Comparison of binary feature points descriptors of images under distortion conditions
Krasnabayeu E.A., Chistabayeu D.V., Malyshev A.L.

 

Vitebsk State University named after P.M. Masherov, Vitebsk, Belarus;
Design Bureau “Display”, Vitebsk, Belarus

 PDF

Abstract:
The article is devoted to the review and analysis of binary descriptors of feature points of objects in digital images under distortion conditions. An overview of the BRIEF, ORB, BRISK, FREAK, AKAZE, LATCH methods is given. The evaluation of properties of the descriptors on sample images is performed. The paper addresses problems of using these methods for real time image processing.

Keywords:
digital image processing, pattern recognition, image analysis, feature detection, feature description, feature matching

Citation:
Krasnabayeu YA, Chistabayeu DV, Malyshev AL. Comparison of binary feature points descriptors of images under distortion conditions. Computer Optics 2019; 43(3): 434-445. DOI: 10.18287/2412-6179-2019-43-3-434-445.

References:

  1. Harris C, Stephens M. A combined corner and edge detector. 4th Alvey Vision Conference 1988; 15(50): 147-151. DOI: 10.5244/c.2.23.
  2. Shi J, Tomasi C. Good features to track. IEEE Conference on Computer Vision and Pattern Recognition 1994; 593-600. DOI: 10.1109/CVPR.1994.323794.
  3. Smith S, Brady J. SUSAN – a new approach to low level image processing. International Journal of Computer Vision 1997; 23(1): 45-78. DOI: 10.1023/A:1007963824710.
  4. Lindeberg T. Feature detection with automatic scale selection. International Journal of Computer Vision 1998; 30(2): 77-116. DOI: 10.1023/A:1008045108935.
  5. Haralick R. Ridges and valleys on digital images. Computer Vision, Graphics, and Image Processing 1983; 22(1): 28–38. DOI: 10.1016/0734-189X(83)90094-4.
  6. Myasnikov VV. Model-based gradient field descriptor as a convenient tool for image recognition and analysis [In Russian]. Computer Optics 2012; 36(4): 596-604.
  7. Myasnikov VV. Description of images using model-oriented descriptors. Computer Optics 2017; 41(6): 888-896. DOI: 10.18287/2412-6179-2017-41-6-888-896.
  8. Makarov AO, Starovoitov VV. Fast algorithms for calculating traits on digital images [In Russian]. Minsk: "UIPI" Publisher; 2005.
  9. Lowe DG. Object recognition from local scale-invariant features. Proceedings of the International Conference on Computer Vision 1999; 2: 1150-1157. DOI: 10.1109/ICCV.1999.790410.
  10. Herbert B, Ess A, Tuytelaars T, Van Gool L. SURF: speeded up robust features. Computer Vision and Image Understanding (CVIU) 2008; 110: 346-359. DOI: 10.1007/11744023_32.
  11. Freeman W, Roth M. Orientation histograms for hand gesture recognition. International Workshop on Automatic Face and Gesture Recognition 1994; 296-301.
  12. Rosten E, Drummond T. Machine learning for high speed corner detection. 9th European Conference on Computer Vision 2006; 1: 430-443. DOI: 10.1007/11744023_34.
  13. Matas J, Chum O, Urban M, Pajdla T. Robust wide baseline stereo from maximally stable extremal regions. British Machine Vision Conference 2002; 22(10): 384-396. DOI: 10.1016/j.imavis.2004.02.006.
  14. Heinly J, Dunn E, Frahm JM. Comparative Evaluation of Binary Features. Computer Vision (ECCV 2012) 2012; 7573: 759-773. DOI: 10.1007/978-3-642-33709-3_54.
  15. Bekele D, Teutsch M, Schuchert T. Evaluation of binary keypoint descriptors. IEEE ICIP 2013: 3652-3656. DOI: 10.1109/ICIP.2013.6738753.
  16. Miksik O, Mikolajczyk K. Evaluation of local detectors and descriptors for fast feature matching. Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) 2012; 2681-2684.
  17. Canclini A, Cesana M, Redondi A, Tagliasacchi M, Ascenso J, Cilla R. Evaluation of low-complexity visual feature detectors and descriptors. 18th International Conference on Digital Signal Processing (DSP) 2013: 1-7. DOI: 10.1109/ICDSP.2013.6622757.
  18. Figat J, Kornuta T, Kasprzak W. Performance evaluation of binary descriptors of local features. ICCVG 2014: Computer Vision and Graphics 2014; 8671: 187-194. DOI: 10.1007/978-3-319-11331-9_23.
  19. Calonder M, Lepetit V, Strecha C, Fua P. BRIEF: binary robust independent elementary features. European Conference on Computer Vision 2010; 6314: 778-792. DOI: 10.1007/978-3-642-15561-1_56.
  20. Rublee E, Rabaud V, Konolige K, Bradski G. ORB: an efficient alternative to SIFT or SURF. IEEE International Conference on Computer Vision 2011; 58(11): 2564-2571. DOI: 10.1109/ICCV.2011.6126544.
  21. Leutenegger S, Chli M, Siegwart RY. BRISK: Binary Robust invariant scalable keypoints. Computer Vision (ICCV) IEEE International Conference 2011: 2548-2555. DOI: 10.1109/ICCV.2011.6126542.
  22. Alahi A, Ortiz R, Vandergheynst P. Freak: Fast retina keypoint. Computer Vision and Pattern Recognition (CVPR) 2012: 510-517. DOI: 10.1109/CVPR.2012.6247715.
  23. Alcantarilla P, Bartoli A, Davison A. KAZE Features. European Conference on Computer Vision 2012; 4: 214-227. DOI: 10.1007/978-3-642-33783-3_16.
  24. Alcantarilla P, Nuevo J, Bartoli A. Fast explicit diffusion for accelerated features in nonlinear scale spaces. British Machine Vision Conference 2013; 13.1–13.11. DOI: 10.5244/C.27.13.
  25. Demchev DM, Volkov VA, Khmeleva VS, Kazakov EE. Sea ice drift retrieval from sar using feature tracking [In Russian]. Problems of the Arctic and Antarctic 2016; 3(109): 5-19.
  26. Levi G, Hassner T. LATCH: Learned arrangements of three patch codes. Winter Conference on Applications of Computer Vision (WACV) 2016: 1-9. DOI: 10.1109/WACV.2016.7477723.
  27. Brown M, Hua G, Winder S. Discriminative learning of local image descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2011; 33(1): 43-57. DOI: 10.1109/TPAMI.2010.54
  28. Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, Kadir T, Van Gool L. A comparison of affine region detectors. International Journal of Computer Vision 2005; 65: 43-72. DOI: 10.1007/s11263-005-3848-x.
  29. Mikolajczyk K, Schmid C. A Performance Evaluation of Local Descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005; 27(10): 1615-1630. DOI: 10.1109/TPAMI.2005.188.
  30. Tuytelaars, T, Mikolajczyk R. Local invariant feature detectors: A survey. Foundations and Trends in Computer Graphics and Vision 2008; 3(3): 177-280. DOI: 10.1561/0600000017.
  31. Verichev AV. Traceable detector of feature points of the image [In Russian]. Collected works of III international conference and youth school "Information technologies and nanotechnology" 2017; 670-675.
  32. Kaehler A, Bradski G. Learning OpenCV 3: Computer vision in C++ with the OpenCV Library. Sebastopol, CA: O’Reilly Media; 2016.
© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846)332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20