Hybrid boundary-medial shape description for biologically variable shapes

Author(s):  
M. Styner ◽  
G. Gerig
Keyword(s):  
2021 ◽  
pp. 0734242X2110450
Author(s):  
Yifeng Li ◽  
Xunpeng Qin ◽  
Zhenyuan Zhang ◽  
Huanyu Dong

Transport and separation processes of solid waste can only be modelled successfully with discrete element methods in case the shape of the particles can be described accurately. The existing techniques for morphological data acquisition, such as computed tomography, laser scanning technique, optical interferometer, stereo photography and structured light technique, are laborious and require a large amount of realistic solid waste samples. Therefore, there is a pressing need for an alternative method to describe the shape of solid waste particles and to generate multiple variations of particles with almost similar shapes. In this paper, a new method to describe solid waste particles is proposed that is frequency-based and uses spherical harmonics (SHs). Additionally, a new shape generation method is introduced that uses the shape description of a single particle to generate an array of related shapes based on a probability density function with a dimensionless control factor η. The newly proposed methods were successfully applied to describe the complex shapes of pieces of metal and plastic scrap. The shapes of these pieces of scrap can be described adequately with 15° of SH expansion and the overall divergence is within 0.1 mm. Five different values for η were tested, which generated shapes with the same distribution as the original particle. Rising levels of η cause the morphological variation of the generated particles to increase. These new methods improve the modelling of transportation and separation processes.


2009 ◽  
Vol 8 (1) ◽  
pp. 89-97 ◽  
Author(s):  
Xiaopeng Zhang ◽  
Jianfei Liu ◽  
Marc Jaeger ◽  
Zili Li

Hierarchical skeletons and shape components are important shape features, and they are useful for shape description and shape understanding. Techniques to extract shape components and hierarchical skeletons from volume data are analyzed in this paper based on multiple distance transformations. The application of volume decomposition for the extraction of hierarchical skeletons is emphasized and specified here. This work includes an establishment of the hierarchical structure of the object volume, a decomposition of the volume into simple sub-volumes, an extraction of compact skeleton segments corresponding to each independent sub-volume, and a connection of these skeleton segments into a hierarchical structure reflecting the organization the initial data


Behaviour ◽  
2020 ◽  
Vol 158 (1) ◽  
pp. 1-18
Author(s):  
Fanrong Xiao ◽  
Rongping Bu ◽  
Haitao Shi

Abstract Masquerade occurs when an organism resembles an inedible or uninteresting object (model), such as a leaf, stick, or stone. The shapes of many species are described as similar to those of models in their microhabitats, but these similarities have not been quantified effectively. To describe the shape similarity between an animal and a model, we applied a shape description method in a field study of the four-eyed turtle (Sacalia quadriocellata). Our results showed that shape similarity between turtles and stones in the Hezonggou River was significantly higher midstream than that upstream and downstream. In line with these findings, masquerade efficiency was highest for turtles in the midstream area, with both inexperienced and experienced human ‘predators’. Masquerade efficiency was positively correlated with shape similarity in all stream sections. Shape similarity ranged from 0 to 1, with <0.65 indicating low masquerade efficiency and >0.80 indicating high masquerade efficiency. Our quantitative method was able to provide data that could be used to form an ecologically plausible argument; thus, shape similarity can be used to assess animal’s masquerade efficiency. This method will be of considerable use in future animal masquerade research.


1995 ◽  
Vol 13 (2) ◽  
pp. 69-83 ◽  
Author(s):  
NIGEL N. CLARK ◽  
STEPHANIE REILLY

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