ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
3d modeling of coronary artery bifurcations from CTA and conventional coronary angiography
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Avoiding mesh folding in 3D optimal surface segmentation
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Convolutional virtual electric field external force for active contours
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Gradient vector flow over manifold for active contours
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Computer Graphics Forum
Fast gradient vector flow computation based on augmented Lagrangian method
Pattern Recognition Letters
Curve skeleton extraction by graph contraction
CVM'12 Proceedings of the First international conference on Computational Visual Media
L1-medial skeleton of point cloud
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Poisson Skeleton Revisited: a New Mathematical Perspective
Journal of Mathematical Imaging and Vision
| Hi-index | 0.15 |
Representing a 3D shape by a set of 1D curves that are locally symmetric with respect to its boundary (i.e., curve skeletons) is of importance in several machine intelligence tasks. This paper presents a fast, automatic, and robust variational framework for computing continuous, subvoxel accurate curve skeletons from volumetric objects. A reference point inside the object is considered a point source that transmits two wave fronts of different energies. The first front (\beta-front) converts the object into a graph, from which the object salient topological nodes are determined. Curve skeletons are tracked from these nodes along the cost field constructed by the second front (\alpha-front) until the point source is reached. The accuracy and robustness of the proposed work are validated against competing techniques as well as a database of 3D objects. Unlike other state-of-the-art techniques, the proposed framework is highly robust because it avoids locating and classifying skeletal junction nodes, employs a new energy that does not form medial surfaces, and finally extracts curve skeletons that correspond to the most prominent parts of the shape and hence are less sensitive to noise.