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A recursive process is applied to each subsets in order to select each x that could maximize the information gain. For example, at first an x is selected to partition P into P<sub>d</sub>, P<sub>s</sub>, P<sub>b</sub> with the most information; then for each subset P<sub>d</sub>, P<sub>s</sub>, P<sub>b</sub>, another y is selected to yield the most information gain (notice that the y could be the same as x ). This recursive process ends when the entropy is zero so that either all pixels in that subset are corners or non-corners.
This generated [[decision tree]] can then be converted into programming code, such as [[C (programming language)|C]] and [[C++]], which is just a bunch of nested if-else statements. For optimization purpose, [[profile-guided optimization]] is used to compile the code. The
Notice that the corners detected using this decision tree algorithm should be slightly different from the results using segment test detector. This is because that [[decision tree model]] depends on the training data, which could not cover all possible corners.
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