Name
Abstract | ||
|---|---|---|
We present here a new randomized algorithm for repairing the topology of objects represented by 3D binary digital images. By "repairing the topology", we mean a systematic way of modifying a given binary image in order to produce a similar binary image which is guaranteed to be well-composed. A 3D binary digital image is said to be well-composed if, and only if, the square faces shared by background and foreground voxels form a 2D manifold. Well-composed images enjoy some special properties which can make such images very desirable in practical applications. For instance, well-known algorithms for extracting surfaces from and thinning binary images can be simplified and optimized for speed if the input image is assumed to be well-composed. Furthermore, some algorithms for computing surface curvature and extracting adaptive triangulated surfaces, directly from the binary data, can only be applied to well-composed images. Finally, we introduce an extension of the aforementioned algorithm to repairing 3D digital multivalued images. Such an algorithm finds application in repairing segmented images resulting from multi-object segmentations of other 3D digital multivalued images. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s10851-007-0054-1 | Journal of Mathematical Imaging and Vision |
Keywords | Field | DocType |
Well-composed images,Digital topology,Randomized algorithms | Digital topology,Randomized algorithm,Computer vision,Topology,Binary image,Digital image,Image formation,Artificial intelligence,Binary data,Digital image processing,Mathematics,Binary number | Journal |
Volume | Issue | ISSN |
30 | 3 | 0924-9907 |
Citations | PageRank | References |
18 | 0.87 | 32 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcelo Siqueira | 1 | 58 | 5.76 |
| Longin Jan Latecki | 2 | 3301 | 176.88 |
| Nicholas J. Tustison | 3 | 1362 | 58.82 |
| Jean Gallier | 4 | 270 | 22.72 |
| James C. Gee | 5 | 4558 | 321.75 |