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Evaluation of processes used in screen imperfection algorithms

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

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  1. Evaluation of processes used in screen imperfection algorithms Siavash A. Renani

  2. 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.

  3. 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).

  4. Index • Thesis • General • Goal • General model for characterization • Projector • Camera • Geometrical alignment

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. RGB Transformation to device-independent values Linearization General model of characterization Ex.Spline interpolation

  10. 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?

  11. Projector - Characterization methods • 3 different interpolation techniques for linearization. • Piecewise Linear assuming constant chromaticity model (PLCC). • Regression

  12. 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

  13. 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.

  14. 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?

  15. Camera: characterization methods

  16. 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.

  17. Camera-Result

  18. 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.

  19. Geometrical alignment.

  20. 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.

  21. 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.

  22. Resten av slides er bare i tilfelle jeg trenger dem. • Takk for hjelpen!

  23. Projector:Mean Delta

  24. Projector:Mean Delta

  25. Projector: interpolation+regression

  26. Projector:Interpolation+regression

  27. Camera-standard deviance.

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