#UNMIXING COLOR MACHINE FREE#

Rev. 106(4), 620–630 (1957)įinlayson, G.D., Schaefer, G.: Convex and Non-convex Illuminant Constraints for Dichromatic Colour Constancy. Jaynes, E.T.: Information Theory and Statistical Mechanics. Jaynes, E.: On the rationale of maximum-entropy methods. Shafer, S.A.: Using color to separate reflection components.

#UNMIXING COLOR MACHINE FREE#

of the 35th Applied Imagery and Pattern Recognition Workshop (2006)įu, Z., Tan, R., Caelli, T.: Specular free spectral imaging using orthogonal subspace projection. In: ICCV 2009: Proceedings of the Twelfth IEEE International Conference on Computer Vision (2009)īergman, M.: Some unmixing problems and algorithms in spectroscopy and hyperspectral imaging. Li, C., Li, F., Kao, C., Xu, C.: Image Segmentation with Simultaneous Illumination and Reflectance Estimation: An Energy Minimization Approach. In: Trémeau, A., Schettini, R., Tominaga, S. Tajima, J.: Illumination chromaticity estimation based on dichromatic reflection model and imperfect segmentation. Klinker, G.J., Shafer, S.A., Kanade, T.: A physical approach to color image understanding. Stainvas, I., Lowe, D.: A generative model for separating illumination and reflectance from images. of the International Geoscience and Remote Sensing Symposium (2001) Lennon, M., Mercier, G., Mouchot, M.C., Hubert-moy, L.: Spectral unmixing of hyperspectral images with the independent component analysis and wavelet packets. This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. We show the utility of our method on trichromatic and hyperspectral imagery and compare our results to those yielded by alternatives elsewhere in the literature. Hence, the method does not require a preset number of material clusters or spectral signatures as input. Furthermore, the final endmembers depend on the similarity between pixel reflectance spectra. As a result, the method presented here is related to soft clustering tasks with a strategy for avoiding local minima. The method is formulated as a simultaneous process of illuminant power spectrum prediction and basis material reflectance decomposition via a statistical approach based upon deterministic annealing and the maximum entropy principle. The method presented here is quite general in nature, being applicable to settings in which sub-pixel information is required. In this paper, we present a statistical approach to spectral unmixing with unknown endmember spectra and unknown illuminant power spectrum.

Unmixing color machine