FREAK: Fast Retina Keypoint. A large number of vision applications rely on matching keypoints across images. The last decade featured an arms-race towards faster and more robust keypoints and association algorithms: Scale Invariant Feature Transform (SIFT)[17], Speed-up Robust Feature (SURF)[4], and more recently Binary Robust Invariant Scalable Keypoints (BRISK)[I6] to name a few. These days, the deployment of vision algorithms on smart phones and embedded devices with low memory and computation complexity has even upped the ante: the goal is to make descriptors faster to compute, more compact while remaining robust to scale, rotation and noise. To best address the current requirements, we propose a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Keypoint (FREAK). A cascade of binary strings is computed by efficiently comparing image intensities over a retinal sampling pattern. Our experiments show that FREAKs are in general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK. They are thus competitive alternatives to existing keypoints in particular for embedded applications.

References in zbMATH (referenced in 13 articles )

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  1. Ma, Jiayi; Jiang, Xingyu; Fan, Aoxiang; Jiang, Junjun; Yan, Junchi: Image matching from handcrafted to deep features: a survey (2021)
  2. Kumar, Mohit; Chatterjee, Sromona; Zhang, Weiping; Yang, Jingzhi; Kolbe, Lutz M.: Fuzzy theoretic model based analysis of image features (2019)
  3. Desolneux, A.; Leclaire, A.: Stochastic image models from SIFT-like descriptors (2018)
  4. Le, Anh Vu; Won, Chee Sun: Key-point based stereo matching and its application to interpolations (2017)
  5. Foi, Alessandro; Boracchi, Giacomo: Foveated nonlocal self-similarity (2016)
  6. Wilkowski, Artur; Kornuta, Tomasz; Stefańczyk, Maciej; Kasprzak, Włodzimierz: Efficient generation of 3D surfel maps using RGB-D sensors (2016)
  7. Farhan, Erez; Hagege, Rami: Geometric expansion for local feature analysis and matching (2015)
  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. Yang, Lian; Lu, Zhangping: A new scheme for keypoint detection and description (2015)
  11. Qu, Xiujie; Zhao, Fei; Zhou, Mengzhe; Huo, Haili: A novel fast and robust binary affine invariant descriptor for image matching (2014)
  12. Zhang, Yun; Tian, Tian; Tian, Jinwen; Gong, Junbin; Ming, Delie: A novel biologically inspired local feature descriptor (2014) ioport
  13. Mainali, Pradip; Lafruit, Gauthier; Yang, Qiong; Geelen, Bert; Van Gool, Luc; Lauwereins, Rudy: SIFER: scale-invariant feature detector with error resilience (2013)