Name
Abstract | ||
|---|---|---|
We investigate the discriminant power of two local and two global texture measures on virus images. The viruses are imaged using negative stain transmission electron microscopy. Local binary patterns and a multi scale extension are compared to radial density profiles in the spatial domain and in the Fourier domain. To assess the discriminant potential of the texture measures a Random Forest classifier is used. Our analysis shows that the multi scale extension performs better than the standard local binary patterns and that radial density profiles in comparison is a rather poor virus texture discriminating measure. Furthermore, we show that the multi scale extension and the profiles in Fourier domain are both good texture measures and that they complement each other well, that is, they seem to detect different texture properties. Combining the two, hence, improves the discrimination between virus textures. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-25085-9_68 | CIARP |
Keywords | Field | DocType |
virus texture,virus texture analysis,fourier domain,standard local binary pattern,local binary pattern,global texture measure,different texture property,spatial domain,radial density profile,good texture measure,poor virus texture,multi scale extension,local binary patterns | Computer vision,Pattern recognition,Computer science,Discriminant,Rather poor,Local binary patterns,Fourier transform,Artificial intelligence,Random forest,Virus morphology,Power of two | Conference |
Volume | ISSN | Citations |
7042 | 0302-9743 | 14 |
PageRank | References | Authors |
0.67 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gustaf Kylberg | 1 | 60 | 3.80 |
| Mats Uppström | 2 | 14 | 0.67 |
| Ida-Maria Sintorn | 3 | 114 | 13.85 |