Object detection in aerial imagery has been well studied in computer vision for years. However, given the complexity of large variations of the appearance of the object and the background in a typical aerial image, a robust and efficient detection is still considered as an open and challenging problem. In this paper, we have developed a theoretic foundation for aerial imagery object detection using semi-supervised learning algorithms. Based on this theory, we have proposed a context-based object detection methodology. Both theoretic analyses and experimental evaluations have successfully demonstrated the great promise of the developed theory and the related detection methodology.
Semi-supervised learning based object detection in aerial imagery
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) ; 1 ; 1011-1016 vol. 1
01.01.2005
132897 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Object Detection in Aerial Imagery Based on Enhanced Semi-Supervised Learning
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