BRIEF: computing a local binary descriptor very fast. Binary descriptors are becoming increasingly popular as a means to compare feature points very fast while requiring comparatively small amounts of memory. The typical approach to creating them is to first compute floating-point ones, using an algorithm such as SIFT, and then to binarize them. In this paper, we show that we can directly compute a binary descriptor, which we call BRIEF, on the basis of simple intensity difference tests. As a result, BRIEF is very fast both to build and to match. We compare it against SURF and SIFT on standard benchmarks and show that it yields comparable recognition accuracy, while running in an almost vanishing fraction of the time required by either.
Keywords for this software
References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Maver, Jasna; Skočaj, Danijel: EL: local image descriptor based on extreme responses to partial derivatives of 2D Gaussian function (2019)
- Demetz, Oliver; Hafner, David; Weickert, Joachim: Morphologically invariant matching of structures with the complete rank transform (2015)
- Kapela, Rafal; Gugala, Karol; Sniatala, Pawel; Swietlicka, Aleksandra; Kolanowski, Krzysztof: Embedded platform for local image descriptor based object detection (2015)
- Lindeberg, Tony: Image matching using generalized scale-space interest points (2015)
- Mou, Wei; Wang, Han; Seet, Gerald: Robust homography estimation based on nonlinear least squares optimization (2014)
- 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