Name
Abstract | ||
|---|---|---|
Model-based image interpretation extracts high-level information from images using a priori knowledge about the object of interest. The computational challenge is to determine the model parameters that best match a given image by searching for the global optimum of the involved objective function. Unfortunately, this function is usually designed manually, based on implicit and domain-dependent knowledge, which prevents the fitting task from yielding accurate results. In this paper, we demonstrate how to improve model fitting by learning objective functions from annotated training images. Our approach automates many critical decisions and the remaining manual steps hardly require domain-dependent knowledge. This yields more robust objective functions that are able to achieve the accurate model fit. Our evaluation uses a publicly available image database and compares the obtained results to a recent state-of-the-art approach. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74936-3_49 | DAGM-Symposium |
Keywords | Field | DocType |
model fitting,model-based image interpretation,accurate model fit,domain-dependent knowledge,involved objective function,robust objective function,model parameter,objective function,available image database,annotated training image,a priori knowledge | Data mining,Computer science,A priori and a posteriori,Global optimum,Artificial intelligence,Image database,Machine learning | Conference |
Volume | ISSN | Citations |
4713 | 0302-9743 | 2 |
PageRank | References | Authors |
0.38 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias Wimmer | 1 | 109 | 10.91 |
| Sylvia Pietzsch | 2 | 50 | 4.26 |
| Freek Stulp | 3 | 448 | 40.02 |
| Bernd Radig | 4 | 460 | 110.42 |