Abstract
Keypoint generation, including detection, description and matching is the basis of a broad range of applications. A more efficient and effective keypoint generation method is always of interest. In this paper, we propose the speed-up BRISK (SBRISK), a variant of the binary robust invariant scalable keypoint (BRISK). SBRISK not only inherits the high speed of BRISK in the keypoint detection, but also adopts a nearly circular symmetric constellation to describe the pattern of keypoint. To adapt to the characteristic orientation of keypoint, SBRISK shifts the binary vector rather than rotating the image pattern or constellation like many other descriptors have done. It abandons interpolation to get intensity at sub-pixel position, since the constellation does not strictly restrict to circular symmetric. Different from BRISK, SBRISK classifies keypoints into bright patterns and dark patterns. Comparison is conducted only within the same class. Meanwhile, a special refinement scheme is imposed upon the initial matching results to improve the match precision. Experiments show that SBRISK has a faster and better performance than BRISK with less memory consumption.
Similar content being viewed by others
References
Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Found. Trends Comput. Graph. Vis. 3(3), 177–280 (2008)
Harris, C., Stephens M.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–151 (1988)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vision 61(1), 63–86 (2004)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)
Yeo, C., Ahammad, P., Ramchandran, K.: Coding of image feature descriptors for distributed rate-efficient visual correspondences. Int. J. Comput. Vision 94(3), 267–281 (2011)
Strecha, C., Bronstein, A.M., Bronstein, M.M., Fua, P.: LDAHash: improved matching with smaller descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 34(1), 66–78 (2012)
Rosten, E.,Drummond T.: Machine learning for high-speed corner detection. In: European Conference on Computer Vision, pp. 430–443 (2006)
Smith, S.M., Brady, J.M.: SUSAN—a new approach to low level image processing. Int. J. Comput. Vis 23(1), 45–78 (1997)
Mair, E., Hager G. D., Burschka D., Suppa M., Hirzinger G.: Adaptive and generic corner detection based on the accelerated segment test. In European Conference on Computer Vision, pp. 183–196 (2010)
Rublee, E., Rabaud V., Konolige K., Bradski G.: ORB: An efficient alternative to SIFT or SURF. In: IEEE International Conference on Computer Vision, pp. 2564–2571(2011)
Leutenegger, S., Chli M., Siegwart R.Y.: BRISK: Binary robust invariant scalable keypoints. In: IEEE International Conference on Computer Vision, pp. 2548–2555 (2011)
Calonder, M., Lepetit V., Strecha C., Fua P.: BRIEF: binary robust independent elementary features. In: European Conference on Computer Vision, pp. 778–792, Heraklion, Crete, Greece (2010)
Tola, E., Lepetit, V., Fua, P.: DAISY: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 815–830 (2010)
Heinly, J., Dunn E., Frahm J.-M.: Comparative evaluation of binary features. In: European Conference on Computer Vision, pp. 759–773 (2012)
Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., Fua, P.: BRIEF: computing a local binary descriptor very fast. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1281–1298 (2012)
Acknowledgments
This paper is supported by NSF, No.61303067.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yang, S., Li, B. & Zeng, K. SBRISK: speed-up binary robust invariant scalable keypoints. J Real-Time Image Proc 12, 583–591 (2016). https://doi.org/10.1007/s11554-014-0434-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11554-014-0434-x