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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  2. Bassetti, Federico; Gualandi, Stefano; Veneroni, Marco: On the computation of Kantorovich-Wasserstein distances between two-dimensional histograms by uncapacitated minimum cost flows (2020)
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  4. Daghyani, Masoud; Zamzami, Nuha; Bouguila, Nizar: Toward an efficient computation of log-likelihood functions in statistical inference: overdispersed count data clustering (2020)
  5. Díaz Martínez, Diego H.; Mémoli, Facundo; Mio, Washington: The shape of data and probability measures (2020)
  6. Escudero-Viñolo, Marcos; Bescos, Jesus: Squeezing the DCT to fight camouflage (2020)
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  8. Maanicshah, Kamal; Azam, Muhammad; Nguyen, Hieu; Bouguila, Nizar; Fan, Wentao: Finite inverted beta-Liouville mixture models with variational component splitting (2020)
  9. Nishiyama, Yu; Kanagawa, Motonobu; Gretton, Arthur; Fukumizu, Kenji: Model-based kernel sum rule: kernel Bayesian inference with probabilistic models (2020)
  10. Park, Soyoung; Carriquiry, Alicia: An algorithm to compare two-dimensional footwear outsole images using maximum cliques and speeded-up robust feature (2020)
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  12. Qin, Zixuan; Yin, Mengxiao; Li, Guiqing; Yang, Feng: SP-Flow: self-supervised optical flow correspondence point prediction for real-time SLAM (2020)
  13. Teng, Hao; Lu, Huijuan; Ye, Minchao; Yan, Ke; Gao, Zhigang; Jin, Qun: Applying of adaptive threshold non-maximum suppression to pneumonia detection (2020)
  14. Waniek, Nicolai: Transition scale-spaces: a computational theory for the discretized entorhinal cortex (2020)
  15. Wen, Zhi-Jie; Liu, Zhi-Hu; Zong, Yi-Chen; Li, Bao-Jun: Latent local feature extraction for low-resolution virus image classification (2020)
  16. Wu, Min; Wicker, Matthew; Ruan, Wenjie; Huang, Xiaowei; Kwiatkowska, Marta: A game-based approximate verification of deep neural networks with provable guarantees (2020)
  17. Yin, Shuai; Yuschenko, A. S.: Object recognition of the robotic system with using a parallel convolutional neural network (2020)
  18. Zamzami, Nuha; Bouguila, Nizar: Deriving probabilistic SVM kernels from exponential family approximations to multivariate distributions for count data (2020)
  19. Zhou, Tian; Li, Yunyi; Gui, Guan: Noise learning based discriminative dictionary learning algorithm for image classification (2020)
  20. Zhuge, Wenzhang; Hou, Chenping; Peng, Shaoliang; Yi, Dongyun: Joint consensus and diversity for multi-view semi-supervised classification (2020)

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