COMPARISON OF BRIEF AND ORB BINARY DESCRIPTORS

M. P. Mukhina, Yu. V. Trach, A. P. Prymak

Abstract


A great deal of features detectors and descriptors are proposed nowadays for various computer vision applications. The task of image processing which is invariant to all weather conditions (such as rain, fog, smoke, unfavorable lights, camera rotation) is presented. The influence of weather conditions on the number of determined key points in the image is analyzed, and how certain unfavorable conditions influence the tracking of these points from frame to frame. Two binary descriptors are considered for finding special points of the image; BRIEF (Binary robust independent elementary features) and ORB (Oriented FAST and rotated BRIEF) descriptors, as well as their modifications, to explore the most efficient descriptor which can be used in applications that run in real time. The result of the study shows that the descriptors have high stability characteristics, working with different types of images and rotation angles, using the recommendations for the use of their modifications.


Keywords


Object recognition; binary descriptors, interest points, key points; descriptors, detectors

References


D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, 60(2): pp. 91–100, 2004.

H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: Speeded up robust features,” in Proceedings of the 9th European Conference on Computer Vision, 2006.

Michael Calonder, et al. “BRIEF: Binary robust independent elementary features,” in Computer Vision – ECCV 2010. Springer Berlin Heidelberg, 2010, pp. 778–792.

M. Ozuysal, M. Calonder, V. Lepetit, and P. Fua, “Fast Keypoint Recognition Using Random Ferns,” IEEE Transactions on PatternAnalysis and Machine Intelligence, vol. 32, no. 3, pp. 448–461, 2010.

K. Mikolajczyk and C. Schmid, “A Performance Evaluation of Local Descriptors,” IEEE Transactions on Pattern Analysis and MachineIntelligence, vol. 27, no. 10, pp. 1615–1630, 2004.

A. Alahi, R. Ortiz & P. Vandergheynst, “Freak: Fast retina keypoint,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, June 2012, pp. 510–517.

R. Ethan, R. Vincent, K. Kurt, K. Gary, “ORB: an efficient alternative to SIFT or SURF,” 2011 IEEE International Conference on Computer Vision (ICCV 2011), vol. 1, pp. 2564–2571, 2011. DOI Bookmark:10.1109/ICCV.2011.6126544.

David G. Lowe, “Object recognition from local scale-invariant features,” Proceedings of the International Conference on Computer Vision, 1999, pp. 1150–1157.

Herbert Bay, Tinne Tuytelaars, and Luc Van Gool, “SURF: Speeded Up Robust Features,” Proceedings of the ninth European Conference on Computer Vision, 2006, pp. 404–417.


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