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--- interimage:attributes_description [2010/06/23 10:23]
castejon
+++ interimage:attributes_description [2010/06/23 13:41]
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@@ Line 57: / Line 57: @@
  {{ :interimage:shapeindex.png }}
 Where P is the polygon perimeter and A is the area.
@@ Line 65: / Line 66: @@
 ===== Spectral Statistical Attributes =====
-  * **Amplitude** - Blablabla.
+  * **Amplitude** -
-  * **Brightness** - Blablabla.
+  * **Brightness** -
-  * **Correlation** - Blablabla.
+  * **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:
+{{ interimage:att_correlation.gif }}
-  * **Covariance** - Blablabla.
+  * **Covariance** - The covariance value represents the similarity degree between two data sets showing how correlated they are. Higher data correlation leads to higher covariance values. The calculus is showed by the following formula where N is the number of image elements for one given area. X(i) are the element values for each given index "i".
+{{ interimage:att_covariance.gif }}
   * **Entropy** - This is a randomness statistical measure that can be used to describe some texture features. Higher data randomness leads to higher entropy values. The calculus is done as showed by the next formula, where n is the number of distinct image element values and p(xi) is the occurrence frequence associated to that pixel value.:
@@ Line 89: / Line 92: @@
-  * **Ratio** - Blablabla.
+  * **Ratio** -
   * **StdDeviation** - The standart deviation represents the numerical data dispersion degree surrounding the mean. It is defined by:
@@ Line 99: / Line 102: @@
 {{ interimage:att_variance.gif }}
-===== Spectral Texture Attributes =====
+===== 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** - 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** - 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** - 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** - 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 }}
+  * **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}}
+  * **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 }}
+  * **StdDeviationGLCM** -

Trace:

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