The first method to improve the image matching process is the "FFT Method" already discussed, with one major addition: a Rotation Matrix. Normal Fourier analysis only works in two dimensions. However, a scanned inmage will, more often than not, be oriented at a certain angle from the normal.

** Improved Correlation**

Using the library of Matlab files at our disposal, the scanned image is first "passed through" the rotational matrix to get an array of matrices. Each matrix in this array corresponds to a different angular orientation. (The range of the angle can be user set.) From this point, each matrix in the array is compared to the database image matrix. Again, like in the FFT Method, the maximum point is obtained to find the point of highest correlation. The matrix that contains the largest correlation is the angle that matches most closely to the database image. The process follows these steps:

**FFT Method w/ Rotation Matrix**

- Pass scanned image through a rotation matrix of user set range
- Place each resultant orientation matrix into an array
- Perform the FFT Method on each matrix in the array, against the database image
- Identify the maximum value (highest correlation) and the matrix (image) that a "match" would correspond to.

**Properties of the Rotation + FFT Method**

- Advantages: Much more robust and likely to get a match compared to the regular FFT method; Doesn't take as much time as Spatial Method
- Disadvantages: While it doesn't take as much time as the spatial method, it is slower than the basic FFT method. Also, as the number of increments in the angle range increase, the slower this method gets. Has the potential to become slower than the spatial method.