TY - JOUR
T1 - SurfCut: Surfaces of Minimal Paths From Topological Structures
AU - Algarni, Marei Saeed Mohammed
AU - Sundaramoorthi, Ganesh
N1 - KAUST Repository Item: Exported on 2020-04-23
Acknowledged KAUST grant number(s): OCRF-2014-CRG3-62140401
Acknowledgements: This work was supported by KAUST OCRF-2014-CRG3-62140401, and the Visual Computing Center at KAUST.
PY - 2018/3/5
Y1 - 2018/3/5
N2 - We present SurfCut, an algorithm for extracting a smooth, simple surface with an unknown 3D curve boundary from a noisy image and a seed point. Our method is built on the novel observation that certain ridge curves of a function defined on a front propagated using the Fast Marching algorithm lie on the surface. Our method extracts and cuts these ridges to form the surface boundary. Our surface extraction algorithm is built on the novel observation that the surface lies in a valley of the distance from Fast Marching. We show that the resulting surface is a collection of minimal paths. Using the framework of cubical complexes and Morse theory, we design algorithms to extract these critical structures robustly. Experiments on three 3D datasets show the robustness of our method, and that it achieves higher accuracy with lower computational cost than state-of-the-art.
AB - We present SurfCut, an algorithm for extracting a smooth, simple surface with an unknown 3D curve boundary from a noisy image and a seed point. Our method is built on the novel observation that certain ridge curves of a function defined on a front propagated using the Fast Marching algorithm lie on the surface. Our method extracts and cuts these ridges to form the surface boundary. Our surface extraction algorithm is built on the novel observation that the surface lies in a valley of the distance from Fast Marching. We show that the resulting surface is a collection of minimal paths. Using the framework of cubical complexes and Morse theory, we design algorithms to extract these critical structures robustly. Experiments on three 3D datasets show the robustness of our method, and that it achieves higher accuracy with lower computational cost than state-of-the-art.
UR - http://hdl.handle.net/10754/626556
UR - http://arxiv.org/abs/1705.00301v1
UR - http://www.scopus.com/inward/record.url?scp=85042885760&partnerID=8YFLogxK
U2 - 10.1109/TPAMI.2018.2811810
DO - 10.1109/TPAMI.2018.2811810
M3 - Article
C2 - 29993597
AN - SCOPUS:85042885760
VL - 41
SP - 726
EP - 739
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
SN - 0162-8828
IS - 3
ER -