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 596 articles )

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  1. Azam, Muhammad; Alghabashi, Basim; Bouguila, Nizar: Multivariate bounded asymmetric Gaussian mixture model (2020)
  2. 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)
  3. Cholin, Ajaykumar S.; Vinay, A.; Bhat, Aditya D.; Deshpande, Arnav Ajay; Murthy, K. N. B.; Natarajan, S.: PSI ((\psi)) invariant features for face recognition (2020)
  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)
  7. Lindeberg, Tony: Provably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascade (2020)
  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. Perez-Gonzalez, Jorge; Arámbula Cosío, Fernando; Huegel, Joel C.; Medina-Bañuelos, Verónica: Probabilistic learning coherent point drift for 3D ultrasound fetal head registration (2020)
  11. Teng, Hao; Lu, Huijuan; Ye, Minchao; Yan, Ke; Gao, Zhigang; Jin, Qun: Applying of adaptive threshold non-maximum suppression to pneumonia detection (2020)
  12. Wen, Zhi-Jie; Liu, Zhi-Hu; Zong, Yi-Chen; Li, Bao-Jun: Latent local feature extraction for low-resolution virus image classification (2020)
  13. Wu, Min; Wicker, Matthew; Ruan, Wenjie; Huang, Xiaowei; Kwiatkowska, Marta: A game-based approximate verification of deep neural networks with provable guarantees (2020)
  14. Zamzami, Nuha; Bouguila, Nizar: Deriving probabilistic SVM kernels from exponential family approximations to multivariate distributions for count data (2020)
  15. Zhou, Tian; Li, Yunyi; Gui, Guan: Noise learning based discriminative dictionary learning algorithm for image classification (2020)
  16. Zhuge, Wenzhang; Hou, Chenping; Peng, Shaoliang; Yi, Dongyun: Joint consensus and diversity for multi-view semi-supervised classification (2020)
  17. Abdollahifard, Mohammad Javad; Mariéthoz, Grégoire; Ghavim, Maryam: Quantitative evaluation of multiple-point simulations using image segmentation and texture descriptors (2019)
  18. Barajas-García, Carolina; Solorza-Calderón, Selene; Gutiérrez-López, Everardo: Scale, translation and rotation invariant wavelet local feature descriptor (2019)
  19. Chen, Mingjia; Zou, Qianfang; Wang, Changbo; Liu, Ligang: EdgeNet: deep metric learning for 3D shapes (2019)
  20. Chi, Jianning; Yu, Xiaosheng; Zhang, Yifei; Wang, Huan: A novel local human visual perceptual texture description with key feature selection for texture classification (2019)

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