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Academic Journal of Computing & Information Science, 2021, 4(1); doi: 10.25236/AJCIS.2021.040104.

A novel and fast blurred image matching method


Zhi Huang*, Yaran Yang

Corresponding Author:
Zhi Huang

The Department of Image Processing, Suzhou North America High School, SuZhou, 215101,China


In this paper, a novel image matching method is proposed in order to improve the performance of image registration, especially for blur images. Firstly, A set of Scale Invariant Feature Transform (SIFT) points are extracted. Secondly, in order to further improve the distinctiveness of the SIFT descriptors, three scale invariant concentric circular regions are applied to produce descriptors. Thirdly, for the purpose of decreasing the high dimensional and complexity of SIFT descriptors, The Local Preserving Projection (LPP) technic is applied to reduce the dimensions of the descriptors. Lastly, the Euclidean distance similarity measurements are used to obtain the results of matching feature points. The experimental results show that the novel image matching method can not only reduce the data amounts, but also improve the matching speed and the matching precision.


Image Match, SIFT descriptors, LPP, Blur images

Cite This Paper

Zhi Huang, Yaran Yang. A novel and fast blurred image matching method. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 1: 20-26. https://doi.org/10.25236/AJCIS.2021.040104.


[1] Szeliski, R., Image alignment and stitching: A tutorial. Foundationsand Trends in Computer Graphics and Vision 2 (1), 1–104(2006).

[2] Brown, M., Lowe, D. G., Automatic panoramic image stitching using invariant features. International Journal of Computer Vision 74 (1), 59–73(2007).

[3] Ferrari, V., Tuytelaars, T., Van Gool, L., Simultaneous object recognition and segmentation by image exploration. In: Computer Vision-ECCV 2004. Springer, pp. 40–54(2004).

[4] Lowe, D. G., Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60 (2), 91–110(2004).

[5] Schaffalitzky, F., Zisserman, A., Multi-view matching for unordered image sets, . In: Computer VisionECCV 2002. Springer, pp. 414–431(2002).

[6] Tuytelaars, T., Van Gool, L., Content-based image retrieval based on local affinely invariant regions. In: Visual Information and Information Systems. Springer, pp. 656–656(1999).

[7] Kratochvil, B., Dong, L., Zhang, L., Nelson, B., Image-based 3d reconstruction using helical nanobelts for localized rotations. Journal of Microscopy 237 (2), 122–135(2010).

[8] Dorko, G., Schmid, C., Selection of scale-invariant parts for object class recognition. In: Computer Vision, Proceedings. Ninth IEEE International Conference on. IEEE, pp. 634–639(2003).

[9] Fergus, R., Perona, P., Zisserman, A., Object class recognition by unsupervised scale-invariant learning. In: Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on. Vol. 2. IEEE, pp. II–264(2003).

[10] Leibe, B., Schiele, B., Interleaving object categorization and segmentation. Springer( 2006).

[11] Opelt, A., Fussenegger, M., Pinz, A., Auer, P., Weak hypothesesand boosting for generic object detection and recognition. In: Computer Vision-ECCV 2004. Springer, pp. 71–84(2004)

[12] Lowe, D. G., Object recognition from local scale-invariant features. In: Computer Vision, 1999. The Proceedings of the Seventh IEEE International

Conference on. Vol. 2. Ieee, pp. 1150–1157(1999).

[13] Morel, J.-M., Yu, G., Asift: A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences 2 (2), 438–469(2009).

[14] Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L. V., A comparison of affine region detectors. International Journal of Computer Vision 65 (1), 43–72(2005).

[15] Mikolajczyk, K., Schmid, C., A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on 27 (10), 1615–1630(2005).

[16] Freeman, W. T., Adelson, E. H., The design and use of steerable filters. IEEE Transactions on Pattern analysis and machine intelligence 13 (9), 891–906(1991).

[17] Koenderink, J. J., van Doorn, A. J.,Representation of local geometry in the visual system. Biological cybernetics 55 (6), 367–375(1987).

[18] Van Gool, L., Moons, T., Ungureanu, D., Affine/photometric invariants for planar intensity patterns. In: Computer VisionECCV’96. Springer, pp.642–651(1996).

[19] Harris, C., Stephens, M., A combined corner and edge detector. In: Alvey vision conference. Vol. 15. Manchester, UK, pp. 50–80(1988).

[20] Mikolajczyk, K., Schmid, C., Indexing based on scale invariant interest points. In: Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on. Vol. 1. IEEE, pp. 525–531(2001).

[21] Ke, Y., Sukthankar, R., Pca-sift: A more distinctive representation for local image descriptors. In: Computer Vision and Pattern Recognition, on. Vol. 2. IEEE, pp. II–506

[22] Tenenbaum, J. B., De Silva, V., Langford, J. C., A global geometric framework for nonlinear dimensionality reduction. (2000), 2319–2323.

[23] Roweis, S. T., Saul, L. K., Nonlinear dimensionality reduction by locally linear embedding. Science 290 (5500), 2323–2326(2000).

[24] Saul, L. K., Roweis, S. T., Think globally, fit locally: unsupervised learning of low dimensional manifolds. The Journal of Machine Learning Research 4, 119–155(2003).

[25] Belkin, M., Niyogi, P.,  Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15 (6), 1373–1396(2003).

[26] Brand, M., Charting a manifold. In: Advances in neural information processing systems. pp. 961–968(2002).

[27] Zha, H., Zhang, Z., Isometric embedding and continuum isomap. In:ICML. pp. 864–871(2003).

[28] Niyogi, X., Locality preserving projections. In: Neural information processing systems. Vol. 16. pp. 153–(2004).

[29] Lindeberg, T., Feature detection with automatic scale selection. International Journal of Computer Vision 30 (2), 79–116(1998).

[30] Cao P , Rui T , Zhang J L , et al. An improved SIFT matching algorithm based on locality preserving projection LPP[C]// Proceedings of the 4th International Conference on Internet Multimedia Computing and Service. ACM, (2012).