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 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
About this course.. Lecturer Shinsaku Hiura, Assoc. Prof. (Division of Systems Science and Applied Informatics) ext room D552 Grading Mainly final examination Regular attendance
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
Who are you? Students with various background Self introduction Your origin / where come from Educational background Expertise (if any) Your interest about camera / image
Why image? Most (simple) sensors Temperature, pressure, voltage,.. Image sensors Position, rotation, size, shape, … “Sensory Information Processing” sensor subjectvalue sensor subjectvalue processing
Pattern and Symbol Array of homogeneous elements Essential information is in the arrangement of values SymbolPattern Black Red Green Yellow Not homogeneous, independent Each value has meanings Information processing
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
What the class is not about Wide coverage of sensors But mostly about image sensors Theories about signal processing Techniques and programming
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)
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)
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)
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)
3-D measurement / camera geometry Spot / Slit / Pattern light projection Camera parameter Pin-hole camera model Calibration Recognition PCA / eigenspace Keywords to learn(4)