| Autor: |
Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Bebis, George, Boyle, Richard, Parvin, Bahram, Koracin, Darko, Paragios, Nikos |
| Zdroj: |
Advances in Visual Computing (978-3-540-76857-9); 2007, p385-392, 8p |
| Abstrakt: |
The aim of this study is to classify structural cartographic objects in high-resolution satellite images. The target classes have an important intra-class variability because the class definitions belong to high-level concepts. Structural attributes seem to be the most plausible cues for the classification task. We propose an Adaboost learning method using edge-based features as weak learners. Multi-scale sub-pixel edges are converted to geometrical primitives as potential evidences of the target object. A feature vector is calculated from the primitives and their perceptual groupings, by the accumulation of combinations of their geometrical and spatial attributes. A classifier is constructed using the feature vector. The main contribution of this paper is the usage of structural shape attributes in a statistical learning method framework. We tested our method on CNES dataset prepared for the ROBIN Competition and we obtained promising results. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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