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. Tie, Yun; Guan, Ling: Automatic face detection in video sequences using local normalization and optimal adaptive correlation techniques (2009)
  2. Törnqvist, David; Schön, Thomas B.; Karlsson, Rickard; Gustafsson, Fredrik: Particle filter SLAM with high dimensional vehicle model (2009)
  3. Veelaert, Peter; Teelen, Kristof: Adaptive and optimal difference operators in image processing (2009)
  4. Wang, Yong; Mei, Tao; Gong, Shaogang; Hua, Xian-Sheng: Combining global, regional and contextual features for automatic image annotation (2009)
  5. Wong, Alexander; Fieguth, Paul: Fast phase-based registration of multimodal image data (2009)
  6. Xiao, Bai; Hancock, Edwin R.; Wilson, Richard C.: Graph characteristics from the heat kernel trace (2009)
  7. Xu, Yong; Ji, Hui; Fermüller, Cornelia: Viewpoint invariant texture description using fractal analysis (2009) ioport
  8. Yoshioka, Michifumi; Maeda, Yasuo; Omatu, Sigeru: Criterion for optimal image resolution using SIFT (2009) ioport
  9. Zagrouba, Ezzeddine; Barhoumi, Walid; Amri, Slim: An efficient image-mosaicing method based on multifeature matching (2009) ioport
  10. Zhang, Guofeng; Hua, Wei; Qin, Xueying; Shao, Yuanlong; Bao, Hujun: Video stabilization based on a 3D perspective camera model (2009) ioport
  11. Zhang, Weize; Tong, Ruofeng; Dong, Jinxiang: Boosted cascade of scattered rectangle features for object detection (2009)
  12. Ziou, Djemel; Hamri, Touati; Boutemedjet, Sabri: A hybrid probabilistic framework for content-based image retrieval with feature weighting (2009)
  13. Agarwal, Ankur; Triggs, Bill: Multilevel image coding with hyperfeatures (2008) ioport
  14. Ahn, Sunghwan; Choi, Jinwoo; Doh, Nakju Lett; Chung, Wan Kyun: A practical approach for EKF-SLAM in an indoor environment: Fusing ultrasonic sensors and stereo camera. (2008) ioport
  15. Balmashnova, E.; Florack, L. M. J.: Novel similarity measures for differential invariant descriptors for generic object retrieval (2008)
  16. Banno, A.; Masuda, T.; Oishi, T.; Ikeuchi, K.: Flying laser range sensor for large-scale site-modeling and its applications in bayon digital archival project (2008) ioport
  17. Carbonetto, Peter; Dorkó, Gyuri; Schmid, Cordelia; Kück, Hendrik; De Freitas, Nando: Learning to recognize objects with little supervision (2008) ioport
  18. Chin, Tat-Jun; You, Yilun; Coutrix, Celine; Lim, Joo-Hwee; Chevallet, Jean-Pierre; Nigay, Laurence: Mobile phone-based mixed reality: The snap2play game (2008) ioport
  19. Choi, Young-Ho; Lee, Tae-Kyeong; Oh, Se-Young: A line feature based SLAM with low grade range sensors using geometric constraints and active exploration for mobile robot. (2008) ioport
  20. Cornelis, Nico; Leibe, Bastian; Cornelis, Kurt; van Gool, Luc: 3D urban scene modeling integrating recognition and reconstruction (2008) ioport

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