Heterogeneous Image Matching via a Novel Feature Describing Model
Source abstract
Abstract
Computer vision has been developed greatly in the past several years, and many useful and interesting technologies have been presented and widely applied. Image matching is an important technology based on similarity measurement. In this paper, we propose a novel feature describing model based on scale space and local principle component analysis for heterogeneous image matching. The traditional uniform eight-direction statistics is updated by a task-related k-direction statistics based on prior information of the keypoints. In addition, the k directions are determined by an approximately solution of a Min-Max problem. The principle component analysis is introduced to compute the main directions of local patches based on the gradient field. In addition, the describing vector is formed by then implementing PCA on each sub-patch of a 4 × 4 mesh. Experimental results show the accuracy and efficiency of proposed method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques · Advanced Vision and Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Sciences, Southwest Petroleum University, Chengdu 610500, China
Institute of Artificial Intelligence, Southwest Petroleum University, Chengdu 610500, China
Research Center of Mathematical Mechanics, Southwest Petroleum University, Chengdu 610500, China
School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Institute of Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
Department of Software Engineering, Kaunas University of Technology, 51386 Kaunas, Lithuania
Funding
National Natural Science Foundation of China
References (47)
Show source-provided references
1. A combined corner and edge detector
2. Förstner, W., and Gülch, E. (1987, January 2–4). A fast operator for detection and precise location of distinct points, corners and centres of circular features. Proceedings of the ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, Switzerland.
3. Lowe, D.G. (1999, January 20–27). Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece.10.1109/ICCV.1999.790410 ↗
4. SUSAN—A new approach to low level image processing10.1023/A:1007963824710 ↗
5. A performance evaluation of local descriptors10.1109/TPAMI.2005.188 ↗
6. Distinctive image features from scale-invariant keypoints10.1023/B:VISI.0000029664.99615.94 ↗
7. Ke, Y., and Sukthankar, R. (July, January 27). PCA-SIFT: A more distinctive representation for local image descriptors. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA.
8. A comprehensive performance evaluation of 3D local feature descriptors10.1007/s11263-015-0824-y ↗
9. A spatial-spectral SIFT for hyperspectral image matching and classification10.1016/j.patrec.2018.08.032 ↗
10. Robust wide-baseline stereo from maximally stable extremal regions10.1016/j.imavis.2004.02.006 ↗
11. A comparison of affine region detectors10.1007/s11263-005-3848-x ↗
12. Fast Affine Invariant Image Matching10.5201/ipol.2018.225 ↗
13. Moravec, H.P. (1977, January 22–25). Towards automatic visual bbstacle avoidance. Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, CA, USA.
14. Image registration methods: a survey10.1016/S0262-8856(03)00137-9 ↗
15. Sift flow: Dense correspondence across scenes and its applications10.1109/TPAMI.2010.147 ↗
16. Dense correspondences across scenes and scales10.1109/TPAMI.2015.2474356 ↗
17. Region-based hierarchical image matching10.1007/s11263-007-0077-5 ↗
18. Bay, H., Tuytelaars, T., and Van Gool, L. (2006). Surf: Speeded up robust features. European Conference on Computer Vision, Graz, Austria, 7–13 May 2006, Springer.10.1007/11744023_32 ↗
19. Shi, J. (1994, January 21–23). Good features to track. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.
20. Rosten, E., and Drummond, T. (2006). Machine learning for high-speed corner detection. European Conference on Computer Vision, Graz, Austria, 7–13 May 2006, Springer.10.1007/11744023_34 ↗
21. Mair, E., Hager, G.D., Burschka, D., Suppa, M., and Hirzinger, G. (2010). Adaptive and generic corner detection based on the accelerated segment test. European Conference on Computer Vision, Springer.10.1007/978-3-642-15552-9_14 ↗
22. Alcantarilla, P.F., Bartoli, A., and Davison, A.J. (2012). KAZE features. European Conference on Computer Vision, Springer.10.1007/978-3-642-33783-3_16 ↗
23. Emergence of simple-cell receptive field properties by learning a sparse code for natural images10.1038/381607a0 ↗
24. Yang, J., Yu, K., Gong, Y., and Huang, T. (2009, January 20–25). Linear spatial pyramid matching using sparse coding for image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.
25. Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition10.1016/j.cviu.2014.02.013 ↗
26. Mixed Integer Programming for Sparse Coding: Application to Image Denoising10.1109/TCI.2019.2896790 ↗
27. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks10.1109/TGRS.2016.2584107 ↗
28. Research on feature extraction of tumor image based on convolutional neural network10.1109/ACCESS.2019.2897131 ↗
29. Multiple Feature Aggregation Using Convolutional Neural Networks for SAR Image-Based Automatic Target Recognition10.1109/LGRS.2018.2865608 ↗
30. Adaptive estimation of active contour parameters using convolutional neural networks and texture analysis10.1109/TMI.2016.2628084 ↗
31. Leutenegger, S., Chli, M., and Siegwart, R. (2011, January 6–13). BRISK: Binary robust invariant scalable keypoints. Proceedings of the 2011 IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain.10.1109/ICCV.2011.6126542 ↗
32. Robust image matching based on the information of SIFT10.1016/j.ijleo.2018.06.094 ↗
33. Optimization of SIFT algorithm for fast-image feature extraction in line-scanning ophthalmoscope10.1016/j.ijleo.2017.09.075 ↗
34. A Zernike moment phase-based descriptor for local image representation and matching10.1109/TIP.2009.2032890 ↗
35. Klare, B., and Jain, A.K. (2010, January 23–26). Heterogeneous Face Recognition: Matching NIR to Visible Light Images. Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.10.1109/ICPR.2010.374 ↗
36. A flexible heterogeneous real-time digital image correlation system10.1016/j.optlaseng.2018.05.010 ↗
37. Visual feature coding based on heterogeneous structure fusion for image classification10.1016/j.inffus.2016.12.010 ↗
38. Heterogeneous image transformation10.1016/j.patrec.2012.04.005 ↗
39. Graphical representation for heterogeneous face recognition10.1109/TPAMI.2016.2542816 ↗
40. Witkin, A.P. (1987). Scale-space filtering. Readings in Computer Vision, Elsevier.10.1016/B978-0-08-051581-6.50036-2 ↗
41. Combined energy minimization for image reconstruction from few views10.1155/2012/154630 ↗
42. A regional adaptive variational PDE model for computed tomography image reconstruction10.1016/j.patcog.2019.03.009 ↗
43. Level set evolution for boundary extraction based on a p-Laplace equation10.1016/j.apm.2010.04.003 ↗
44. Jolliffe, I. (2011). Principal Component Analysis, Springer.10.1007/978-3-642-04898-2_455 ↗
45. Fergus, R., Perona, P., and Zisserman, A. (2003). Object class recognition by unsupervised scale-invariant learning. CVPR (2), Madison, WI, USA, 16–22 June 2003, IEEE.10.1109/CVPR.2003.1211479 ↗
46. Heinly, J., Dunn, E., and Frahm, J.M. (2012). Comparative evaluation of binary features. European Conference on Computer Vision, Springer.10.1007/978-3-642-33709-3_54 ↗
47. Scale & affine invariant interest point detectors10.1023/B:VISI.0000027790.02288.f2 ↗