3D surface registration using estimated local shape variation

Author(s):  
Keiko Ono ◽  
Ryuji Ono ◽  
Yoshiko Hanada
Author(s):  
Mathieu Salzmann ◽  
Francesc Moreno-Noguer ◽  
Vincent Lepetit ◽  
Pascal Fua

Author(s):  
Damian JJ Farnell ◽  
Chern Khor ◽  
Zoe Doyle ◽  
Wayne N Ayre ◽  
Elizabeth Chadwick

3D surface scans were carried out to determine the shapes of the upper sections of (skeletal) crania of adult Eurasian otters (Lutra lutra) from Great Britain. Landmark points were placed on these shapes by using a graphical user interface (GUI) and distance measurements (i.e., the length, height, and width of the crania) could be found by using the landmark points. These “GUI-based” distances were shown to be accurate and reliable in comparison to physical measurements taken on the crania directly by using a digital calliper. The crania of males were 6.85mm, 5.44mm, 1.66mm larger in terms of length, width and height, respectively, than females in our sample (P < 0.001), i.e., male otters had significantly larger skulls than females. Significant differences in size occurred also by geographical area in Great Britain (P < 0.05). Multilevel Principal Components Analysis (mPCA) indicated that sex and geographical area explained 31.1% and 9.6% of shape variation in “unscaled” shape data and that they explained 17.2% and 9.7% of variation in “scaled” data. The first mode of variation at level 1 (sex) correctly reflected size changes between males and females for “unscaled” shape data. Modes at level 2 (geographical area) also showed possible changes in size and shape. Clustering by sex and geographical area was observed in standardised component scores. Such clustering in cranial shape by geographical area might reflect genetic differences that are known to occur in otter populations in Great Britain, although other potentially confounding factors (e.g. population age-structure, diet, etc.) might also drive regional differences. Furthermore, sample sizes per group were small for geographical comparisons. However, this work provides a successful first test of the effectiveness of 3D surface scans and multivariate methods such as mPCA to study the cranial morphology of otters.


2014 ◽  
Vol 41 (6Part9) ◽  
pp. 205-205 ◽  
Author(s):  
T Han ◽  
B Smith ◽  
M Salehpour ◽  
K Gifford

Author(s):  
Jakob Andreas Bærentzen ◽  
Jens Gravesen ◽  
François Anton ◽  
Henrik Aanæs

2020 ◽  
Vol 6 (10) ◽  
pp. 106
Author(s):  
Damian J. J. Farnell ◽  
Chern Khor ◽  
Wayne Nishio Ayre ◽  
Zoe Doyle ◽  
Elizabeth A. Chadwick

Three-dimensional (3D) surface scans were carried out in order to determine the shapes of the upper sections of (skeletal) crania of adult Eurasian otters (Lutra lutra) from Great Britain. Landmark points were placed on these shapes using a graphical user interface (GUI) and distance measurements (i.e., the length, height, and width of the crania) were found by using the landmark points. Male otters had significantly larger skulls than females (P < 0.001). Differences in size also occurred by geographical area in Great Britain (P < 0.05). Multilevel Principal Components Analysis (mPCA) indicated that sex and geographical area explained 31.1% and 9.6% of shape variation in “unscaled” shape data and that they explained 17.2% and 9.7% of variation in “scaled” data. The first mode of variation at level 1 (sex) correctly reflected size changes between males and females for “unscaled” shape data. Modes at level 2 (geographical area) also showed possible changes in size and shape. Clustering by sex and geographical area was observed in standardized component scores. Such clustering in a cranial shape by geographical area might reflect genetic differences in otter populations in Great Britain, although other potentially confounding factors (e.g., population age-structure, diet, etc.) might also drive regional differences. This work provides a successful first test of the effectiveness of 3D surface scans and multivariate methods, such as mPCA, to study the cranial morphology of otters.


2020 ◽  
Vol 123 ◽  
pp. 103860
Author(s):  
Qingguang Chen ◽  
Xing Jin ◽  
Haihua Zhu ◽  
Hassan S. Salehi ◽  
Kaihua Wei

Sign in / Sign up

Export Citation Format

Share Document