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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  1. Jindal, Himanshu; Kasana, Singara Singh; Saxena, Sharad: Underwater pipelines panoramic image transmission and refinement using acoustic sensors (2018)
  2. Lindeberg, Tony: Spatio-temporal scale selection in video data (2018)
  3. Wang, Yiding; Zheng, Xuan: Cross-device hand vein recognition based on improved SIFT (2018)
  4. Boufounos, Petros T.; Rane, Shantanu; Mansour, Hassan: Representation and coding of signal geometry (2017)
  5. Brown, Peter; Yang, Yuedong; Zhou, Yaoqi; Pullan, Wayne: A heuristic for the time constrained asymmetric linear sum assignment problem (2017)
  6. Fehri, Amin; Velasco-Forero, Santiago; Meyer, Fernand: Prior-based hierarchical segmentation highlighting structures of interest (2017)
  7. Mendes Júnior, Pedro R.; de Souza, Roberto M.; de O. Werneck, Rafael; Stein, Bernardo V.; Pazinato, Daniel V.; de Almeida, Waldir R.; Penatti, Otávio A. B.; da S. Torres, Ricardo; Rocha, Anderson: Nearest neighbors distance ratio open-set classifier (2017)
  8. Ngo, Phuc; Kenmochi, Yukiko; Sugimoto, Akihiro; Talbot, Hugues; Passat, Nicolas: Discrete rigid registration: a local graph-search approach (2017)
  9. Özyeşil, Onur; Voroninski, Vladislav; Basri, Ronen; Singer, Amit: A survey of structure from motion (2017)
  10. Szemenyei, Marton; Vajda, Ferenc: Dimension reduction for objects composed of vector sets (2017)
  11. Alvarez, Luis; Cuenca, Carmelo; Esclarín, Julio; Mazorra, Luis; Morel, Jean-Michel: Affine invariant distance using multiscale analysis (2016)
  12. Anand, Saket; Mittal, Sushil; Meer, Peter: Robust estimation for computer vision using Grassmann manifolds (2016)
  13. Ansari, Zafar Ahmed; Harit, Gaurav: Nearest neighbour classification of Indian sign language gestures using kinect camera (2016) ioport
  14. Collier, Olivier; Dalalyan, Arnak S.: Minimax rates in permutation estimation for feature matching (2016)
  15. Galdámez, Pedro Luis; González Arrieta, Angélica; Ramón Ramón, Miguel: A small look at the ear recognition process using a hybrid approach (2016)
  16. Guo, Kanghui; Labate, Demetrio: Characterization and analysis of edges in piecewise smooth functions (2016)
  17. Hussain, Khaled F.; Moussa, Ghada S.: On-road vehicle classification based on random neural network and bag-of-visual words (2016)
  18. Kanwal, Nadia; Bostanci, Erkan; Clark, Adrian F.: Evaluation method, dataset size or dataset content: how to evaluate algorithms for image matching? (2016) ioport
  19. Klein, Shmuel T.; Shapira, Dana: Compressed matching for feature vectors (2016)
  20. Kraft, Marek; Nowicki, Michał; Penne, Rudi; Schmidt, Adam; Skrzypczyński, Piotr: Efficient RGB-D data processing for feature-based self-localization of mobile robots (2016)

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