Estimating the spectral sensitivity of a digital sensor using calibration targets

A digital sensor which is used inside a digital camera usually responds to a range of wavelengths. The response of the sensor is proportional to the product of the irradiance falling onto the sensor and the sensitivity of the sensor integrated over all wavelengths. Knowledge of the sensor's response function is important for colorimetry and the research area of color constancy. Such data may not always be available from the manufacturer of the camera. The sensitivity of the imaging device is a result of the hardware properties of theimaging chip, the lens and filters used, and the post-processing done by the processor contained inside the camera. We will be using an evolution strategy to obtain the sensor response curves of a camera given a single image of a calibration target.

[1]  Olaf Hellwich,et al.  Genetic Algorithm SAmple Consensus (GASAC) - A Parallel Strategy for Robust Parameter Estimation , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[2]  K. Deb An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[3]  Pietro Cerveri,et al.  Combined evolution strategies for dynamic calibration of video-based measurement systems , 2001, IEEE Trans. Evol. Comput..

[4]  Matthew Anderson,et al.  Proposal for a Standard Default Color Space for the Internet - sRGB , 1996, CIC.

[5]  Hans-Paul Schwefel,et al.  Evolution and Optimum Seeking: The Sixth Generation , 1993 .

[6]  Ingo Rechenberg,et al.  Evolutionsstrategie '94 , 1994, Werkstatt Bionik und Evolutionstechnik.

[7]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[8]  Gunther Wyszecki,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition , 2000 .

[9]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[10]  Zou Xiu A Robust Evolutionary Algorithm for Constrained Multi-Objective Optimization Problems , 2004 .

[11]  Hussein A. Abbass,et al.  An Evolutionary Algorithm for Constrained Multiobjective Optimization Problems , 2001 .

[12]  G D Finlayson,et al.  Color constancy at a pixel. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[13]  David A. Forsyth,et al.  A Novel Approach To Colour Constancy , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[14]  Sana Ben Hamida,et al.  An Adaptive Algorithm for Constrained Optimization Problems , 2000, PPSN.

[15]  Peter J. Fleming,et al.  Multiobjective genetic algorithms made easy: selection sharing and mating restriction , 1995 .

[16]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[17]  J. Cohen,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulas , 1968 .

[18]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[19]  Alice E. Smith,et al.  Penalty functions , 1996 .

[20]  William C. Thibault,et al.  An evolutionary approach to camera-based projector calibration , 2006, GECCO '06.

[21]  Zbigniew Michalewicz,et al.  A Survey of Constraint Handling Techniques in Evolutionary Computation Methods , 1995 .

[22]  Qiang Ji,et al.  Camera calibration with genetic algorithms , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[23]  Mark S. Drew,et al.  Removing Shadows from Images , 2002, ECCV.

[24]  Brian A. Wandell,et al.  The Synthesis and Analysis of Color Images , 1992, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Douglas A. G. Vieira,et al.  Handling constraints as objectives in a multiobjective genetic based algorithm , 2002 .

[26]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms for Constrained Parameter Optimization Problems , 1996, Evolutionary Computation.

[27]  Bernardete Ribeiro,et al.  On the estimation of spectral data: a genetic algorithm approach , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[28]  Frank Hoffmeister,et al.  Problem-Independent Handling of Constraints by Use of Metric Penalty Functions , 1996, Evolutionary Programming.

[29]  M. S. Drew,et al.  Color constancy - Generalized diagonal transforms suffice , 1994 .

[30]  Graham D. Finlayson,et al.  Color in Perspective , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  G. Buchsbaum A spatial processor model for object colour perception , 1980 .

[32]  William R. Mathew,et al.  Color as a Science , 2005 .

[33]  Ángel Fernando Kuri Morales,et al.  Penalty Function Methods for Constrained Optimization with Genetic Algorithms: A Statistical Analysis , 2002, MICAI.