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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 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

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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 RGB Linearization Transformation to device- independent values 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 150 Random colors 33 colors pr ramp 100 colors for test- set 51 colors for the training- set 10 to 20 colors 10 to 20 colors 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.

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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 nameMethod description Method 1Gamma method for linearization and regression into CIEXYZ space Method 2Polynomial fitting for linearization and regression into CIEXYZ space Method 3No linearization beyond a cubic root function and regression into CIELAB space Method 4Gamma method and a cubic root function for linearization and regression into CIELAB space Method 5Polynomial 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 1Method 2Method 3Method 4Method 5 3x310.357.7719.669.037.80 3x58.117.1816.298.216.18 3x106.203.976.583.513.75 3x204.522.242.821.792.53 3x353.201.401.341.101.37

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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. 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.

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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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Projector: Mean Delta

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Projector: interpolation+regression

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Projector: Interpolation+regression

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Camera-standard deviance.

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