Distinctive Image Features from Scale-Invariant Keypoints View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2004-11

AUTHORS

David G. Lowe

ABSTRACT

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. More... »

PAGES

91-110

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  • Image Processing Device, Image Processing Method, And Program
  • Determining Probabilities From Compared Covariance Appearance Models To Detect Objects Of Interest In Images
  • Method And Apparatus For Color Transfer Between Images
  • Method And System For Image Analysis
  • Multi-Query Privacy-Preserving Parking Management System And Method
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