Abstract
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) |
| Editors | A Hoogs, L Davis |
| Place of Publication | United States |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Number of pages | 7 |
| ISBN (Print) | 9781509006410 |
| DOIs | |
| Publication status | Published - 2016 |
| Event | 2016 IEEE Winter Conference on Applications of Computer Vision - Lake Placid, United States Duration: 7 Mar 2016 → 10 Mar 2016 |
Conference
| Conference | 2016 IEEE Winter Conference on Applications of Computer Vision |
|---|---|
| Country | United States |
| City | Lake Placid |
| Period | 7/03/16 → 10/03/16 |
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Heat Propagation Contours for 3D Non-rigid Shape Analysis. / Wang, X.; Sohel, Ferdous; Bennamoun, Mohammed; Lei, H.
2016 IEEE Winter Conference on Applications of Computer Vision (WACV). ed. / A Hoogs; L Davis. United States : IEEE, Institute of Electrical and Electronics Engineers, 2016. 7477648.Research output: Chapter in Book/Conference paper › Conference paper
TY - GEN
T1 - Heat Propagation Contours for 3D Non-rigid Shape Analysis
AU - Wang, X.
AU - Sohel, Ferdous
AU - Bennamoun, Mohammed
AU - Lei, H.
PY - 2016
Y1 - 2016
N2 - We present a novel local shape descriptor by means of General Adaptive Neighborhoods (GANs) based on the properties of the heat diffusion process on a Riemannian manifold. The GAN is a spatial region, surrounding the feature point and fitting its local shape structure, which is isometric. Our signature, called the Heat Propagation Contours (HPCs), is obtained by analysing the well-known heat kernel and extracting contours automatically within the GAN as heat dissipates from the feature point onto the rest of the shape. HPCs capture geometric information around the feature point by investigating the heat propagation process both in the temporal and spatial domain. HPCs share many useful characteristics with the heat based methods. Particularly, it captures the intrinsic geometry of a shape and is suitable for non-rigid shape analysis. In addition, our signature provides an elegant and efficient way to describe the neighborhood of the feature point in a multi-scale approach. The proposed descriptor is evaluated on several datasets to demonstrate its effectiveness. © 2016 IEEE.
AB - We present a novel local shape descriptor by means of General Adaptive Neighborhoods (GANs) based on the properties of the heat diffusion process on a Riemannian manifold. The GAN is a spatial region, surrounding the feature point and fitting its local shape structure, which is isometric. Our signature, called the Heat Propagation Contours (HPCs), is obtained by analysing the well-known heat kernel and extracting contours automatically within the GAN as heat dissipates from the feature point onto the rest of the shape. HPCs capture geometric information around the feature point by investigating the heat propagation process both in the temporal and spatial domain. HPCs share many useful characteristics with the heat based methods. Particularly, it captures the intrinsic geometry of a shape and is suitable for non-rigid shape analysis. In addition, our signature provides an elegant and efficient way to describe the neighborhood of the feature point in a multi-scale approach. The proposed descriptor is evaluated on several datasets to demonstrate its effectiveness. © 2016 IEEE.
U2 - 10.1109/WACV.2016.7477648
DO - 10.1109/WACV.2016.7477648
M3 - Conference paper
SN - 9781509006410
BT - 2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
A2 - Hoogs, A
A2 - Davis, L
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - United States
ER -