Transcription of Distinctive Image Features from Scale-Invariant Keypoints
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Distinctive Image Features from Scale-Invariant Keypoints
Distinctive Image Featuresfrom Scale-Invariant KeypointsDavid G. LoweComputer Science DepartmentUniversity of British ColumbiaVancouver, , 5, 2004 AbstractThis paper presents a method for extracting Distinctive invariant Features fromimages that can be used to perform reliable matching betweendifferent views ofan object or scene. The Features are invariant to Image scaleand rotation, andare shown to provide robust matching across a a substantial range of affine dis-tortion, change in 3D viewpoint, addition of noise, and change in Features are highly Distinctive , in the sense that a single feature can be cor-rectly matched with high probability against a large database of Features frommany images.
Marsic and Dickinson (1999) provides more distinctive feature descriptors using wavelet co-efficients. The problem of identifying an appropriate and co nsistent scale for feature detection has been studied in depth by Lindeberg (1993, 1994). He describes this as a problem of scale selection, and we make use of his results below.
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