SURF: Speeded Up Robust Features. In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF’s strong performance.

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  1. Álvarez-Miranda, Eduardo; Díaz-Guerrero, John: Multicriteria saliency detection: a (exact) robust network design approach (2020)
  2. Lindeberg, Tony: Provably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascade (2020)
  3. Maanicshah, Kamal; Azam, Muhammad; Nguyen, Hieu; Bouguila, Nizar; Fan, Wentao: Finite inverted beta-Liouville mixture models with variational component splitting (2020)
  4. Wu, Min; Wicker, Matthew; Ruan, Wenjie; Huang, Xiaowei; Kwiatkowska, Marta: A game-based approximate verification of deep neural networks with provable guarantees (2020)
  5. Barajas-García, Carolina; Solorza-Calderón, Selene; Gutiérrez-López, Everardo: Scale, translation and rotation invariant wavelet local feature descriptor (2019)
  6. Farhan, Erez: Highly accurate matching of weakly localized features (2019)
  7. Maver, Jasna; Skočaj, Danijel: EL: local image descriptor based on extreme responses to partial derivatives of 2D Gaussian function (2019)
  8. Shi, Buhai; Zhang, Qingming; Xu, Haibo: A geometrical-information-assisted approach for local feature matching (2019)
  9. Baier, Daniel; Frost, Sarah: Relating brand confusion to ad similarities and brand strengths through image data analysis and classification (2018)
  10. Desolneux, A.; Leclaire, A.: Stochastic image models from SIFT-like descriptors (2018)
  11. Jacobson, Adam; Chen, Zetao; Milford, Michael: Leveraging variable sensor spatial acuity with a homogeneous, multi-scale place recognition framework (2018)
  12. Kleinschmidt, Sebastian P.; Wagner, Bernardo: Spatial fusion of different imaging technologies using a virtual multimodal camera (2018)
  13. Lindeberg, Tony: Dense scale selection over space, time, and space-time (2018)
  14. Lindeberg, Tony: Spatio-temporal scale selection in video data (2018)
  15. Li, Xin; Belianinov, Alex; Dyck, Ondrej; Jesse, Stephen; Park, Chiwoo: Two-level structural sparsity regularization for identifying lattices and defects in noisy images (2018)
  16. Rodríguez, Mariano; Delon, Julie; Morel, Jean-Michel: Covering the space of tilts. Application to affine invariant image comparison (2018)
  17. Sánchez, Javier; Morel, Jean-Michel: Motion smoothing strategies for 2D video stabilization (2018)
  18. Savvopoulos, Alkiviadis; Kanavos, Andreas; Mylonas, Phivos; Sioutas, Spyros: LSTM accelerator for convolutional object identification (2018)
  19. Tsai, Chi-Yi; Hsu, Kuang-Jui; Nisar, Humaira: Efficient model-based object pose estimation based on multi-template tracking and PnP algorithms (2018)
  20. Vasilakakis, Michael D.; Iakovidis, Dimitris K.; Spyrou, Evaggelos; Koulaouzidis, Anastasios: DINOSARC: color features based on selective aggregation of chromatic image components for wireless capsule endoscopy (2018)

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