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.
Highlights
Image matching is the process of aligning images of the same scene which have been acquired under different conditions, such as with a different field of view, at different scales, different resolutions, different times, or using different sensors, and so on
The proposed algorithm was evaluated on the four data sets using the five evaluation measures and was compared with five existing algorithms, where the results showed that the proposed algorithm had better matching performance
Edge-oriented histogram descriptor (EHD): this algorithm first detects the contour of the image and, the edge histogram descriptor is obtained by using the MPEG-7 standard [69]
Summary
Image matching is the process of aligning images of the same scene which have been acquired under different conditions, such as with a different field of view, at different scales, different resolutions, different times, or using different sensors, and so on. It is a prerequisite step for many applications [1,2,3], Sensors 2019, 19, 4244; doi:10.3390/s19194244 www.mdpi.com/journal/sensors. Image matching methodologies have been widely and effectively applied in the field of the target location, presenting unique advantages [8]. For the purpose of determining the best matching relations between two images with such differences, an effective feature point detection approach is of significant use
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