The Synthesis and Analysis of Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
An evolutionary approach to camera-based projector calibration
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Genetic Algorithm SAmple Consensus (GASAC) - A Parallel Strategy for Robust Parameter Estimation
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Estimating the spectral sensitivity of a digital sensor using calibration targets
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Color Constancy
Combined evolution strategies for dynamic calibration ofvideo-based measurement systems
IEEE Transactions on Evolutionary Computation
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In color image processing, several sensors are used which respond to the light in the red, green and blue parts of the spectrum. When working with color images taken by an optical system it is very important to know the sensitivity of the entire optical system. The optical system consists of the sensor, lens and any filters which may be used. The response characteristics of the lens and filters can be measured inside the laboratory. However, for many digital cameras it is not clear how the sensors contained inside the camera respond to light. This information may not be available from the manufacturer of the camera. Even if we knew the response characteristics of the sensor, it may not be clear what algorithms are employed by the manufacturer before the data is finally stored as an image file. We show how genetic programming may be used to obtain the sensor response functions using a single image from a calibration target as input together with the reflectance data of this calibration target.