evaluation of processes used in screen imperfection algorithms
DESCRIPTION
Evaluation of processes used in screen imperfection algorithms. Siavash A. Renani. Introduction. Screen compensation algorithm Divided in four parts Projector characterization Camera characterization Geometrical alignment Screen compensation - PowerPoint PPT PresentationTRANSCRIPT
Evaluation of processes used in screen imperfection algorithms
Siavash A. Renani
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.
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).
Index
Thesis– General– Goal
General model for characterization Projector Camera Geometrical alignment
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.
Thesis- general
Color I is projected
Camera captures projected colors.
Colors are again modified, this time by the camera
Colors are modified by the projector.
Colors are modified by the
screen
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.
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.
General model of characterization
RGB Linearization
Transformation to device-
independent values
Ex.Spline interpolation
B
G
R
AZYX
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?
Projector - Characterization methods
3 different interpolation techniques for linearization.
Piecewise Linear assuming constant chromaticity model (PLCC).
Regression
Projector-experiment
Gamut of the projector
150 Random colors33
colors pr
ramp
100 colors
for test-set
51 colors for the training-set
10 to 20colors
10 to 20colors
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.
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.
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?
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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1
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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.
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
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. It’s 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.
Geometrical alignment.
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.
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.
Resten av slides er bare i tilfelle jeg trenger dem.
Takk for hjelpen!
Projector:Mean Delta
Mean delta
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
10 12 14 16 18 20
Nr. of colors in the color channels
Mean
delt
a E
Spline
Linear
Cubic
Max delta
0
2
4
6
8
10
12
10 12 14 16 18 20
Nr. of colors in the color channels
Ma
x d
elt
a E
Spline
Linear
Cubic
Projector:Mean Delta
Projector: interpolation+regression
Comparison of mean Delta
0
0.5
1
1.5
2
2.5
10 12 14 16 18 20
Nr. Of Colors in the training-set
Me
an
De
lta
E
Spline
Linear
Cubic
Projector:Interpolation+regression
Comparison of Max Delta
0
2
4
6
8
10
12
14
10 12 14 16 18 20
Nr. Of Colors in the training-set
Max D
elt
a E
Spline
Linear
Cubic
Camera-standard deviance.