Anti-fuzzy local feature descriptor on images

被引：0

作者：

Tang G. ^{[1
]}

机构：

[1] School of Computer Science and Technology, Xidian Univ., Xi'an

来源：

Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2019年 / 46卷 / 01期

关键词：

Feature descriptor; Feature point matching; Image matching; Object recognition;

D O I：

10.19665/j.issn1001-2400.2019.01.007

中图分类号：

学科分类号：

摘要：

The SIFT descriptor is only partially invariant to illumination when extracting the local features of the image. In particular, the SIFT descriptor is not invariant to non-linear illumination changes and cannot accurately extract the feature points or few of them can be extracted from the fuzzy object image. In order to solve these problems, a new anti-fuzzy local feature descriptor is proposed that is consistent with the visual cognition process of the human visual system from bottom-top and top-down. Experimental results suggest that the proposed operator is robust to the changes of illumination conditions, and more feature points can be extracted accurately from the fuzzy object image. The proposed operator retains the advantages of SIFT descriptors such as invariance of scaling, rotation and compression, and can significantly improve the matching rate on fuzzy images. © 2019, The Editorial Board of Journal of Xidian University. All right reserved.

引用

页码：39 / 45

页数：6

共 17 条

[1]

Lowe D.G., Distinctive Image Features from Scale-invariant Keypoints, International Journal of Computer Vision, 60, 2, pp. 91-110, (2004)

[2]

Ma J., Zhou H., Zhao J., Et al., Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming, IEEE Transactions on Geoscience and Remote Sensing, 53, 12, pp. 6469-6481, (2015)

[3]

Li L., Liu Z., Noise-robust Multi-feature Joint Learning HRRP Recognition Method, Journal of Xidian University, 45, 4, pp. 57-62, (2018)

[4]

Xu X., Zhao Y., Multimodal Face Recognition for Profile Views Based on SIFT and LBP, Lecture Notes in Computer Science: 8912, pp. 20-30, (2015)

[5]

Chien H.J., Chuang C.C., Chen C.Y., Et al., When to Use What Feature? SIFT, SURF, ORB, or A-KAZE Features for Monocular Visual Odometry, Proceedings of the 2016 International Conference on Image and Vision Computing, (2017)

[6]

Li C., Wang J., Ji H., Et al., CPHD Multi-target Tracking Algorithm with Unknown Model Parameters, Journal of Xidian University, 44, 2, pp. 37-41, (2017)

[7]

Tareen S.A.K., Saleem Z., A Comparative Analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK, Proceedings of the 2018 International Conference on Computing, Mathematics and Engineering Technologies: Invent, Innovate and Integrate for Socioeconomic Developmen, pp. 1-10, (2018)

[8]

Lindeberg T., Scale Invariant Feature Transform, Scholarpedia, 7, 5, (2012)

[9]

Kabbai L., Azaza A., Abdellaoui M., Et al., Image Matching Based on LBP and SIFT Descriptor, Proceedings of the 201512th International Multi-Conference on Systems, Signals and Devices, (2015)

[10]

Bai S., Hou J., Ohnishi N., Scene Categorization Through Combining LBP and SIFT Features Effectively, International Journal of Pattern Recognition and Artificial Intelligence, 30, 1, (2016)

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