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An advanced gradient texture feature descriptor based on phase information for infrared and visible image matching

  • 1134T: Multi-Source and Heterogeneous Multimedia Analytics
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Abstract

Infrared and visible image matching has many applications in remote sensing, computer vision, military fields, etc. The differences in the many characteristics of infrared images and visible images make a robust feature description vital but difficult. Texture orientation information retains the general properties in refrared and visible images, and multi-scale, and multi-oriented Gabor filters can accurately reveal the texture orientation information. This paper presents a feature descriptor by capturing the phase information between neighboring pixels with Log-Gabor filters. Firstly, the original matching image is enhanced via histogram equalization to emphasize the regions of interest, and the gradient magnitude for each pixel is computed to extract the image profile, which advances the performance of the algorithm significantly. Secondly, multi-scale and multi-oriented Log–Gabor filters are utilized to obtain the angle information for different scales and phases in the neighboring region of each pixel, and the angle information is indexed by computing the maximum of energy, including the magnitude, real part, and imaginary part to generate the marked image in which the histograms of the subregion of the detected keypoints are employed to generate the feature descriptors. Finally, we advocate five evaluation measures for testing the performance of the algorithm. The proposed approach is evaluated with four data sets composed of images obtained in visible light and infrared spectra, and its performance is compared with the performance of the state-of-the-art algorithms: Scale-invariant feature transform(SIFT), Speeded up robust features(SURF), Oriented fast and rotated BRIEF(ORB), the edge-oriented histogram descriptor (EHD), the phase congruency edge-oriented histogram discriptor (PCEHD), and the Log–Gabor histogram descriptor (LGHD). The experimental results indicate that the performance of the proposed approach is higher than that of other state-of-the-art algorithms.

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Acknowledgements

This work was supported by the National Science Foundation of China under Grant No. 61671170, 61872085, and 51875138, Science and Technology Foundation of National Defense Key Laboratory of Science and Technology on Parallel and Distributed Processing Laboratory (PDL) under Grant No.6142110180406, Science and Technology Foundation of ATR National Defense Key Laboratory under Grant No. 6142503180402, China Academy of Space Technology(CAST). Innovation Fund under Grant No.2018CAST33, Joint Fund of China Electronics Technology Group Corporation and Equipment Pre-Research under Grant No.6141B08231109, Excellent Discipline Team project no.JDXKTD-2019008. Basic Scientific Research Business Expenses of Provincial Universities in Heilongjiang Province No.2019-KYYWF-1384.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, 150001, People’s Republic of China

    Xiaomin Liu & Jun-Bao Li

  2. Information and Electronic Technology Institute, Jiamusi University, Jiamusi, 154002, People’s Republic of China

    Xiaomin Liu

  3. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266510, China

    Jeng-Shyang Pan

  4. Fujian Provincial Key Laboratory of Big Data Minning and Applications, Fujian University of Technology, Fuzhou, 350118, China

    Jeng-Shyang Pan

  5. Department of Information Management, Chaoyang University of Technology, Taichung City, Taiwan

    Jeng-Shyang Pan

  6. School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, 150001, People’s Republic of China

    Shuo Wang

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  1. Xiaomin Liu
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Correspondence to Jun-Bao Li.

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Liu, X., Li, JB., Pan, JS. et al. An advanced gradient texture feature descriptor based on phase information for infrared and visible image matching. Multimed Tools Appl 80, 16491–16511 (2021). https://doi.org/10.1007/s11042-020-10213-z

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