Name
Abstract | ||
|---|---|---|
Digital images are generally created as discrete measurements of light, as performed by dedicated sensors. Consequently, each pixel contains a discrete approximation of the light inciding in a sensor element. The nature of this measurement implies certain uncertainty due to discretization matters. In this work we propose to model such uncertainty using intervals, further leading to the generation of so-called interval-valued images. Then, we study the partial differentiation of such images, putting a spotlight on antisymmetric convolution operators for such task. Finally, we illustrate the utility of the interval-valued images by studying the behaviour of an extended version of the well-known Canny edges detection method. |
Year | Venue | Keywords |
|---|---|---|
2015 | IFSA-EUSFLAT | Image processing,Interval-valued information,Edge detection,Canny method |
Field | DocType | Volume |
Canny edge detector,Discretization,Computer vision,Convolution,Edge detection,Image processing,Antisymmetric relation,Digital image,Operator (computer programming),Artificial intelligence,Mathematics | Conference | 89 |
ISSN | Citations | PageRank |
1951-6851 | 1 | 0.35 |
References | Authors | |
19 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Lopez-Molina | 1 | 231 | 21.58 |
| Cédric Marco-Detchart | 2 | 1 | 0.35 |
| Juan Cerron | 3 | 8 | 2.36 |
| Humberto Bustince | 4 | 1938 | 134.10 |
| Bernard De Baets | 5 | 2994 | 300.39 |