Implementation of colour appearance models for comparing colorimetrically images using a calibrated digital camera

Transcription

1 Implementation of colour appearance models for comparing colorimetrically images using a calibrated digital camera Elisabeth Chorro Calderón MSc Dissertation Colour and Vision Group, University of Alicante

2 Introduction Where is the origin of this project? Natural stone industry: Manual visual classification Near classification distance Problems: Over-classification of some sub-types of marbles, limestones, etc Lots without commercial output

3 To establish judgements for classifying colorimetrically and in an automatic way natural stones To avoid the over-classification distinguishing according to the viewing distance to the natural stone slab

4 Colorimetry Non-related colours vs. related colours Colorimetry Vision models Camera colour Colorimetry Colour Appearance Models Imaging Colour Appearance Models

5 Colour Vision Models Colorimetry Vision models Camera colour Colour Appearance Models CIECAM02 (Luo & Hunt, 1998) Physiological Colour Appearance Models ATTD 05 (Capilla & Luque, 2005) Imaging Colour Appearance Models S-CIELAB (Zhang & Wandell, 1996) Spatial extension of S-CIELAB model i-cam (Fairchild & Johnson, 2002) Spatial extension of CIECAM02 model

6 Camera colour Advantage of a digital camera relative to a telespectroradiometer Colorimetry Vision models Camera colour Drawbacks of a digital camera Integral colorimetric Colour using mathematical optimization

7 Integral colour Colorimetry Vision models Camera colour Spatial correction (Pujol, et al. Applied Optics, in press) Non-uniformity in response of imaging sensor in front the same incident luminous Spectral characterization (Martinez-Verdú, et al, JIST 2002) Measurement of the spectral sensitivities using a monochromator method Colorimetric characterization (Martinez-Verdú, et al, JIST 2003) Colorimetric profile with luminance adaptation

8 Colour by optimization Training colour set Colorimetry Vision models Camera colour Measure XYZ Obtain RGB X = a 1 +a 2 R+a 3 G+a 4 B+a 5 RG+ +a 20 B 3 Y = b 1 +b 2 R+b 3 G+b 4 B+b 5 RG+ +b 20 B 3 Z = c 1 +c 2 R+c 3 G+c 4 B+c 5 RG+ +ca 20 B 3 test + + a 1 a 2 a 3 b 1 b 2 b 3 c 1 c 2 c 3 = X Y Z

9 Comparing s Comparing images Spatial colour dithering Comparing results between both methods Integral XYZ int XYZ reales CIELAB L * a * b * int, int L * a * b * real, mat Polynomical XYZ mat L * a * b * mat

10 Comparing textured images Comparing s Comparing images Spatial colour dithering reference Sample Calibration Calibration XYZ ref XYZ samp

11 Comparing textured images CIELAB Comparing s Comparing images Spatial colour dithering XYZ ref XYZ samp S-CIELAB L * a * b * ref L * a * b * samp S-CIELAB distance

12 Simulation of spatial colour dithering Comparing s Comparing images Spatial colour dithering X' (i', j' ) = 1 k k 2 i, j= 1 X 0 ( i, j ) Y' (i' Z' (i',, j' j' ) ) = = 1 k 1 k k 2 i, j= 1 k 2 i, j= 1 Y Z 0 0 ( i, j ) ( i, j )

13 rosa Calibrations CIELAB S-CIELAB S-CIELAB & distance simulation Comparing colour s verde turquesa lila azul rojo rojo plateado naranja amarillo verde pastel blanco amarillo pastel naranja negro azul marino verde Integral Polynomical average

14 CIELAB Calibrations CIELAB S-CIELAB S-CIELAB & distance simulation Integral Polynomical Relative frequency Relative frequency

15 S-CIELAB Calibrations CIELAB S-CIELAB S-CIELAB & distance simulation Integral Polynomical Relative frequency Relative frequency

16 S-CIELAB & distance simulation Calibrations CIELAB S-CIELAB S-CIELAB & distance simulation Integral Polynomical Relative frequency Relative frequency ( )

17 Calibrations CIELAB S-CIELAB S-CIELAB & distance simulation Relative frequency Polynomical Classification judgements k = k = k = For same type of natural stone: The greater viewing distance, the lower width The greater viewing distance, the higher height of the S-CIELAB colour difference histograms

18 Camera colour : Optimization model is better than integral model Judgement of colorimetric classification of textures based on S-CIELAB colour differences It is possible to improve the judgement of colorimetric classification of textures taking into account the spatial colour dithering to some viewing distances

19 Future works To use i-cam model, based on CIECAM02 To implement into the ATTD 05 model the spatial modelling at several stages, in order to use it for comparing images To evaluate the performance of the new version of the ATTD 05 model relative to i-cam model To establish classification judgements of natural stones according to the best spatialcolour appearance model

20 References Acharya, T. & Ray, A.K. Image processing. Principles and applications. John Wiley & Sons, Inc. (2005). Lasarte, M., Pujol, J., Arjona, M. Vilaseca, M., Optimized Algorithm for the Spatial Non-Uniformity Correction of an Imaging System Based on a CCD Color Camera, Applied Optics (2006, in press) Capilla, P., Artigas, y J.M, Pujol, J.P. Fundamentos de colorimetría. Publicaciones de la Universidad de Valencia (2002) Capilla, P., Gómez-Chova, J., Artigas, J.M. y Luque, M.J. Architecture and performance of a new multistage colour vision model. Vision Research (2006, in press). Martínez-Verdú, F., Balboa, R., Chorro, E., de Fez, D., Viqueira, Colour measurement of natural stones using a calibrated digital camera. Proceedings of AIC 05, p (Granada, 2005).

21 References CIE web site: Fairchild, M.D. Color appearance models. John Wiley & Sons, Inc. (2005). Johnson, G.M., Fairchild, M. D. A top down description of S- CIELAB and CIEDE2000. Color Res. Appl. 28(6) (2003). S-CIELAB web site: Völz, Hans, G. Industrial color testing. Fundamentals and techniques. John Wiley & Sons, Inc. Second, completely revised edition. pp 15-69, (2001) Zhang, X.M., Wandell, B.A. A spatial extension of CIELAB for digital color image reproduction. Soc for Info Disp Symp Tech Digest. 27, (1996).