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. Farhan, Erez: Highly accurate matching of weakly localized features (2019)
  2. Horng, Ming-Huwi; Kuok, Chan-Pang; Fu, Min-Jun; Lin, Chii-Jen; Sun, Yung-Nien: Cobb angle measurement of spine from X-ray images using convolutional neural network (2019)
  3. 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)
  4. Liu, Xi; Ma, Zhengming; Niu, Guo: Mixed region covariance discriminative learning for image classification on Riemannian manifolds (2019)
  5. Lu, Yanfeng; Jia, Lihao; Qiao, Hong; Li, Yi; Qi, Zongshuai: Enhanced biologically inspired model for image recognition based on a novel patch selection method with moment (2019)
  6. Maver, Jasna; Skočaj, Danijel: EL: local image descriptor based on extreme responses to partial derivatives of 2D Gaussian function (2019)
  7. Pham-Toan, D.; Vo-Van, T.; Pham-Chau, A. T.; Nguyen-Trang, T.; Ho-Kieu, D.: A new binary adaptive elitist differential evolution based automatic (k)-medoids clustering for probability density functions (2019)
  8. Rajan, Purnima; Ma, Yongming; Jedynak, Bruno: Cox processes for counting by detection (2019)
  9. Shi, Buhai; Zhang, Qingming; Xu, Haibo: A geometrical-information-assisted approach for local feature matching (2019)
  10. Vitoria, Patricia; Ballester, Coloma: Automatic flare spot artifact detection and removal in photographs (2019)
  11. Xue, Shan; Zhu, Hong: Low-resolution and open-set face recognition via recursive label propagation based on statistical classification (2019)
  12. Zhang, Zhengwu; Klassen, Eric; Srivastava, Anuj: Robust comparison of kernel densities on spherical domains (2019)
  13. Baier, Daniel; Frost, Sarah: Relating brand confusion to ad similarities and brand strengths through image data analysis and classification (2018)
  14. Deep, G.; Kaur, L.; Gupta, S.: Biomedical image retrieval using microscopic configuration with local structural information (2018)
  15. Desolneux, A.; Leclaire, A.: Stochastic image models from SIFT-like descriptors (2018)
  16. Ding, Yanyun; Xiao, Yunhai: Symmetric Gauss-Seidel technique-based alternating direction methods of multipliers for transform invariant low-rank textures problem (2018)
  17. Fan, Jicong; Tian, Zhaoyang; Zhao, Mingbo; Chow, Tommy W. S.: Accelerated low-rank representation for subspace clustering and semi-supervised classification on large-scale data (2018)
  18. Fawzi, Alhussein; Fawzi, Omar; Frossard, Pascal: Analysis of classifiers’ robustness to adversarial perturbations (2018)
  19. Jansson, Ylva; Lindeberg, Tony: Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields (2018)
  20. Jindal, Himanshu; Kasana, Singara Singh; Saxena, Sharad: Underwater pipelines panoramic image transmission and refinement using acoustic sensors (2018)

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