tailieunhanh - Distinctive Image Features from Scale-Invariant Keypoints

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 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 | Distinctive Image Features from Scale-Invariant Keypoints David G. Lowe Computer Science Department University of British Columbia Vancouver . Canada lowe@ January 5 2004 Abstract This paper presents a method for extracting distinctive invariant features from images that canbe usedtoperform 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 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 amongclutter andocclusion while achieving nearreal-time performance. Accepted for publication in the International Journal of Computer Vision 2004. 1 1 Introduction Image matching is a fundamental aspect of many problems in computer vision including object or scene recognition solving for 3D structure from multiple images stereo correspondence and motion tracking. This paper describes image features that have many properties that make them suitable for matching differing images of an object or scene. The features are invariant to image scaling and rotation and partially invariant to change in illumination and 3D camera viewpoint. They are well localized in both the spatial and frequency domains reducing the probability of disruption by occlusion clutter or noise. .