Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
A novel algorithm for color constancy
International Journal of Computer Vision
Illumination invariant colour recognition
BMVC 94 Proceedings of the conference on British machine vision (vol. 2)
Color constancy for scenes with varying illumination
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Color by Correlation: A Simple, Unifying Framework for Color Constancy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Color models for outdoor machine vision
Computer Vision and Image Understanding
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
What is the Spectral Dimensionality of Illumination Functions in Outdoor Scenes?
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Finding Images with Similar Lighting Conditions in Large Photo Collections
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
What Do the Sun and the Sky Tell Us About the Camera?
International Journal of Computer Vision
Color constancy via convex kernel optimization
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Neural Network Based Terrain Classification Using Wavelet Features
Journal of Intelligent and Robotic Systems
Estimating the Natural Illumination Conditions from a Single Outdoor Image
International Journal of Computer Vision
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We present an algorithm for color classification with explicit illuminant estimation and compensation. A Gaussian classifier is trained with color samples from just one training image. Then, using a simple diagonal illumination model, the illuminants in a new scene that contains some of the surface classes seen in the training image are estimated in a maximum likelihood framework using the Expectation Maximization algorithm. We also show how to impose priors on the illuminants, effectively computing a maximum a posteriori estimation. Experimental results are provided to demonstrate the performance of our classification algorithm in the case of outdoor images.