Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images

Xiaomin Liu; Jun-Bao Li; Jeng-Shyang Pan

doi:10.3390/s19194244

Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images

Sensors (Basel). 2019 Sep 29;19(19):4244. doi: 10.3390/s19194244.

Authors

Xiaomin Liu^{1

2}, Jun-Bao Li³, Jeng-Shyang Pan^{4

5

6}

Affiliations

¹ School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China. 17b901029@stu.hit.edu.cn.
² Information and Electronic Technology Institute, Jiamusi University, Jiamusi 154002, China. 17b901029@stu.hit.edu.cn.
³ School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China. lijunbao@hit.edu.cn.
⁴ College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, China. jengshyangpan@fjut.edu.cn.
⁵ Fujian Provincial Key Laboratory of Big Data Minning and Applications, Fujian University of Technology, Fuzhou 350118, China. jengshyangpan@fjut.edu.cn.
⁶ College of Informatics, Chaoyang University of Science and Technology, Taichung 413, Taiwan. jengshyangpan@fjut.edu.cn.

Abstract

Infrared and visible image matching methods have been rising in popularity with the emergence of more kinds of sensors, which provide more applications in visual navigation, precision guidance, image fusion, and medical image analysis. In such applications, image matching is utilized for location, fusion, image analysis, and so on. In this paper, an infrared and visible image matching approach, based on distinct wavelength phase congruency (DWPC) and log-Gabor filters, is proposed. Furthermore, this method is modified for non-linear image matching with different physical wavelengths. Phase congruency (PC) theory is utilized to obtain PC images with intrinsic and affluent image features for images containing complex intensity changes or noise. Then, the maximum and minimum moments of the PC images are computed to obtain the corners in the matched images. In order to obtain the descriptors, log-Gabor filters are utilized and overlapping subregions are extracted in a neighborhood of certain pixels. In order to improve the accuracy of the algorithm, the moments of PCs in the original image and a Gaussian smoothed image are combined to detect the corners. Meanwhile, it is improper that the two matched images have the same PC wavelengths, due to the images having different physical wavelengths. Thus, in the experiment, the wavelength of the PC is changed for different physical wavelengths. For realistic application, BiDimRegression method is proposed to compute the similarity between two points set in infrared and visible images. The proposed approach is evaluated on four data sets with 237 pairs of visible and infrared images, and its performance is compared with state-of-the-art approaches: the edge-oriented histogram descriptor (EHD), phase congruency edge-oriented histogram descriptor (PCEHD), and log-Gabor histogram descriptor (LGHD) algorithms. The experimental results indicate that the accuracy rate of the proposed approach is 50% higher than the traditional approaches in infrared and visible images.

Keywords: corners detection; infrared and visible image matching; phase congruency; physical wavelength; spectral decomposition.

Abstract

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