RIT/Vista Point: Developing Summer Courses in
Image Science for the Mobile Imaging Community
RIT/Center for Imaging Science and Vista Point Technologies
announce a two-part summer program:
Imaging Science for the Mobile Imaging Community
I. Foundations of Imaging Science (July 7 – August 8, 2008)
Week 1: Introduction, Human Visual System, Photometry/Radiometry, Basic Statistics
Week 2: Geometrical Optics, Sensor Characteristics, Parametric Statistical Methods
Week 3: Image Processing, Introduction to Color Measurement
Week 4: Displays and Output, Image Evaluation, Psychophysics
Week 5: Experimental Design, Collaborative Investigations, Wrap-up
II. Advanced Topics in Imaging Science (planned for summer, 2009)
Week 6: System Characterization for Digital Camera Systems
Week 7: Flash and Shutters, Advanced Optics, Color Science I
Week 8: Color Science II, Vision, Perception and JND’s; Statistical Process Control
Week 9: Image Quality Prediction, Advanced Image Processing
Week 10: Video and other Imaging Applications, Collaborative Project; Course Review
Textbooks:
The Science of Imaging, Sakby
[Introductory Statistics Textbook TBD]
Handouts, course-supplied materials
References:
Handbook of Image Quality, Keelan
Color Science, Wyszecki & Stiles
Billmeyer and Saltzman’s Principles of Color Technology, Burns
Seeing the Light, Falk, Brill, Stork
Contrast Sensitivity of the Human Eye and its Effects on Image Quality,
Barten
Prerequisites:
One year of college-level physics, algebra and trigonometry
Basic software programming skills (knowledge of Matlab or IDL helpful)
Knowledge of photographic principles or interest to learn
Laboratory Exercises (Foundations of Imaging Science):
Characterizing HVS / contrast sensitivity / Eye Resolution
Measuring Light / Statistical Variability / Field “uniformity”
Optical Image Formation / Magnification, Resolution, MTF, Distortion
Detectors and Response / sensitivity and noise
Statistics of samples and populations (determining differences in mean, sigma)
Grayscale adjustments via image processing; histograms and eye response
Color measurement, reproduction and variability (create custom color test chart)
Display calibration, hardcopy WYSIWYG considerations
Objective performance characterization (SFR, dynamic range, SNR, sensitivity)
Softcopy ruler experiment, JND’s
Week-long team project (TBD)
Laboratory Exercises (Advanced Topics):
Spatial, Tonal, Spectral, Noise Characterizations (CIPA, Imatest, DxO, IE)
Lens CRA measurements
Flare (VGI, stray light) and Blemish measurements
Spectral color measurements, CCT, blackbodies
Subjective image quality assessments (color uniformity, sharpness, exposure)
Statistical analysis of previously collected image quality data
Analysis of lens performance – incoming inspection (MTF/thru focus/yield)
Image processing affects on Image Quality (texture smoothing vs. sharpness)
Developing a simple autofocus optimization scheme via histogram analysis
Determining minimum image requirements for OCR
Multispectral imaging with filters and specialized detectors
Final team project (last week of course) - TBD
Detailed Course Syllabus – Foundations of Imaging Science
(Weeks 1 – 2)
Introduction: The imaging chain; sources, objects, reflectance, optics, sensors, image
processing, color balancing, compression, display, visual evaluation, environment
Basic Statistics: mean, variance, histograms, normal curves and the area under them,
hypothesis tests, test statistics, Student’s T distribution, alpha/beta risk
Human Visual System: Perception, log vs. linear response; rods/cones, photopic vs.
scotopic, contrast sensitivity function, angular resolution of eye
Lab: Characterizing HVS / Contrast Sensitivity / Eye Resolution
Photometry/Radiometry: Energy calculations and units, solid angles, flux, intensity,
illuminance, inverse-square law, exposure equations, light sources (daylight, fluorescent,
incandescent, xenon flash), filters – interference vs. absorptive, IR rejection, color balancing
and ND filters
Lab: Measuring Light / Statistical Variability / Field “uniformity”
Geometrical Optics: principles of image formation – object and image distance,
magnification, focal length, f/number, hyperfocal distance, depth of field, field of view,
distortion
Lab: Optical Image Formation / Magnification, Resolution, MTF, Distortion
Detailed Course Syllabus – Foundations of Imaging Science
(Weeks 2 - 4)
Sensors: Imaging principles and characteristics of film, CCD’s and CMOS sensors;
sensitivity (spectral and integrated), ISO speed calculations, SNR measurements,
dynamic range & OECF, CMOS micro-lenses and Chief Ray Angle (CRA) matching,
Bayer and other CFA configurations, tonal transfer
Lab: Detectors and Response / sensitivity and noise
Parametric Statistics: Computation of confidence intervals, estimates of population
statistics from samples, hypothesis tests for variance, Chi-Square distribution,
regression, correlation coefficients
Lab: Statistics of samples and populations (determining differences in mean, sigma)
Image Processing: Spatial, tonal, spectral, noise adjustments (sharpening, exposure
compensation, color balancing). “In-camera” adjustments: Auto-exposure, auto-whitebalance, anti-vignetting.
