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Computer Vision Lecture #1

Computer Vision Lecture #1. Hossam Abdelmunim 1 & Aly A. Farag 2 1 Computer & Systems Engineering Department, Ain Shams University, Cairo, Egypt 2 Electerical and Computer Engineering Department, University of Louisville, Louisville, KY, USA ECE619/645 – Spring 2011. Course Outline.

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Computer Vision Lecture #1

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  1. Computer VisionLecture #1 Hossam Abdelmunim1 & Aly A. Farag2 1Computer & Systems Engineering Department, Ain Shams University, Cairo, Egypt 2Electerical and Computer Engineering Department, University of Louisville, Louisville, KY, USA ECE619/645 – Spring 2011

  2. Course Outline • Computer Vision – Focus is on basic computer vision skills – Tools for research • Mathematical Background – Basic material – Numerical/computational Skills • Matlab – Quick prototyping – tool for checking out ideas

  3. Text Books 1. A Guided Tour of Computer Vision, by V. S. Nalwa, Addison-Wesley, 1993. 2. Introductory Techniques for 3-D Computer Vision, by EmanueleTrucco, Alessandro Verri, Prentice-Hall, 1998. 3. Multiple View Geometry, by Richard Hartley, Andrew Zisserman, Cambridge University Press, 2000, 2003. 4. Numerical Recipes in C, by William Press et al., Cambridge Univ. Press, 1992. 5. Pattern Classification and Scene Analysis, by Richard O. Duda, Peter E. Hart, John Wiley & Sons, 1973.

  4. Computer Vision • Computer vision basics • – Image creation • – Cameras, Eyes, Calibration • – Features, correspondence • – 3D vision • – Optical Flow • – Tracking • – Compression, vision for content delivery

  5. Mathematics • Basic Mathematical tools • – Linear systems, matrix decompositions • – Vector space, Basis, eigenvalues, eigenvectors • – Calculus: gradients, Taylor series, maxima • • Projective geometry • – Homographies, projectivities, constraints • – Fundamental matrix • – Trifocal tensor • • Probability, Random Variables, Classification

  6. Numerical Methods • • Solving linear systems of equations • • Matrix decompositions • – LU, Eigenvalue, SVD, • • Optimization • • Use Matlab as a tool to quickly try ideas • • Familiarity with image types • • “Practical computer vision”

  7. Problems we should be able to solve • Calibrate a camera • Rectify an image • Create a three dimensional reconstruction • Insert model into image • Track objects • Track people • Read papers on these topics and do thesis research

  8. Application Example-1 Detect ground plane in video and introduce pictures on them

  9. Application Example-2 morphing: blending between two photographs

  10. Application Example-3 morphing: blending between two photographs

  11. Application Example-4 turning a collection of photographs into a 3D model

  12. Application Example-5 Tracking

  13. What is Vision? • Recognize objects • – people we know • – things we own • • Locate objects in space • – to pick them up • • Track objects in motion • – catching a baseball • – avoiding collisions with cars on the road • • Recognize actions • – walking, running, pushing

  14. Vision is • Deceivingly easy • Deceptive • Computationally demanding • Critical to many applications

  15. Vision is Deceivingly Easy • We see effortlessly • – seeing seems simpler than “thinking” • – we can all “see” but only select gifted people can solve • “hard” problems like chess • – we use nearly 70% of our brains for visual perception! • • All “creatures” see • – frogs “see” • – birds “see” • – snakes “see” • but they do not see alike

  16. Vision is Deceptive • Vision is an exceptionally strong sensation • – vision is immediate • – we perceive the visual world as external to ourselves, • but it is a reconstruction within our brains • – we regard how we see as reflecting the world “as it • is;” but human vision is • • subject to illusions • • quantitatively imprecise • • limited to a narrow range of frequencies of radiation • • passive

  17. Some Illusion

  18. Some Illusion

  19. Some Illusion

  20. Some Illusion

  21. Human Vision is Passive • It relies on external energy sources • (sunlight, light bulbs, fires) providing light • that reflects off of objects to our eyes • Vision systems can be “active” - carry their • own energy sources • – Radars • – Bat acoustic imaging systems

  22. Spectral Limitation of Human Vision • • We “see” only a small part of the energy • spectrum of sunlight • – we don’t see ultraviolet or lower frequencies of light • – we don’t see infrared or higher frequencies of light • – we see less than .1% of the energy that reaches our eyes • • But objects in the world reflect and emit energy in these and other parts of the spectrum

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