Name
Abstract | ||
|---|---|---|
In this work we present a novel technique we term active graph matching, which integrates the popular active shape model into a sparse graph matching problem. This way we are able to combine the benefits of a global, statistical deformation model with the benefits of a local deformation model in form of a second-order random field. We present a new iterative energy minimization technique which achieves empirically good results. This enables us to exceed state-of-the art results for the task of annotating nuclei in 3D microscopic images of C. elegans. Furthermore with the help of the generalized Hough transform we are able to jointly segment and annotate a large set of nuclei in a fully automatic fashion for the first time. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10404-1_11 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Active shape model,Computer vision,Random field,Annotation,Pattern recognition,Segmentation,Computer science,Hough transform,Matching (graph theory),Artificial intelligence,Dense graph,Energy minimization | Conference | 8673 |
Issue | ISSN | Citations |
Pt 1 | 0302-9743 | 5 |
PageRank | References | Authors |
0.50 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dagmar Kainmueller | 1 | 57 | 7.20 |
| Florian Jug | 2 | 17 | 2.01 |
| Carsten Rother | 3 | 9074 | 451.62 |
| Eugene Myers | 4 | 3164 | 496.92 |