Modeling Light Reflection for Computer Color Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametric models for facial features segmentation
Signal Processing
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
| Hi-index | 0.00 |
We show that a color algorithm capable of separating illumination from reflectance in a Mondrian world can be learned from a set of examples. The learned algorithm is equivalent to filtering the image data---in which reflectance and illumination are mixed---through a center-surround receptive field in individual chromatic channels. The operation resembles the ``retinex'''' algorithm recently proposed by Edwin Land. This result is a specific instance of our earlier results that a standard regularization algorithm can be learned from examples. It illustrates that the natural constraints needed to solve a problem in inverse optics can be extracted directly from a sufficient set of input data and the corresponding solutions. The learning procedure has been implemented as a parallel algorithm on the Connection Machine System.