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. Abdollahifard, Mohammad Javad; Mariéthoz, Grégoire; Ghavim, Maryam: Quantitative evaluation of multiple-point simulations using image segmentation and texture descriptors (2019)
  2. Barajas-García, Carolina; Solorza-Calderón, Selene; Gutiérrez-López, Everardo: Scale, translation and rotation invariant wavelet local feature descriptor (2019)
  3. Chen, Mingjia; Zou, Qianfang; Wang, Changbo; Liu, Ligang: EdgeNet: deep metric learning for 3D shapes (2019)
  4. Chen, Pei-Yin; Huang, Jih-Jeng: A hybrid autoencoder network for unsupervised image clustering (2019)
  5. Chi, Jianning; Yu, Xiaosheng; Zhang, Yifei; Wang, Huan: A novel local human visual perceptual texture description with key feature selection for texture classification (2019)
  6. Eliades, Charalambos; Lenc, Ladislav; Král, Pavel; Papadopoulos, Harris: Automatic face recognition with well-calibrated confidence measures (2019)
  7. Evangelopoulos, Xenophon; Brockmeier, Austin J.; Mu, Tingting; Goulermas, John Y.: Continuation methods for approximate large scale object sequencing (2019)
  8. Farhan, Erez: Highly accurate matching of weakly localized features (2019)
  9. 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)
  10. 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)
  11. Kumar, Mohit; Chatterjee, Sromona; Zhang, Weiping; Yang, Jingzhi; Kolbe, Lutz M.: Fuzzy theoretic model based analysis of image features (2019)
  12. Lenc, Karel; Vedaldi, Andrea: Understanding image representations by measuring their equivariance and equivalence (2019)
  13. Liu, Xi; Ma, Zhengming; Niu, Guo: Mixed region covariance discriminative learning for image classification on Riemannian manifolds (2019)
  14. 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)
  15. Ma, Jiayi; Zhao, Ji; Jiang, Junjun; Zhou, Huabing; Guo, Xiaojie: Locality preserving matching (2019)
  16. Maver, Jasna; Skočaj, Danijel: EL: local image descriptor based on extreme responses to partial derivatives of 2D Gaussian function (2019)
  17. 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)
  18. Rajan, Purnima; Ma, Yongming; Jedynak, Bruno: Cox processes for counting by detection (2019)
  19. Savchenko, A. V.: Sequential three-way decisions in multi-category image recognition with deep features based on distance factor (2019)
  20. Shi, Buhai; Zhang, Qingming; Xu, Haibo: A geometrical-information-assisted approach for local feature matching (2019)

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