(1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-image by out-phase into a 81-dimensional vector lines (3) All 200 line vector KL transform, derive its corresponding covariance matrix eigenvectors and eigenvalues, in descending order eigenvalues and corresponding eigenvectors (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the main element, the original image block to the 40 on the projection eigenvector obtained projection coefficient is the sub-block eigenvector. (5) calculated for all sub-block eigenvector.

KLtransform.doc

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