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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  3. Lindeberg, Tony: Provably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascade (2020)
  4. Perez-Gonzalez, Jorge; Arámbula Cosío, Fernando; Huegel, Joel C.; Medina-Bañuelos, Verónica: Probabilistic learning coherent point drift for 3D ultrasound fetal head registration (2020)
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  6. Wu, Min; Wicker, Matthew; Ruan, Wenjie; Huang, Xiaowei; Kwiatkowska, Marta: A game-based approximate verification of deep neural networks with provable guarantees (2020)
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  8. Abdollahifard, Mohammad Javad; Mariéthoz, Grégoire; Ghavim, Maryam: Quantitative evaluation of multiple-point simulations using image segmentation and texture descriptors (2019)
  9. Barajas-García, Carolina; Solorza-Calderón, Selene; Gutiérrez-López, Everardo: Scale, translation and rotation invariant wavelet local feature descriptor (2019)
  10. Chen, Mingjia; Zou, Qianfang; Wang, Changbo; Liu, Ligang: EdgeNet: deep metric learning for 3D shapes (2019)
  11. Eliades, Charalambos; Lenc, Ladislav; Král, Pavel; Papadopoulos, Harris: Automatic face recognition with well-calibrated confidence measures (2019)
  12. Evangelopoulos, Xenophon; Brockmeier, Austin J.; Mu, Tingting; Goulermas, John Y.: Continuation methods for approximate large scale object sequencing (2019)
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  14. Jeong, Chiyoon; Yang, Hyun S.; Moon, KyeongDeok: A novel approach for detecting the horizon using a convolutional neural network and multi-scale edge detection (2019)
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  18. Xue, Shan; Zhu, Hong: Low-resolution and open-set face recognition via recursive label propagation based on statistical classification (2019)
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  20. Baier, Daniel; Frost, Sarah: Relating brand confusion to ad similarities and brand strengths through image data analysis and classification (2018)

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