Robust key point descriptor for multi-spectral image matching

Yao, Qunyan; Cao, Zhiguo; Wen, Zhigang; Yu, Zhenghong

doi:10.1109/jsee.2014.00078

Cited by 6 publications

(2 citation statements)

References 21 publications

(37 reference statements)

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“…More studies have detailed the advances in visible and infrared image matching [46,47]. Concerning the methods which deal with the special application of handling multispectral images, the descriptor edge-oriented histogram (EOH) has been considered to be a dramatic baseline in scientific studies [46,47,48,49]. However, it has been shown that it is difficult to select an appropriate threshold for the EOH descriptor, in order to ensure that the edges extracted from multispectral images are similar [48].…”

Section: Related Workmentioning

confidence: 99%

“…Concerning the methods which deal with the special application of handling multispectral images, the descriptor edge-oriented histogram (EOH) has been considered to be a dramatic baseline in scientific studies [46,47,48,49]. However, it has been shown that it is difficult to select an appropriate threshold for the EOH descriptor, in order to ensure that the edges extracted from multispectral images are similar [48]. Thus, Fu et al advocated a local feature descriptor with a combination of structural and textural information for multispectral image matching, which is perfect for the non-linear intensity changes of multispectral images [50].…”

Section: Related Workmentioning

confidence: 99%

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

Liu

Pan

2019

Sensors

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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.

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