Research Article
Edge Detection from RGB-D Image Based on Structured Forests
Algorithm 1
Growth randomized tree (
).
Input: training sample set , , | Output: random tree classifier | if all the training samples of belong to the same category or , then | return | end if | select parameter space subset randomly: | for to do | | end for | Compute the optimal parameter of the node classifier: | Set the current dataset of the left and right child nodes : , | for to do | if then | | else | | end if | end for | New left child node: | New right child node: | return |
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