Synthesizing trees from samples
نویسندگان
چکیده
In this paper, we propose a novel tree modeling approach, synthesizing new trees from samples. First, we capture real world trees as samples by image-based modeling or laser scanning techniques. Then, we present a two-level statistical tree model and design a maximum likelihood estimation algorithm to extract it from samples. At the low level in the tree model, groups of similar organs are clustered to depict tree organ details statistically. At the high level, a set of transitions between clusters is outlined to describe the stochastic distribution of organs. Experimental results show that our two-level model extracted from samples is capable of synthesizing new trees similar, yet visually different.
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