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| {{ interimage:att_contrastglcm.gif }} | {{ interimage:att_contrastglcm.gif }} | ||
| - | * **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. | + | * **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 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_dissimilarityglcm.gif }} | {{ interimage:att_dissimilarityglcm.gif }} | ||
| - | * **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. | + | * **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 level co-ocurrences instead of using point value frequencies. The co-ocurrence matrix is used and the calculus is shown by 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_entropyglcm.gif }} | {{ interimage:att_entropyglcm.gif }} | ||
| - | * **HomogeneityGLCM** - Returns a value representing the distance between the distribuition of co-ocurrence matrix elements and those diagonal elements. The returned values range is between [0,1]. For images with low values variation the returned value will be near to zero. 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. | + | * **HomogeneityGLCM** - Returns a value representing the distance between the distribution of co-ocurrence matrix elements and those diagonal elements. The returned value range is between [0,1]. For images with low value, variation the returned value will be near zero. The calculus is shown by 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_homogeneityglcm.gif }} | {{ interimage:att_homogeneityglcm.gif }} | ||
| - | * **MeanGLCM** - The GLCM mean value is expressed in function of the frequency of co-ocorrence of image elements related to their neighborhood under one pre-defined direction. 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. | + | * **MeanGLCM** - The GLCM mean value is expressed in function of the frequency of co-occurrence of image elements related to their neighborhood under one pre-defined direction. 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_meanglcm.gif }} | {{ interimage:att_meanglcm.gif }} | ||
| - | * **QuiSquareGLCM** - This metric can be understood as a form of energy normalization expressed in function of the linear dependency gray levels for image elements. The calculus is showed by the next formulas 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. Pj is the marginal probability for that co-ocurrence. | + | * **QuiSquareGLCM** - This metric can be understood as a form of energy normalization expressed in function of the linear dependency gray levels for image elements. The calculus is shown by the next formulas 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. Pj is the marginal probability for that co-ocurrence. |
| {{ interimage:att_quisquareglcm_1.gif }} | {{ interimage:att_quisquareglcm_1.gif }} | ||
| {{ interimage:att_quisquareglcm_2.gif }} | {{ interimage:att_quisquareglcm_2.gif }} | ||
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| - | * **StdDeviationGLCM** - The standart deviation is a measure that represents the values dispersion around a GLCM mean value. The GLCM standart deviation calcule differs from the simple standart deviation because the use of co-ocurrence frequencies. 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. | + | * **StdDeviationGLCM** - The standard deviation is a measure that represents the value dispersion around a GLCM mean value. The GLCM standard deviation calculus differs from the simple standard deviation because the use of co-occurrence frequencies. The calculus is shown by 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_stddeviationglcm.gif }} | {{ interimage:att_stddeviationglcm.gif }} | ||
| ===== Neighborhood Attributes ===== | ===== Neighborhood Attributes ===== | ||
| - | * **existenceOf** - existence of an neighbor object belonging to the selected class **C** in a certain range **R** (in pixels) around the image object. If at least one object is found the value is 1 (true), othewise it would be 0 (false). The distance between the image object and its neighbors is calculated considering their centroids. If (**R**=0) only direct neighbors will be considered. | + | * **existenceOf** - existence of an neighbor object belonging to the selected class **C** in a certain range **R** (in pixels) around the image object. If at least one object is found the value is 1 (true), otherwise it would be 0 (false). The distance between the image object and its neighbors is calculated considering their centroids. If (**R**=0) only direct neighbors will be considered. |
| * **numberOf** - number of neighbor objects belonging to the selected class **C** in a certain range **R** (in pixels) around the image object. The distance between the image object and its neighbors is calculated considering their centroids. If (**R**=0) only direct neighbors will be considered. | * **numberOf** - number of neighbor objects belonging to the selected class **C** in a certain range **R** (in pixels) around the image object. The distance between the image object and its neighbors is calculated considering their centroids. If (**R**=0) only direct neighbors will be considered. | ||
