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 39 articles )

Showing results 1 to 20 of 39.
Sorted by year (citations)

1 2 next

  1. Aravkin, Aleksandr; Davis, Damek: Trimmed statistical estimation via variance reduction (2020)
  2. Edgar Riba, Dmytro Mishkin, Daniel Ponsa, Ethan Rublee, Gary Bradski: Kornia: an Open Source Differentiable Computer Vision Library for PyTorch (2019) arXiv
  3. Farhan, Erez: Highly accurate matching of weakly localized features (2019)
  4. Tariq, Humera; Samreen, Asia; Amjad, Usman: Haze removal using improved automatic quick shift segmentation (2019)
  5. Tremblay, Nicolas; Barthelmé, Simon; Amblard, Pierre-Olivier: Determinantal point processes for coresets (2019)
  6. Boyd, Zachary M.; Bae, Egil; Tai, Xue-Cheng; Bertozzi, Andrea L.: Simplified energy landscape for modularity using total variation (2018)
  7. Desolneux, A.; Leclaire, A.: Stochastic image models from SIFT-like descriptors (2018)
  8. Keriven, Nicolas; Bourrier, Anthony; Gribonval, Rémi; Pérez, Patrick: Sketching for large-scale learning of mixture models (2018)
  9. Liu, Chongwen; Shang, Zhaowei; Lin, Bo; Tang, Yuan Yan: A semantic tree method for image classification and video action recognition (2018)
  10. Muggleton, Stephen; Dai, Wang-Zhou; Sammut, Claude; Tamaddoni-Nezhad, Alireza; Wen, Jing; Zhou, Zhi-Hua: Meta-interpretive learning from noisy images (2018)
  11. Yin, Ke; Tai, Xue-Cheng: An effective region force for some variational models for learning and clustering (2018)
  12. Zhu, Wei; Wang, Bao; Barnard, Richard; Hauck, Cory D.; Jenko, Frank; Osher, Stanley: Scientific data interpolation with low dimensional manifold model (2018)
  13. Boufounos, Petros T.; Rane, Shantanu; Mansour, Hassan: Representation and coding of signal geometry (2017)
  14. Tron, Roberto; Daniilidis, Kostas: The space of essential matrices as a Riemannian quotient manifold (2017)
  15. de Amorim, Renato Cordeiro: A survey on feature weighting based K-means algorithms (2016)
  16. Joutsijoki, Henry; Haponen, Markus; Rasku, Jyrki; Aalto-Setälä, Katriina; Juhola, Martti: Machine learning approach to automated quality identification of human induced pluripotent stem cell colony images (2016)
  17. Ramírez-Corona, Mallinali; Sucar, L. Enrique; Morales, Eduardo F.: Hierarchical multilabel classification based on path evaluation (2016)
  18. Rey-Otero, Ives; Morel, Jean-Michel; Delbracio, Mauricio: An analysis of the factors affecting keypoint stability in scale-space (2016)
  19. Zeppelzauer, Matthias; Zieliński, Bartosz; Juda, Mateusz; Seidl, Markus: Topological descriptors for 3D surface analysis (2016)
  20. Zheng, Jiangbin; Liu, Yanan; Ren, Jinchang; Zhu, Tingge; Yan, Yijun; Yang, Heng: Fusion of block and keypoints based approaches for effective copy-move image forgery detection (2016)

1 2 next