Lab: Grayscale adjustments via image processing; histograms and eye response
Introduction to Color: Radiation systems and color mixing, defining, describing, and
measuring color; assessing color reproduction “accuracy” and preference; introduction to
color systems and spaces (CIELAB, RGB, Yuv, sRGB, etc.)
Lab: Color measurement, reproduction and variability (create a custom color test chart)
Detailed Course Syllabus – Foundations of Imaging Science
(Weeks 4 – 5)
Displays and Output: Softcopy display types and characteristics, hardcopy
reproduction; resolution and color characteristics; calibration
Lab: Display calibration, hardcopy WYSIWYG considerations, absolute color
reproduction
Psychophysics: Subjective evaluation approaches (paired comparisons, rank order,
quality ruler, etc.); Image Quality Circle; perceptual “nesses”, psychometric scaling
approaches, linking objective and subjective characteristics; introduction to JND’s
Lab: Softcopy ruler experiment, JND’s
Experimental Design: Choosing factors and response variable(s), experimental
planning (randomization, samples sizes, etc.), factorial experiments, ANOVA, Guage
R&R; application of parametric and nonparametric approaches
Lab: Week-long Capstone team project (TBD)
Detailed Course Syllabus – Advanced Topics in Image Science
(Weeks 6 – 7; summer 2009)
Week 6: System Characterization for Camera Systems:
Tone Reproduction, Spatial Performance, Noise; sampling theory, Nyquist limit, pixel sizes
vs. performance, spatial frequency response (SFR), tonal transfer, low light and exposure
time, image blur; “High Dynamic Range” imaging, types of noise and assessment (noise
power, etc.)
Labs: Spatial, Tonal, Spectral, Noise Characterizations (tools: CIPA, Imatest, DxO, IE)
Week 7: Flash and Shutters – characterization of exposure; Advanced Optics; PSF,
MTF, understanding lens design outputs, measuring aberrations and blemishes, optical
image stabilization, autofocus and zoom lenses; Color Science I: spectral characterization
Lab: Lens CRA measurements or Calculating Flash Guide Numbers
Lab: Flare (VGI, stray light) and Blemish measurements
Lab: Spectral color measurements, CCT, blackbody
Detailed Course Syllabus – Advanced Topics in Image Science
(Weeks 8 – 9; summer 2009)
Week 8: Color science II – specifying color reproduction, color space transformations, CIE
diagram, white points, color perception and adaptation; Vision and JND’s, perceptual
evaluations; Statistical Process Control, control charts, sampling plans, six-sigma
methodology and terms
Lab: Spectral color measurements, CCT, blackbody
Lab: Subjective image quality assessments (color uniformity, sharpness, exposure)
Lab: Statistical analysis of Week 6 image quality data
Lab: 6-Sigma analysis of lens performance – incoming inspection
Week 9: Image Quality Prediction: SQF, SQRI, Specifications and ISO standards;
Advanced Image Processing (compression methods, artifact removal, simulations,
texture smoothing, “Extended depth of Field (EDOF)” and computational imaging
Lab: Image processing affects on Image Quality (texture smoothing vs. sharpness)
Lab: Developing a simple autofocus optimization scheme via histogram analysis
Lab: Determining minimum image requirements for OCR
Detailed Course Syllabus – Advanced Topics in Image Science
(Week 10; summer 2009)
Week 10: Video; Quality assessment, standard vs. HD formats, test methods;
Survey of Other Imaging Applications; Multispectral systems, radiology, thermal
imaging, etc.,
Collaborative Project; Course Review
Lab: Multispectral imaging with filters and specialized detectors
Lab: Collaborative team project (TBD)