Name
Abstract | ||
|---|---|---|
We present a model-based method for object identification in images from natural scenes. It has successfully been implemented for the classification of cars based on their rear view. In a first step characteristic features such as lines and corners are detected within the image. Generic models of object-classes, described by the same set of features, are stored in a database. Each model represents a whole class of objects (e.g. passenger cars, vans, big trucks). A pre-processing module suggests a region of interest. A method based on the elastic net technique [2] then is used to map the model on the image features. During this iterative process the model is allowed to undergo changes in scale, position and certain deformations. Deformations are kept within limits such that one model can fit to all objects belonging to the same class, but not to objects of other classes. In each iteration step a value to assess the matching process is obtained. |
Year | DOI | Venue |
|---|---|---|
1992 | 10.1007/978-3-642-77785-1_12 | DAGM-Symposium |
Keywords | Field | DocType |
constrained elastic nets,object recognition,elastic net | Truck,Computer vision,Iterative and incremental development,Pattern recognition,Elastic net regularization,Feature (computer vision),Computer science,Artificial intelligence,Region of interest,Elasticity (economics),Cognitive neuroscience of visual object recognition | Conference |
ISBN | Citations | PageRank |
3-540-55936-1 | 4 | 0.62 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Schwarzinger | 1 | 4 | 0.62 |
| Detlev Noll | 2 | 53 | 8.62 |
| W von Seelen | 3 | 503 | 140.13 |