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 506 articles )

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  1. Deep, G.; Kaur, L.; Gupta, S.: Biomedical image retrieval using microscopic configuration with local structural information (2018)
  2. Ding, Yanyun; Xiao, Yunhai: Symmetric Gauss-Seidel technique-based alternating direction methods of multipliers for transform invariant low-rank textures problem (2018)
  3. Fawzi, Alhussein; Fawzi, Omar; Frossard, Pascal: Analysis of classifiers’ robustness to adversarial perturbations (2018)
  4. Jansson, Ylva; Lindeberg, Tony: Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields (2018)
  5. Jindal, Himanshu; Kasana, Singara Singh; Saxena, Sharad: Underwater pipelines panoramic image transmission and refinement using acoustic sensors (2018)
  6. Lindeberg, Tony: Dense scale selection over space, time, and space-time (2018)
  7. Lindeberg, Tony: Spatio-temporal scale selection in video data (2018)
  8. Muggleton, Stephen; Dai, Wang-Zhou; Sammut, Claude; Tamaddoni-Nezhad, Alireza; Wen, Jing; Zhou, Zhi-Hua: Meta-interpretive learning from noisy images (2018)
  9. Öfjäll, Kristoffer; Felsberg, Michael: Approximative coding methods for channel representations (2018)
  10. Sánchez, Javier; Morel, Jean-Michel: Motion smoothing strategies for 2D video stabilization (2018)
  11. Wäldchen, Jana; Mäder, Patrick: Plant species identification using computer vision techniques: a systematic literature review (2018)
  12. Wang, Yiding; Zheng, Xuan: Cross-device hand vein recognition based on improved SIFT (2018)
  13. Yu, Hancheng; Qin, Haibao; Peng, Maoting: A fast approach to texture-less object detection based on orientation compressing map and discriminative regional weight (2018)
  14. Zhang, Yu; Yuan, Ye; Guo, Fangda; Wang, Yishu; Wang, Guoren: Improving the multimodal probabilistic semantic model by ELM classifiers (2018)
  15. Boufounos, Petros T.; Rane, Shantanu; Mansour, Hassan: Representation and coding of signal geometry (2017)
  16. Brown, Peter; Yang, Yuedong; Zhou, Yaoqi; Pullan, Wayne: A heuristic for the time constrained asymmetric linear sum assignment problem (2017)
  17. Fehri, Amin; Velasco-Forero, Santiago; Meyer, Fernand: Prior-based hierarchical segmentation highlighting structures of interest (2017)
  18. Hosseini, Mahdi S.; Plataniotis, Konstantinos N.: Finite differences in forward and inverse imaging problems: maxpol design (2017)
  19. Le, Anh Vu; Won, Chee Sun: Key-point based stereo matching and its application to interpolations (2017)
  20. Li, Zheng; Liu, Yiguang; Li, Jipeng; Xu, Wenzheng: The mapping-adaptive convolution: a fundamental theory for homography or perspective invariant matching methods (2017)

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