Name
Abstract | ||
|---|---|---|
An image thinning technique using a neural network is proposed. Using different activation functions at different layers,
the proposed neural network removes the boundary pixels from four directions in such a manner that the general configuration
of the input pattern is unaltered and the connectivity is preserved. The resulting object, called a skeleton, provides an
abstraction of the global shape of the object. The skeleton is often useful for geometrical and structural analysis of the
object. The output skeleton here satisfies the basic properties of a skeleton, namely connectivity and unit thickness. The
proposed method is experimentally found to be more efficient in terms of better medial axis representation and robustness
to boundary noise over a few existing algorithms. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/s005210200024 | Neural Computing and Applications |
Keywords | DocType | Volume |
thinning,neural network,binary image,skeleton,shape | Journal | 11 |
Issue | ISSN | Citations |
2 | 1433-3058 | 0 |
PageRank | References | Authors |
0.34 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amitava Datta | 1 | 734 | 81.63 |
| Srimanta Pal | 2 | 242 | 32.13 |
| Sushanta Chakraborti | 3 | 0 | 0.34 |