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| - | ===== Spectral Texture Attributes ===== | ||
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| + | ===== Texture Attributes ===== | ||
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| + | The texture attributes are based on the co-occurence gray scale matrix (GLCM) described by the following references: | ||
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| + | * 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. | ||
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| + | * **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 }} | ||
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| + | * **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 }} | ||
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| + | * **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 }} | ||
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| + | * **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 }} | ||
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| + | * **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. | ||
| + | {{ interimage:att_homogeneityglcm.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. | ||
| + | {{ interimage:att_meanglcm.gif }} | ||
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| + | * **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. | ||
| + | {{ interimage:att_quisquareglcm_1.gif }} | ||
| + | {{ interimage:att_quisquareglcm_2.gif }} | ||
| + | {{ interimage:att_quisquareglcm_3.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. | ||
| + | {{ interimage:att_stddeviationglcm.gif }} | ||
| ===== Neighborhood Attributes ===== | ===== Neighborhood Attributes ===== | ||
