Vlfeat: an open and portable library of computer vision algorithms. VLFeat is an open and portable library of computer vision algorithms. It aims at facilitating fast prototyping and reproducible research for computer vision scientists and students. It includes rigorous implementations of common building blocks such as feature detectors, feature extractors, (hierarchical) k-means clustering, randomized kd-tree matching, and super-pixelization. The source code and interfaces are fully documented. The library integrates directly with MATLAB, a popular language for computer vision research.

References in zbMATH (referenced in 46 articles )

Showing results 1 to 20 of 46.
Sorted by year (citations)

1 2 3 next

  1. Aravkin, Aleksandr; Davis, Damek: Trimmed statistical estimation via variance reduction (2020)
  2. Boyd, Zachary M.; Porter, Mason A.; Bertozzi, Andrea L.: Stochastic block models are a discrete surface tension (2020)
  3. Chin, Tat-Jun; Cai, Zhipeng; Neumann, Frank: Robust fitting in computer vision: easy or hard? (2020)
  4. Pritts, James; Kukelova, Zuzana; Larsson, Viktor; Lochman, Yaroslava; Chum, Ondřej: Minimal solvers for rectifying from radially-distorted scales and change of scales (2020)
  5. Edgar Riba, Dmytro Mishkin, Daniel Ponsa, Ethan Rublee, Gary Bradski: Kornia: an Open Source Differentiable Computer Vision Library for PyTorch (2019) arXiv
  6. Farhan, Erez: Highly accurate matching of weakly localized features (2019)
  7. Lenc, Karel; Vedaldi, Andrea: Understanding image representations by measuring their equivariance and equivalence (2019)
  8. Ma, Jiayi; Zhao, Ji; Jiang, Junjun; Zhou, Huabing; Guo, Xiaojie: Locality preserving matching (2019)
  9. Tariq, Humera; Samreen, Asia; Amjad, Usman: Haze removal using improved automatic quick shift segmentation (2019)
  10. Tremblay, Nicolas; Barthelmé, Simon; Amblard, Pierre-Olivier: Determinantal point processes for coresets (2019)
  11. Ahmad, Shahzor; Cheong, Loong-Fah: Robust detection and affine rectification of planar homogeneous texture for scene understanding (2018)
  12. Boyd, Zachary M.; Bae, Egil; Tai, Xue-Cheng; Bertozzi, Andrea L.: Simplified energy landscape for modularity using total variation (2018)
  13. Desolneux, A.; Leclaire, A.: Stochastic image models from SIFT-like descriptors (2018)
  14. Keriven, Nicolas; Bourrier, Anthony; Gribonval, Rémi; Pérez, Patrick: Sketching for large-scale learning of mixture models (2018)
  15. Liu, Chongwen; Shang, Zhaowei; Lin, Bo; Tang, Yuan Yan: A semantic tree method for image classification and video action recognition (2018)
  16. Muggleton, Stephen; Dai, Wang-Zhou; Sammut, Claude; Tamaddoni-Nezhad, Alireza; Wen, Jing; Zhou, Zhi-Hua: Meta-interpretive learning from noisy images (2018)
  17. Xie, Yuan; Tao, Dacheng; Zhang, Wensheng; Liu, Yan; Zhang, Lei; Qu, Yanyun: On unifying multi-view self-representations for clustering by tensor multi-rank minimization (2018)
  18. Yin, Ke; Tai, Xue-Cheng: An effective region force for some variational models for learning and clustering (2018)
  19. Zhu, Wei; Wang, Bao; Barnard, Richard; Hauck, Cory D.; Jenko, Frank; Osher, Stanley: Scientific data interpolation with low dimensional manifold model (2018)
  20. Boufounos, Petros T.; Rane, Shantanu; Mansour, Hassan: Representation and coding of signal geometry (2017)

1 2 3 next