Global and Local Shape Analysis of the Hippocampus Based on Level-of-Detail Representations

Author(s):  
Jeong-Sik Kim ◽  
Soo-Mi Choi ◽  
Yoo-Joo Choi ◽  
Myoung-Hee Kim
2004 ◽  
Vol 11A (7) ◽  
pp. 555-562
Author(s):  
Jeong-Sik Kim ◽  
Soo-Mi Choi ◽  
Yoo-Ju Choi ◽  
Myoung-Hee Kim

2012 ◽  
Vol 239-240 ◽  
pp. 694-699
Author(s):  
Li Feng Yao ◽  
Jian Fei Ouyang ◽  
Xiang Ma

In bio-medicine and other fields, shape analysis is very important for diagnosis of diseases and prediction of shape variation. This paper focuses on the surface parameterization of tube-like 3D objects to obtain and analyze shape information from a sample shape, including its size and the shape variation between different samples. It can well represent the global and local shape information for statistical analysis and for the construction of Medial Shape Model. Firstly, we extract the axis curve of the object by a heat conduction model. Secondly, we obtain the latitude circles by using the normal planes to cross the surface. Then we get the final parameterized surface with quad-dominant meshes by registering the points of single latitude circle and between different circles through coordinate transformation and alignment. Subsequently, we apply the approach to parameterization of a rib bone.


2015 ◽  
Vol 149 ◽  
pp. 1535-1543 ◽  
Author(s):  
Jian Zhang ◽  
Dapeng Tao ◽  
Xiangjuan Bian ◽  
Xiaosi Zhan

2018 ◽  
Vol 151 ◽  
pp. 31-40 ◽  
Author(s):  
Patricia Dore ◽  
Ardian Dumani ◽  
Geddes Wyatt ◽  
Alex J. Shepherd

2011 ◽  
Vol 58 (12) ◽  
pp. 3418-3428 ◽  
Author(s):  
S. Diciotti ◽  
S. Lombardo ◽  
M. Falchini ◽  
G. Picozzi ◽  
M. Mascalchi

2012 ◽  
Vol 1 (1) ◽  
pp. 3 ◽  
Author(s):  
J. Määttä ◽  
A. Hadid ◽  
M. Pietikäinen

Author(s):  
Suni S S ◽  
Gopakumar K

In this work a framework based on histogram of orientation of optical flow (HOOF) and local binary pattern from three orthogonal planes (LBP_TOP) is proposed for recognizing dynamic hand gestures. HOOF algorithm extracts local shape and dynamic motion information of gestures from image sequences and local descriptor LBP is extended to three orthogonal planes to create an efficient motion descriptor. These features are invariant to scale, translation, illumination and direction of motion. The performance of the new framework is tested in two different ways. The first one is by fusing the global and local features as one descriptor and the other is using features separately to train the multi class support vector machine. Performance analysis shows that the proposed approach produces better results for recognizing dynamic hand gestures when compared with state of the art methods


Sign in / Sign up

Export Citation Format

Share Document