices: • Mathematical Foundation • Dimensionality Reduction (top) Introduction

  25. Bibliography • R. G. Gonzalez, R. E. Woods, Digital Image Processing; Prentice Hall, 3rd Ed., 2007 • R. G. Gonzalez, R. E. Woods, Digital Image Processing; Prentice Hall, 2nd Ed., 2002. • R. G. Gonzalez, R. E. Woods, S.L. Eddings, Digital Image Processing using MATLAB; Prentice Hall, 2003. • M. Nixon, A. Aguado, Feature Extraction & Image Processing, Newnes, 2002. • R. O. Duda, Peter E. Hart, D. G. Stork, Pattern Classification, Wiley-Interscience; 2nd edition, 2000. Introduction

  26. Next Topic Digital Image Fundamentals Introduction

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