Sentence examples for skeleton concepts from inspiring English sources

In recent years, the field of computer graphics developed several new skeleton concepts that make use of prior knowledge of the plant network.

In the remainder of the article, I introduce two basic skeleton concepts in detail and demonstrate their applicability to plant networks on selected examples.

Two basic skeleton concepts used today are the medial axis (Lee, 1982) and the Reeb graph (Biasotti et al., 2008) shown in Fig. 3.

In the following, I apply the introduced skeleton concepts to images from a freely available rice root data set used in Iyer-Pascuzzi et al. (2010).

Current literature reviews a plethora of skeleton concepts to be computed from 2D binary images (Lam et al., 1992), 3D images of voxels (Sobiecki et al., 2014), and point clouds (Cornea and Min, 2007; Bucksch, 2011).

Although I used several thresholds in this article, I do not propose them as an optimal algorithmic solution, but as a simple tool to start experiencing and understanding skeleton concepts.

The limiting factor is not the skeleton concept, but the imaging technology and its ability to resolve the complete plant network.

Pound et al. (2013) introduced a skeletonization concept for plant phenotyping that uses a probability field computed from the gray values of a 2D image to grow a connected skeleton of young and sparse roots.

Fibers are included in the particle size distribution of the solid skeleton through the concept of an equivalent diameter, defined on the basis of the specific surface.

On the basis of evidence that does not support the traditional concept that inserts and orthoses are used to align the skeleton, they proposed a new concept for the function and fitting of shoes and shoe inserts.

The key ideas are using level set functions to describe the shapes of arbitrary irregular embedding components and resorting to the concept of structural skeleton to formulate the distance control constraints explicitly.