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有附件Distinctive Image Features from Scale-Invariant Keypoints

文献技术官 添加于 2011-6-5 20:05 | 3832 次阅读 | 0 个评论
  •  作 者

    Lowe DG
  •  摘 要

    This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.
  •  详细资料

    • 文献种类: Journal Article
    • 期刊名称: International Journal of Computer Vision
    • 期刊缩写: International Journal of Computer Vision
    • 期卷页: 2004  60 2 91-110
    • ISBN: 0920-5691
  • 学科领域 信息系统 » 计算机科学

  • 相关链接 DOI URL 

  •  附 件

    PDF附件Distinctive Image Features from Scale-Invariant Keypoints 
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