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Sensory Information Processing

Sensory Information Processing. Shinsaku HIURA Division of Systems Science and Applied Informatics. About this course. For whom: Non-Japanese-speaking people Students who can not take this lecture in next year Credits : 2 Class web page

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Sensory Information Processing

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  1. Sensory Information Processing Shinsaku HIURA Division of Systems Science and Applied Informatics

  2. About this course.. • For whom: • Non-Japanese-speaking people • Students who can not take this lecture in next year • Credits : 2 • Class web page http://www-sens.sys.es.osaka-u.ac.jp/users/shinsaku/lec/ Just google “Shinsaku Hiura” and you can find me online • Day / Period Monday, 2nd period • If we have no class day, I will announce on web site also

  3. About this course.. • LecturerShinsaku Hiura, Assoc. Prof.(Division of Systems Science and Applied Informatics)shinsaku@sys.es.osaka-u.ac.jpext. 6371room D552 • Grading • Mainly final examination • Regular attendance

  4. My profile • Researcher (of course) • Image processing / recognition • 3-D measurement of the scene • Computational photograph see my web page • Photographer • B/W fine print using chemical process • Exhibitions, CD cover photo, etc. • Classic camera collector

  5. Who are you? Students with various background • Self introduction • Your origin / where come from • Educational background • Expertise (if any) • Your interest about camera / image

  6. Why image? • Most (simple) sensors • Temperature, pressure, voltage, .. • Image sensors • Position, rotation, size, shape, …  “Sensory Information Processing” sensor subject value sensor processing subject value

  7. Pattern and Symbol Pattern Symbol 0 1 2 3 Black Red Green Yellow Information processing • Array of homogeneouselements • Essential information isin the arrangement of values • Not homogeneous, independent • Each value has meanings

  8. Image processing inthe narrow sense Image processing and understanding • Image processing(Pattern  Pattern) • Improvement of image quality (denoise, etc.) • Image encoding, compression • Media conversion (visualization of info.) •  Image recognition and understanding(Pattern  Symbol) • OCR (character recognition) • 3-D Scene description from images • Image generation, rendering (Symbol  Pattern) • Computer Graphics

  9. What the class is not about • Wide coverage of sensors • But mostly about image sensors • Theories about signal processing • Techniques and programming

  10. What the class is about • Image sensors • Imaging device • Optics (imaging lens) • Basics of image processing • Measurement using the image • 3-D shape measurement (geometry) • Color, luminance (photometry)

  11. Keywords to learn(1) Optics • Gaussian optics (paraxial optics) • Focal length, F-no, dispersion • Lens aberration (coma, chromatic aberration, etc..) • Lens tilt, Scheimflüg law • Depth of field, depth of focus,hyperfocal distance,Permissible circle of confusion

  12. Keywords to learn(2) Optics • Resolution, MTF, OTF • Diffraction limit • Vignetting, cos4 law Sensor / device • CCD / CMOS • Bayer filter / demosaicing • Blooming, smear, thermal noise • Optical low-pass filter

  13. Keywords to learn(3) Image signal • NTSC / PAL / SECAM • YC separation Color representation • RGB / XYZ color space • L*a*b* color space • Metamerism, xy chromaticity • gamut

  14. Keywords to learn(4) 3-D measurement / camera geometry • Spot / Slit / Pattern light projection • Camera parameter • Pin-hole camera model • Calibration • Recognition • PCA / eigenspace

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