Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision | Next revision Both sides next revision | ||
|
interimage:attributes_description [2010/06/23 11:33] castejon |
interimage:attributes_description [2010/06/23 11:42] castejon |
||
|---|---|---|---|
| Line 101: | Line 101: | ||
| * **Variance** - Like the standart deviation, the variance also represents the numerical data dispersion degree surrounding the mean but in the original data values scale. It is defined by: | * **Variance** - Like the standart deviation, the variance also represents the numerical data dispersion degree surrounding the mean but in the original data values scale. It is defined by: | ||
| {{ interimage:att_variance.gif }} | {{ interimage:att_variance.gif }} | ||
| + | |||
| Line 122: | Line 123: | ||
| {{ interimage:att_dissimilarityglcm.gif }} | {{ interimage:att_dissimilarityglcm.gif }} | ||
| - | * **EntropyGLCM** - | + | * **EntropyGLCM** - Like the simple statistical entropy the GLCM entropy also is a statistical measure of image data randomness. The difference is that it uses frequencies of gray levels co-ocurrences instead of using point values frequencies. The co-ocurrences matrix is used and the calculus is showed by the next formula where "i" and "j" are adjacent image points values under one pre-defined direction. p(i,j) is the probability of that co-ocurrence over the image. |
| + | {{ interimage:att_entropyglcm.gif }} | ||
| * **HomogeneityGLCM** - | * **HomogeneityGLCM** - | ||
