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, 2 nd period If we have no class day, I will announce on web site also

3. About this course.. Lecturer Shinsaku Hiura, Assoc. Prof. (Division of Systems Science and Applied Informatics) shinsaku@sys.es.osaka-u.ac.jp ext. 6371 room D552 shinsaku@sys.es.osaka-u.ac.jp 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 subjectvalue sensor subjectvalue processing

7. Pattern and Symbol Array of homogeneous elements Essential information is in the arrangement of values 01230123 SymbolPattern Black Red Green Yellow Not homogeneous, independent Each value has meanings Information processing

8. 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 Image processing in the narrow sense

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. 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 Keywords to learn(1)

12. Optics Resolution, MTF, OTF Diffraction limit Vignetting, cos 4 law Sensor / device CCD / CMOS Bayer filter / demosaicing Blooming, smear, thermal noise Optical low-pass filter Keywords to learn(2)

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

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