It is often useful to view an image as a random process. If we assume a collection of images are all sampled from the same distribution, we can estimate the covariance between pixels in each image. An eigenvalue/eigenvector decomposition of the covariance matrix reveals the principal directions of variation between images in the collection. This has applications in image coding, image classification, object recognition, and more. This lab will explore the concepts of image covariance, covariance estimation, and eigen decomposition of images. These ideas will then be used to design a basic image classifier.
To run this laboratory, you will need Matlab with both the signal and image processing tool boxes.
Laboratory Procedure - Instructions for running the laboratory in pdf format.
training_data.zip - A set of tiff images of letters from the English alphabet. This is a training set that will be used for some analysis and training. The zip file also contains a Matlab script for reading all the images into an array.
test_data.zip - A set of tiff images of letters from the English alphabet. This set will be used to test a classifier.
Questions or comments should be sent to:
Prof. Charles A. Bouman, School of Electrical and Computer Engineering, Purdue University, West Lafayette IN 47907; bouman@ecn.purdue.edu