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. Ahmed, Mirza Tahir; Ziauddin, Sheikh; Marshall, Joshua A.; Greenspan, Michael: Point cloud registration using virtual interest points from Macaulay’s resultant of quadric surfaces (2021)
  2. Furini, Fabio; Ljubić, Ivana; San Segundo, Pablo; Zhao, Yanlu: A branch-and-cut algorithm for the edge interdiction clique problem (2021)
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  5. Azam, Muhammad; Alghabashi, Basim; Bouguila, Nizar: Multivariate bounded asymmetric Gaussian mixture model (2020)
  6. Bassetti, Federico; Gualandi, Stefano; Veneroni, Marco: On the computation of Kantorovich-Wasserstein distances between two-dimensional histograms by uncapacitated minimum cost flows (2020)
  7. Cheng, Yawen; Yin, Hang; Ye, Qiaolin; Huang, Peng; Fu, Liyong; Yang, Zhangjing; Tian, Yuan: Improved multi-view GEPSVM via inter-view difference maximization and intra-view agreement minimization (2020)
  8. Daghyani, Masoud; Zamzami, Nuha; Bouguila, Nizar: Toward an efficient computation of log-likelihood functions in statistical inference: overdispersed count data clustering (2020)
  9. Díaz Martínez, Diego H.; Mémoli, Facundo; Mio, Washington: The shape of data and probability measures (2020)
  10. Escudero-Viñolo, Marcos; Bescos, Jesus: Squeezing the DCT to fight camouflage (2020)
  11. Lindeberg, Tony: Provably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascade (2020)
  12. Maanicshah, Kamal; Azam, Muhammad; Nguyen, Hieu; Bouguila, Nizar; Fan, Wentao: Finite inverted beta-Liouville mixture models with variational component splitting (2020)
  13. Nishiyama, Yu; Kanagawa, Motonobu; Gretton, Arthur; Fukumizu, Kenji: Model-based kernel sum rule: kernel Bayesian inference with probabilistic models (2020)
  14. Park, Soyoung; Carriquiry, Alicia: An algorithm to compare two-dimensional footwear outsole images using maximum cliques and speeded-up robust feature (2020)
  15. Perez-Gonzalez, Jorge; Arámbula Cosío, Fernando; Huegel, Joel C.; Medina-Bañuelos, Verónica: Probabilistic learning coherent point drift for 3D ultrasound fetal head registration (2020)
  16. Qin, Zixuan; Yin, Mengxiao; Li, Guiqing; Yang, Feng: SP-Flow: self-supervised optical flow correspondence point prediction for real-time SLAM (2020)
  17. Tang, Jiali; Wang, Yan; Huang, Chenrong; Liu, Huangxiaolie; Al-Nabhan, Najla: Image edge detection based on singular value feature vector and gradient operator (2020)
  18. Teng, Hao; Lu, Huijuan; Ye, Minchao; Yan, Ke; Gao, Zhigang; Jin, Qun: Applying of adaptive threshold non-maximum suppression to pneumonia detection (2020)
  19. Waniek, Nicolai: Transition scale-spaces: a computational theory for the discretized entorhinal cortex (2020)
  20. Wen, Zhi-Jie; Liu, Zhi-Hu; Zong, Yi-Chen; Li, Bao-Jun: Latent local feature extraction for low-resolution virus image classification (2020)

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