Name
Abstract | ||
|---|---|---|
In this paper we introduce a family of cubeness measures, $${\mathcal{C}_{\beta}(S)}$$, as a generalisation of the cubeness measures introduced in Martinez-Ortiz and Žunić (Lecture notes in computer science, vol 5856, pp 716–723, 2009). All measures from the new family retain all desirable properties from the original measure: they range over (0, 1] and reach 1 only when the given shape is a cube; they are invariant with respect to rotation, translation, and scaling transformations. The new measures depend on a parameter β which controls the influence that each individual point from the shape considered contributes to the measure computed. This allows us to create a family of descriptors $${\{\mathcal{C}_{\beta}(S)\ |\ \beta \in (-3,0) \cup (0, \infty)\}}$$, such that the behaviour of any measure $${\mathcal{C}_{\beta}(S),}$$ from the family, varies depending on the assigned parameter β. Because different cubeness measures produce a different shape rankings, using several cubeness measures $${\mathcal{C}_{\beta}(S)}$$ (obtained for different β values) increases the classification efficiency in certain shape classification tasks, as is demonstrated on several examples. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s00138-011-0328-x | Mach. Vis. Appl. |
Keywords | Field | DocType |
3D shape,Shape descriptors,Cubeness measure,Shape classification,Image processing | Discrete mathematics,Pattern recognition,Generalization,Artificial intelligence,Invariant (mathematics),Geometry,Scaling,Mathematics,Cube | Journal |
Volume | Issue | ISSN |
23 | 4 | 0932-8092 |
Citations | PageRank | References |
1 | 0.38 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Martinez-Ortiz | 1 | 25 | 6.54 |
| Joviša Žunić | 2 | 75 | 7.94 |