ASIFT

A fully affine invariant image comparison method, Affine-SIFT (ASIFT) is introduced. While SIFT is fully invariant with respect to only four parameters namely zoom, rotation and translation, the new method treats the two left over parameters : the angles defining the camera axis orientation. Against any prognosis, simulating all views depending on these two parameters is feasible. The method permits to reliably identify features that have undergone very large affine distortions measured by a new parameter, the transition tilt. State-of-the-art methods hardly exceed transition tilts of 2 (SIFT), 2.5 (Harris-Affine and Hessian-Affine) and 10 (MSER). ASIFT can handle transition tilts up 36 and higher.


References in zbMATH (referenced in 32 articles , 1 standard article )

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  1. O’Neill, Riley C. W.; Angulo-Umaña, Pedro; Calder, Jeff; Hessburg, Bo; Olver, Peter J.; Shakiban, Chehrzad; Yezzi-Woodley, Katrina: Computation of circular area and spherical volume invariants via boundary integrals (2020)
  2. Yang, Yun; Yu, Yanhua: Moving frames and differential invariants on fully affine planar curves (2020)
  3. Desolneux, A.; Leclaire, A.: Stochastic image models from SIFT-like descriptors (2018)
  4. Ding, Yanyun; Xiao, Yunhai: Symmetric Gauss-Seidel technique-based alternating direction methods of multipliers for transform invariant low-rank textures problem (2018)
  5. Rodríguez, Mariano; Delon, Julie; Morel, Jean-Michel: Covering the space of tilts. Application to affine invariant image comparison (2018)
  6. Sur, Frédéric; Blaysat, Benoît; Grédiac, Michel: Rendering deformed speckle images with a Boolean model (2018)
  7. Bohi, Amine; Prandi, Dario; Guis, Vincente; Bouchara, Frédéric; Gauthier, Jean-Paul: Fourier descriptors based on the structure of the human primary visual cortex with applications to object recognition (2017)
  8. Korman, Simon; Reichman, Daniel; Tsur, Gilad; Avidan, Shai: Fast-Match: fast affine template matching (2017)
  9. Li, Zheng; Liu, Yiguang; Li, Jipeng; Xu, Wenzheng: The mapping-adaptive convolution: a fundamental theory for homography or perspective invariant matching methods (2017)
  10. Alvarez, Luis; Cuenca, Carmelo; Esclarín, Julio; Mazorra, Luis; Morel, Jean-Michel: Affine invariant distance using multiscale analysis (2016)
  11. Becker, Florian; Petra, Stefania; Schnörr, Christoph: Optical flow (2015)
  12. Farhan, Erez; Hagege, Rami: Geometric expansion for local feature analysis and matching (2015)
  13. Fedorov, Vadim; Arias, Pablo; Sadek, Rida; Facciolo, Gabriele; Ballester, Coloma: Linear multiscale analysis of similarities between images on Riemannian manifolds: practical formula and affine covariant metrics (2015)
  14. Raviv, Dan; Kimmel, Ron: Affine invariant geometry for non-rigid shapes (2015)
  15. Raviv, Dan; Raskar, Ramesh: Scale invariant metrics of volumetric datasets (2015)
  16. El Mir, Ghina; Saint-Jean, Christophe; Berthier, Michel: Conformal geometry for viewpoint change representation (2014)
  17. Mishkin, Dmytro; Matas, Jiří: Matching of images of non-planar objects with view synthesis (2014)
  18. Qu, Xiujie; Zhao, Fei; Zhou, Mengzhe; Huo, Haili: A novel fast and robust binary affine invariant descriptor for image matching (2014)
  19. Raviv, Dan; Bronstein, Alexander M.; Bronstein, Michael M.; Waisman, Dan; Sochen, Nir; Kimmel, Ron: Equi-affine invariant geometry for shape analysis (2014)
  20. Tepper, Mariano; Musé, Pablo; Almansa, Andrés: On the role of contrast and regularity in perceptual boundary saliency (2014)

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