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| - | ===== Spectral Texture Attributes ===== | ||
| - | The texture attributes are based on the co-occurence gray scale matrix (GLCM) described by 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. | ||
| - | * Computer and Robot Vision - Robert M. Haralick - Addison-Wesley Publishing Company. | ||
| - | * Angular2ndMomentGLCM (a.k.a. EnergyGLCM) | + | ===== Texture Attributes ===== |
| + | |||
| + | The texture attributes are based on the co-occurence gray scale matrix (GLCM) described by 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. | ||
| + | * 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. | ||
| + | {{ interimage:att_angular2ndmomentglcm.gif }} | ||
| - | * ContrastGLCM - The contrast is a estimate of the local variations. Average of the squares of the differences between pixels. | + | * **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. |
| + | {{ interimage:att_contrastglcm.gif }} | ||
| - | * DissimilarityGLCM | + | * **DissimilarityGLCM** - Returns one intensity measure quite similar to contrast between one point and its neighborhood. But the difference it that this measure has linear increments. 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. |
| + | {{ 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** - |
| - | * MeanGLCM | + | * **MeanGLCM** - |
| - | * QuiSquareGLCM | + | * **QuiSquareGLCM** - |
| - | * StdDeviationGLCM | + | * **StdDeviationGLCM** - |
