Haralick Textural Features: Difference between revisions
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Then <math>P(i,j,d,\alpha)</math> is the number of occurrences where a pixel with value <math>i</math> and a pixel with value <math>j</math> are distance <math>d</math> apart along angle <math>\alpha \in \{0^\circ, 45^\circ, 90^\circ, 135^\circ\}</math>. | Then <math>P(i,j,d,\alpha)</math> is the number of occurrences where a pixel with value <math>i</math> and a pixel with value <math>j</math> are distance <math>d</math> apart along angle <math>\alpha \in \{0^\circ, 45^\circ, 90^\circ, 135^\circ\}</math>. Note that each neighbor pair is counted twice (e.g. pixel 1 is neighbor of 0 and 0 is neighbor of 1). | ||
If we fix <code>d=1</code>, then we get four matrices of co-occurances along each direction: | If we fix <code>d=1</code>, then we get four matrices of co-occurances along each direction: | ||
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#* Note that the original paper has a typo. | #* Note that the original paper has a typo. | ||
# Entropy: <math>f_9 = - \sum_i \sum_j p(i,j) \log(p(i,j))</math> | # Entropy: <math>f_9 = - \sum_i \sum_j p(i,j) \log(p(i,j))</math> | ||
# Difference Variance: <math> | # Difference Variance: <math>f_{10}= var(p_{x-y})</math> | ||
# Difference Entropy: <math>f_{11} = -\sum_{i=0}^{N_{g}-1} p_{x-y}(i) \log p_{x-y}(i)</math> | # Difference Entropy: <math>f_{11} = -\sum_{i=0}^{N_{g}-1} p_{x-y}(i) \log p_{x-y}(i)</math> | ||
# Information Measures of Correlation 1: | # Information Measures of Correlation 1: | ||