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interimage:attributes_description [2010/06/23 13:41] castejon |
interimage:attributes_description [2010/06/23 15:23] rsilva |
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| {{ :interimage:shapeindex.png }} | {{ :interimage:shapeindex.png }} | ||
| Where P is the polygon perimeter and A is the area. | Where P is the polygon perimeter and A is the area. | ||
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| ===== Spectral Statistical Attributes ===== | ===== Spectral Statistical Attributes ===== | ||
| - | * **Amplitude** - | + | * **Amplitude** - represents the difference between the maximum and minimum pixel values of a region for the given image band/channel. |
| * **Brightness** - | * **Brightness** - | ||
| - | * **Correlation** - Correlation is a similarity measures between two data sets under an absolute scale between [-1,1]. It is calculated as showed by the next formula: | + | * **Correlation** - Correlation is a similarity measure between two data sets under an absolute scale between [-1,1]. It is calculated as showed by the next formula: |
| {{ interimage:att_correlation.gif }} | {{ interimage:att_correlation.gif }} | ||
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| * **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 }} | ||
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| * **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-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. | ||
| - | {{ 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 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. | ||
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| - | * **StdDeviationGLCM** - | + | * **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. |
| + | {{ interimage:att_stddeviationglcm.gif }} | ||
| ===== Neighborhood Attributes ===== | ===== Neighborhood Attributes ===== | ||
