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.


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    Title :

    Semi-supervised learning based object detection in aerial imagery


    Contributors:
    Jian Yao, (author) / Zhongfei Zhang, (author)


    Publication date :

    2005-01-01


    Size :

    132897 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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