Project Title: Decorrelating transforms with color images
 Student: Charles Mathis, cmathis@stat.stanford.edu
 
 Description: This project will look at different ways of using the
 eigendecomposition (decorrelation) ideas of the Karhunen Loeve transform
 for color images. These images are vector-valued for each pixel, yielding
 different components to decorrelate. This project will try three
 approaches. First, decorrelating the three colors (transforming to three
 decorrelated variables spanning the color space) and then spatially
 decorrelating each variable. Second, spatially decorrelating each color
 component, and then decorrelating the three color variables. Third,
 decorrelating the joint color-space distribution (i.e. regarding the image
 as 3*n^2 pixels and decorrelating them). This project will use a training
 set of images to develop a transform for each of these three approaches,
 and compare performance (i.e. energy concentration ability) within and
 outside of the training set.