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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
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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 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
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.
RGB Transformation to device-independent values Linearization General model of characterization Ex.Spline interpolation
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 beconsidered 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 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
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: 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.
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 • 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!
