SIFT

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.


References in zbMATH (referenced in 580 articles )

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  1. Shao, Ling; Kadir, Timor; Brady, Michael: Geometric and photometric invariant distinctive regions detection (2007) ioport
  2. Sridharan, Mohan; Stone, Peter: Structure-based color learning on a mobile robot under changing illumination. (2007) ioport
  3. Yao, Jian; Cham, Wai-Kuen: Robust multi-view feature matching from multiple unordered views (2007)
  4. Zhang, J.; Marszałek, M.; Lazebnik, S.; Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study (2007) ioport
  5. Caspi, Yaron; Simakov, Denis; Irani, Michal: Feature-based sequence-to-sequence matching (2006) ioport
  6. Ferrari, Vittorio; Tuytelaars, Tinne; Van Gool, Luc: Simultaneous object recognition and segmentation from single or multiple model views (2006) ioport
  7. Hörster, Eva; Lienhart, Rainer: Calibrating and optimizing poses of visual sensors in distributed platforms (2006) ioport
  8. Li, Maohai; Hong, Bingrong; Luo, Ronghua; Wei, Zhenhua: A novel method for mobile robot simultaneous localization and mapping (2006)
  9. Lobay, Anthony; Forsyth, D. A.: Shape from texture without boundaries (2006) ioport
  10. Musé, Pablo; Sur, Frédéric; Cao, Frédéric; Gousseau, Yann; Morel, Jean-Michel: An a contrario decision method for shape element recognition (2006) ioport
  11. Rothganger, Fred; Lazebnik, Svetlana; Schmid, Cordelia; Ponce, Jean: 3D object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints (2006) ioport
  12. Vergauwen, Maarten; Van Gool, Luc: Web-based 3D reconstruction service (2006) ioport
  13. Walther, Dirk; Koch, Christof: Modeling attention to salient proto-objects (2006)
  14. Zhang, Hanling; Liu, Jie: Robust image watermarking using local invariant features and independent component analysis (2006)
  15. Corso, Jason J.; Ye, Guangqi; Hager, Gregory D.: A touch/force display system for haptic interface (2005) ioport
  16. Kim, Sungho; Jang, Gijeong; Kweon, In So: An effective 3D target recognition model imitating robust methods of the human visual system (2005) ioport
  17. Kim, Sungho; Kweon, In So: Automatic model-based 3D object recognition by combining feature matching with tracking (2005) ioport
  18. Mikolajczyk, K.; Tuytelaars, T.; Schmid, C.; Zisserman, A.; Matas, J.; Schaffalitzky, F.; Kadir, T.; Van Gool, L.: A comparison of affine region detectors (2005) ioport
  19. Tu, Zhuowen; Chen, Xiangrong; Yuille, Alan L.; Zhu, Song-Chun: Image parsing: unifying segmentation, detection, and recognition (2005) ioport
  20. Lowe, David G.: Distinctive image features from scale-invariant keypoints (2004) ioport

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