The all-geodesic furthest neighbor problem for simple polygons
SCG '87 Proceedings of the third annual symposium on Computational geometry
Shock Graphs and Shape Matching
International Journal of Computer Vision
The Discrete Analytical Hyperspheres
IEEE Transactions on Visualization and Computer Graphics
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Handbook of Mathematical Models in Computer Vision
Handbook of Mathematical Models in Computer Vision
Decomposition for efficient eccentricity transform of convex shapes
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Shape representation and classification using the Poisson equation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
The eccentricity transform (of a digital shape)
DGCI'06 Proceedings of the 13th international conference on Discrete Geometry for Computer Imagery
An improved coordinate system for point correspondences of 2D articulated shapes
DGCI'09 Proceedings of the 15th IAPR international conference on Discrete geometry for computer imagery
Matching 2D and 3D articulated shapes using the eccentricity transform
Computer Vision and Image Understanding
Invariant representative cocycles of cohomology generators using irregular graph pyramids
Computer Vision and Image Understanding
| Hi-index | 0.00 |
The eccentricity transform associates to each point of a shape the geodesic distance to the point farthest away from it. The transform is defined in any dimension, for simply and non simply connected sets. It is robust to Salt & Pepper noise and is quasi-invariant to articulated motion. Discrete analytical concentric circles with constant thickness and increasing radius pave the 2D plane. An ordering between pixels belonging to circles with different radius is created that enables the tracking of a wavefront moving away from the circle center. This is used to efficiently compute the single source shape bounded distance transform which in turn is used to compute the eccentricity transform. Experimental results for three algorithms are given: a novel one, an existing one, and a refined version of the existing one. They show a good speed/error compromise.