Neural networks for pattern recognition
Neural networks for pattern recognition
Digital Image Processing
IEEE Computer Graphics and Applications
Efficient Image Segmentation by Mean Shift Clustering and MDL-Guided Region Merging
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Recovering Intrinsic Images from a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color scanner calibration via a neural network
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
A comparison of computational color constancy Algorithms. II. Experiments with image data
IEEE Transactions on Image Processing
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This paper presents a new approach for automatic image color correction, based on statistical learning. The method both parameterizes color independently of illumination and corrects color for changes of illumination. The motivation for using a learning approach is to deal with changes of lighting typical of indoor environments such as home and office. The method is based on learning color invariants using a modified multi-layer perceptron (MLP). The MLP is odd-layered. The middle layer includes two neurons which estimate two color invariants and one input neuron which takes in the luminance desired in output of the MLP. The advantage of the modified MLP over a classical MLP is better performance and the estimation of invariants to illumination. The trained modified MLP can be applied using look-up tables (LUTs), yielding very fast processing. Results illustrate the approach.