Name
Abstract | ||
|---|---|---|
Existing approaches to extracting 3D point landmarks based on deformable models require a good model initialization to avoid local suboptima during model fitting. To overcome this drawback, we propose a generally applicable novel hybrid optimization algorithm combining the advantages of both conjugate gradient (cg-)optimization (known for its time efficiency) and genetic algorithms (exhibiting robustness against local suboptima). We apply our algorithm to 3DMR and CTimages depicting tip-like and saddle-like anatomical structures such as the horns of the lateral ventricles in the human brain or the zygomatic bones as part of the skull. Experimental results demonstrate that the robustness of model fitting is significantly improved using hybrid optimization compared to a purely local cg-method. Moreover, we compare an edge strength- to an edge distance-based fitting measure. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/3-540-45404-7_15 | DAGM-Symposium |
Keywords | Field | DocType |
local cg-method,edge strength,model fitting,hybrid optimization algorithm,genetic algorithm,local suboptima,deformable model,good model initialization,hybrid optimization,fitting measure,deformable models,conjugate gradient method,conjugate gradient | Conjugate gradient method,Pattern recognition,Computer science,Robustness (computer science),Musical instrument,Optimization algorithm,Artificial intelligence,Initialization,Anatomical structures,Edge strength,Genetic algorithm | Conference |
ISBN | Citations | PageRank |
3-540-42596-9 | 1 | 0.35 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manfred Alker | 1 | 10 | 1.35 |
| Sönke Frantz | 2 | 76 | 8.01 |
| Karl Rohr | 3 | 377 | 52.96 |
| H. Siegfried Stiehl | 4 | 516 | 67.10 |