Name
Abstract | ||
|---|---|---|
This paper describes a color classification technique for the color subspaces definition based in 3D reconstruction approaches. These color subspaces use implicit functions to create a bounding surface that will fit a set of characteristic color samples to define a particular color. The implicit subspace reconstruction allow to define clusters of arbitrary shape for a better approximation of the color distribution, reducing misclassification problems obtained when using predefined geometrical shapes. In addition, the proposed method presents less computational complexity than methods based in color signal transformation. allowing dynamical tuning of the subspaces, and provides robustness and ease parameterization. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30498-2_68 | ADVANCES IN ARTIFICIAL INTELLIGENCE - IBERAMIA 2004 |
Keywords | Field | DocType |
color classification,computer vision,segmentation,implicit surfaces | Computer science,Artificial intelligence,Contextual image classification,Color quantization,Color image,Iterative reconstruction,Discrete mathematics,Computer vision,Pattern recognition,Subspace topology,Linear subspace,Implicit function,Color normalization | Conference |
Volume | ISSN | Citations |
3315 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raziel Álvarez | 1 | 30 | 3.84 |
| Erik Millán | 2 | 1 | 0.37 |
| Ricardo Swain Oropeza | 3 | 17 | 4.28 |
| Alejandro Aceves-lópez | 4 | 2 | 0.75 |