Evaluation of processes used in screen imperfection algorithms

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Evaluation of processes used in screen
imperfection algorithms

• Siavash A. Renani

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Introduction

• Screen compensation algorithm
• Divided in four parts
• Projector characterization
• Camera characterization
• Geometrical alignment
• Screen compensation
• A Projection System with Radiometric
  compensation for Screen Imperfections, Nayar et
  al.
• Making One Object Look Like Another Controlling
  Appearance Using a Projector-Camera System,
  Grossberg et al.
• Robust Content-Dependent Photometric Projector
  Compensation, Ashdown et al.

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Motivation

• Screens increases the cost of projectors
• Screens takes up space
• Screens decreases projectors mobility
• And therefore decreases functionality.
• Can alter color of objects (Virtual offices).

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Index

• Thesis
• General
• Goal
• General model for characterization
• Projector
• Camera
• Geometrical alignment

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Thesis-general

• This thesis focus on the different steps of
  achieving screen independence.
• Evaluated 2 projector characterization methods
  and established their parameters.
• Evaluated 4 camera characterization methods and
  established their parameters.
• Transformation of coordinates of the screen from
  the captured image to the original image.
• Use of regression to compensate for the screens
  effect.

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Thesis- general
Colors are modified by the projector.
Color I is projected
Colors are modified by the screen
Camera captures projected colors. Colors are
again modified, this time by the camera
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Thesis - general

• Input and output devices are restricted by their
  sensors and/or ability to reproduce colors.
• To be able to calculate how screens modify
  colors, we need to know how input and output
  devices modify them first.

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Thesis-Goal

• Evaluate characterization methods for camera
• Evaluate characterization methods for projectors
• Implement Geometrical alignment algorithm
• Investigate the effect of screen compensation as
  the characterization error changes.

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General model of characterization
Ex.Spline interpolation
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Projector Resarch Questions

• How many colors are needed for linearization
  using linear, spline and cubic interpolation?
• How will PLCC compare against a characterization
  using regression?
• How many colors in the training set is needed to
  for the color difference to be considered hardly
  visible, when regression is used?

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Projector - Characterization methods

• 3 different interpolation techniques for
  linearization.
• Piecewise Linear assuming constant chromaticity
  model (PLCC).
• Regression

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Projector-experiment
Gamut of the projector
Color difference is calculated for different
amount of colors used in linearization and as
trainining-set. PLCC do no require
training-set. Different interpolaiton techniques
was used to linearize RGB.
51 colors for the training-set
33 colors pr ramp
150 Random colors
100 colors for test-set
10 to 20 colors
10 to 20 colors
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Projector conclusion

• PLCC performed better than regression. With only
  12 colors used in linearization acceptable result
  is achieved.
• Possible threat The assumptions of the PLCC
  model is correct for the test-set but not for the
  whole gamut.
• It is possible to achieve good result with
  regression using 12 or more colors for
  linearization and 12-18 colors in the
  training-set.

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Camera Research questions

• How many colors should be used for regression?
• What order of polynomial regression should we
  use?
• How will the use of only the cubic root function
  before transformation to LAB perform?
• How will use of CIELAB compare to CIEXYZ?
• Will always the method that performs best in
  CIEXYZ perform best also in CIELAB?
• How stabile are these methods?

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Camera characterization methods
Method name Method description
Method 1 Gamma method for linearization and regression into CIEXYZ space
Method 2 Polynomial fitting for linearization and regression into CIEXYZ space
Method 3 No linearization beyond a cubic root function and regression into CIELAB space
Method 4 Gamma method and a cubic root function for linearization and regression into CIELAB space
Method 5 Polynomial fitting and a cubic root function for linearization and regression not CIELAB space
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Camera Experiment

• Regression up to fourth order was used.
• Methods were tested 100 timer per training-set.
• 180 random colors were measured
• 33 grey values were used for linearization.

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Camera-Result
Size of regression Matrix Method 1 Method 2 Method 3 Method 4 Method 5
3x3 10.35 7.77 19.66 9.03 7.80
3x5 8.11 7.18 16.29 8.21 6.18
3x10 6.20 3.97 6.58 3.51 3.75
3x20 4.52 2.24 2.82 1.79 2.53
3x35 3.20 1.40 1.34 1.10 1.37
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Camere-conclusion

• Number of colors used for regression was
  dependent on methods and order of regression.
• Minimum order Second order regression.
• Use of cubic root function proved to yield good
  results but was very unstabile.
• CIELAB performed better than CIEXYZ and was more
  stabile.
• Its not certain that method that perfoms well in
  CIEXYZ performs as well in CIELAB. (Method 1 and
  4 versus Method 2 and 5).
• Stability was dependent on amount of colors in
  the training-set, order of regression and
  linearization method.

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Geometrical alignment.
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Geometrical alignment

• The points are detected
• Each point are binary coded.
• Divided in blocks
• Regression for finding transformation matrix.
• Compensation
• Divide image in blocks.
• Multiply with the transformation matrix.
• Dependent on size of the screen, the resolution
  of the camera and number of points and blocks.

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Acknowledgement

• I want to thank Mr. Hardeberg and HiG
  administration for giving me chance to visit
  Japan.
• I want also to thank Tsukdada-san, Toda-san,
  Funyama-san, Inoue-san and rest of the NEC
  employees who have welcomed me warmly.

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Resten av slides er bare i tilfelle jeg trenger
dem.

• Takk for hjelpen!

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ProjectorMean Delta
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ProjectorMean Delta
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Projector interpolationregression
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ProjectorInterpolationregression
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Camera-standard deviance.