BRISK

BRISK: Binary robust invariant scalable keypoints. Effective and efficient generation of keypoints from an image is a well-studied problem in the literature and forms the basis of numerous Computer Vision applications. Established leaders in the field are the SIFT and SURF algorithms which exhibit great performance under a variety of image transformations, with SURF in particular considered as the most computationally efficient amongst the high-performance methods to date. In this paper we propose BRISK, a novel method for keypoint detection, description and matching. A comprehensive evaluation on benchmark datasets reveals BRISK’s adaptive, high quality performance as in state-of-the-art algorithms, albeit at a dramatically lower computational cost (an order of magnitude faster than SURF in cases). The key to speed lies in the application of a novel scale-space FAST-based detector in combination with the assembly of a bit-string descriptor from intensity comparisons retrieved by dedicated sampling of each keypoint neighborhood.


References in zbMATH (referenced in 15 articles )

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  1. Desolneux, A.; Leclaire, A.: Stochastic image models from SIFT-like descriptors (2018)
  2. Rodríguez, Mariano; Delon, Julie; Morel, Jean-Michel: Covering the space of tilts. Application to affine invariant image comparison (2018)
  3. Wang, Chengyou; Zhang, Zhi; Zhou, Xiao: An image copy-move forgery detection scheme based on A-KAZE and SURF features (2018)
  4. Hassannejad, Hamid; Matrella, Guido; Ciampolini, Paolo; de Munari, Ilaria; Mordonini, Monica; Cagnoni, Stefano: A new approach to image-based estimation of food volume (2017)
  5. Le, Anh Vu; Won, Chee Sun: Key-point based stereo matching and its application to interpolations (2017)
  6. Rey-Otero, Ives; Morel, Jean-Michel; Delbracio, Mauricio: An analysis of the factors affecting keypoint stability in scale-space (2016)
  7. Wilkowski, Artur; Kornuta, Tomasz; Stefańczyk, Maciej; Kasprzak, Włodzimierz: Efficient generation of 3D surfel maps using RGB-D sensors (2016)
  8. Kapela, Rafal; Gugala, Karol; Sniatala, Pawel; Swietlicka, Aleksandra; Kolanowski, Krzysztof: Embedded platform for local image descriptor based object detection (2015)
  9. Mondéjar-Guerra, V. M.; Muñoz-Salinas, R.; Marín-Jiménez, M. J.; Carmona-Poyato, A.; Medina-Carnicer, R.: Keypoint descriptor fusion with Dempster-Shafer theory (2015)
  10. Rey-Otero, Ives; Delbracio, Mauricio: Is repeatability an unbiased criterion for ranking feature detectors? (2015)
  11. Yang, Lian; Lu, Zhangping: A new scheme for keypoint detection and description (2015)
  12. Qu, Xiujie; Zhao, Fei; Zhou, Mengzhe; Huo, Haili: A novel fast and robust binary affine invariant descriptor for image matching (2014)
  13. Zagoris, Konstantinos; Pratikakis, Ioannis; Antonacopoulos, Apostolos; Gatos, Basilis; Papamarkos, Nikos: Distinction between handwritten and machine-printed text based on the bag of visual words model (2014) ioport
  14. Zhang, Yun; Tian, Tian; Tian, Jinwen; Gong, Junbin; Ming, Delie: A novel biologically inspired local feature descriptor (2014) ioport
  15. Xiao, Ming; Pan, Liang; Hu, Tianjiang; Shen, Lincheng: A novel fusion scheme for vision aided inertial navigation of aerial vehicles (2013)