SIFT Keypoint Detector. 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 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.

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  5. Collier, Olivier; Dalalyan, Arnak S.: Minimax rates in permutation estimation for feature matching (2016)
  6. Guo, Kanghui; Labate, Demetrio: Characterization and analysis of edges in piecewise smooth functions (2016)
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  10. Porikli, Fatih: Regression on Lie groups and its application to affine motion tracking (2016)
  11. Tartavel, Guillaume; Peyré, Gabriel; Gousseau, Yann: Wasserstein loss for image synthesis and restoration (2016)
  12. Wen, Jia; Wang, Xue-ping; Kong, Ling-fu; Zhang, Shi-hui: Using weighted part model for pedestrian detection in crowded scenes based on image segmentation (2016)
  13. Becker, Florian; Petra, Stefania; Schnörr, Christoph: Optical flow (2015)
  14. Bourrier, Anthony; Perronnin, Florent; Gribonval, Rémi; Pérez, Patrick; Jégou, Hervé: Explicit embeddings for nearest neighbor search with Mercer kernels (2015)
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  18. Do, Thanh-Nghi; Poulet, François: Parallel multiclass logistic regression for classifying large scale image datasets (2015)
  19. Farhan, Erez; Hagege, Rami: Geometric expansion for local feature analysis and matching (2015)
  20. Fedorov, Vadim; Arias, Pablo; Sadek, Rida; Facciolo, Gabriele; Ballester, Coloma: Linear multiscale analysis of similarities between images on Riemannian manifolds: practical formula and affine covariant metrics (2015)

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