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 440 articles )

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  1. Brown, Peter; Yang, Yuedong; Zhou, Yaoqi; Pullan, Wayne: A heuristic for the time constrained asymmetric linear sum assignment problem (2017)
  2. Alvarez, Luis; Cuenca, Carmelo; Esclarín, Julio; Mazorra, Luis; Morel, Jean-Michel: Affine invariant distance using multiscale analysis (2016)
  3. Anand, Saket; Mittal, Sushil; Meer, Peter: Robust estimation for computer vision using Grassmann manifolds (2016)
  4. Ansari, Zafar Ahmed; Harit, Gaurav: Nearest neighbour classification of Indian sign language gestures using kinect camera (2016) ioport
  5. Collier, Olivier; Dalalyan, Arnak S.: Minimax rates in permutation estimation for feature matching (2016)
  6. Galdámez, Pedro Luis; González Arrieta, Angélica; Ramón Ramón, Miguel: A small look at the ear recognition process using a hybrid approach (2016)
  7. Guo, Kanghui; Labate, Demetrio: Characterization and analysis of edges in piecewise smooth functions (2016)
  8. Kanwal, Nadia; Bostanci, Erkan; Clark, Adrian F.: Evaluation method, dataset size or dataset content: how to evaluate algorithms for image matching? (2016) ioport
  9. Klein, Shmuel T.; Shapira, Dana: Compressed matching for feature vectors (2016)
  10. Li, L.; Liu, W.; Wang, C.; Liang, A.: Robust and flexible landmarks detection for uncontrolled frontal faces in the wild (2016) ioport
  11. Osuna-González, G.; Carbajal-Espinosa, O.; Loukianov, A.; Bayro-Corrochano, E.: Geometric perception of pose and tracking (2016)
  12. Porikli, Fatih: Regression on Lie groups and its application to affine motion tracking (2016)
  13. Rey-Otero, Ives; Morel, Jean-Michel; Delbracio, Mauricio: An analysis of the factors affecting keypoint stability in scale-space (2016)
  14. Schmidt, Martin; Weickert, Joachim: Morphological counterparts of linear shift-invariant scale-spaces (2016)
  15. Tartavel, Guillaume; Peyré, Gabriel; Gousseau, Yann: Wasserstein loss for image synthesis and restoration (2016)
  16. Wen, Jia; Wang, Xue-ping; Kong, Ling-fu; Zhang, Shi-hui: Using weighted part model for pedestrian detection in crowded scenes based on image segmentation (2016) ioport
  17. Zhou, Peicheng; Cheng, Gong; Liu, Zhenbao; Bu, Shuhui; Hu, Xintao: Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping (2016) ioport
  18. Becker, Florian; Petra, Stefania; Schnörr, Christoph: Optical flow (2015)
  19. Bourrier, Anthony; Perronnin, Florent; Gribonval, Rémi; Pérez, Patrick; Jégou, Hervé: Explicit embeddings for nearest neighbor search with Mercer kernels (2015)
  20. Bronstein, Alexander M.; Bronstein, Michael M.: Manifold intrinsic similarity (2015)

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