Abstract
A large number of vision applications rely on matching key points across images, its main problem is to find a fast and robust key point descriptor and a matching strategy. This paper presents a two-step matching strategy based on voting and an improved binary descriptor CS-FREAK by adding the neighborhood intensity information of the sampling points to the FREAK descriptor. This method divides the matching task into two steps, firstly simplify the FREAK[1] 8-layer retina model to a 5-layer one and construct a binary descriptor, secondly encode the neighborhood intensity information of the center symmetry sampling points, and then create a 16-dimentional histogram according to a pre-constructed index table, which is the basis for voting strategy. This two-step matching strategy can improve learning efficiency meanwhile enhance the descriptor identification ability, and improve the matching accuracy. Experimental results show that the accuracy of the matching method is superior to SIFT and FREAK.
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References
Alahi, A., Ortiz, R., Vandergheynst, P.: Freak: Fast retina keypoint. In: Computer Vision and Pattern Recognition (CVPR), pp. 510–517. IEEE Press, New York (2012)
Lin, D., Xiliang, N., Tao, J., Shunshi, H.: Automatic registration of CCD images and IR images based on invariant feature. In: Infrared and Laser Engineering, vol. 40, pp. 350–354 (2011)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. In: International Journal of Computer Vision, vol. 60, pp. 91–110. Kluwer Academic Publishers (2004)
Xingmiao, L., Shicheng, W., Jing, Z.: Infrared image moving object detection based on image block reconstruction. In: Infrared and Laser Engineering, vol. 1, pp. 176–180 (2011)
Liu, J., Zhang, T.: Recognition of the blurred image by complex moment invariants. In: Pattern Recognition Letters, vol. 26, pp. 1128–1138 (2005)
Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation for local image descriptors. In: Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 506–513. IEEE Press, Washington, DC (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1615–1630 (2005)
Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Calonder, M., Lepetit, V., Strecha, C., Fua, P.: Brief: Binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)
Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: Binary robust invariant scalable keypoints. In: IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555. IEEE Press, Barcelona (2011)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: An efficient alternative to SIFT or SURF. In: IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571. IEEE Press, Barcelona (2011)
Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)
Wang, Z., Fan, B., Wu, F.: Local intensity order pattern for feature description. In: IEEE International Conference on Computer Vision (ICCV), pp. 603–610. IEEE Press, Barcelona
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Wang, J., Wang, X., Yang, X., Zhao, A. (2014). CS-FREAK: An Improved Binary Descriptor. In: Tan, T., Ruan, Q., Wang, S., Ma, H., Huang, K. (eds) Advances in Image and Graphics Technologies. IGTA 2014. Communications in Computer and Information Science, vol 437. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45498-5_15
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DOI: https://doi.org/10.1007/978-3-662-45498-5_15
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