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. This paper also describes an approach to using these featuresfor object recognition. The recognition proceeds by matching individual fea-tures to a database of Features from known objects using a fast nearest-neighboralgorithm, followed by a Hough transform to identify clusters belonging to a sin-gle object, and finally performing verification through least-squares solution forconsistent pose parameters.

cation based on local image gradient directions. All future operations are performed on image data that has been transformed relative to the assigned orientation, scale, and location for each feature, thereby providing invariance to these transformations. 4. Keypoint descriptor: The local image gradients are measured at the selected scale

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