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 17:14] hermann |
interimage:attributes_description [2010/06/23 17:18] hermann |
||
|---|---|---|---|
| Line 114: | Line 114: | ||
| ===== Texture Attributes ===== | ===== Texture Attributes ===== | ||
| - | The texture attributes are based on the co-occurence gray scale matrix (GLCM) described by the following references: | + | The texture attributes are based on the co-occurrence gray scale matrix (GLCM) described in the following references: |
| - | * Textural Features for Image Classification - Robert M. Haralick, K. Shanmugam, Its'hak Dinstein. Systems, Man and Cybernetics, IEEE Transactions on In Systems, Man and Cybernetics, IEEE Transactions on, Vol. 3, No. 6. (1973), pp. 610-621. | + | * Textural Features for Image Classification - Robert M. Haralick, K. Shanmugam, Its'hak Dinstein. Systems, Man and Cybernetics, IEEE Transactions on Systems, Man and Cybernetics, IEEE Transactions on, Vol. 3, No. 6. (1973), pp. 610-621. |
| * Computer and Robot Vision - Robert M. Haralick - Addison-Wesley Publishing Company. | * Computer and Robot Vision - Robert M. Haralick - Addison-Wesley Publishing Company. | ||
| \\ | \\ | ||
| - | * **Angular2ndMomentGLCM (a.k.a. EnergyGLCM)** - Returns the square sum of image points pairs occurrences under one pre-defined direction. The returned values range is between [0,1]. For those images without variations the value will be 1. The calculus is showed on 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. | + | * **Angular2ndMomentGLCM (a.k.a. EnergyGLCM)** - Returns the square sum of image point pairs occurrences under one pre-defined direction. The returned value range is between [0,1]. For those images without variations the value will be 1. The calculus is shown on the next formula where "i" and "j" are adjacent image point values under one pre-defined direction. p(i,j) is the probability of that co-occurrence over the image. |
| {{ interimage:att_angular2ndmomentglcm.gif }} | {{ interimage:att_angular2ndmomentglcm.gif }} | ||
| - | * **ContrastGLCM** - Returns a contrast intensity measure between one image point and its neighborhood. For those images without variations the contrast value will be zero. The calculus is showed on 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. | + | * **ContrastGLCM** - Returns a contrast intensity measure between one image point and its neighborhood. For those images without variations the contrast value will be zero. The calculus is shown on the next formula where "i" and "j" are adjacent image point values under one pre-defined direction. p(i,j) is the probability of that co-ocurrence over the image. |
| {{ interimage:att_contrastglcm.gif }} | {{ interimage:att_contrastglcm.gif }} | ||
