Registration of infrared and visible images based on the correlation of the edges

Yi, Xiang; Wang, Bingjian; Yao, Fang; Shang-qian, Liu

doi:10.1109/cisp.2013.6745309

Cited by 9 publications

(2 citation statements)

References 4 publications

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“…Area-based methods commonly utilize a template window of a given size to detect the feature points between two images. After the template window in an image is defined, the corresponding window in the other image is found by computing the matching information according to a predefined similarity measure, such as cross-correlation [25], phase correlation [26], or mutual information [27]. The centers of the matching windows are regarded as the feature points, which are then used to align the two images.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%

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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“…In contrast to global region-based methods, local feature-based methods utilize the extracted features to establish correspondence, and they are generally divided into two groups: typical features-based methods and structural features-based methods. In the first group, extracted typical features include edges [17], lines [18][19][20][21][22], contours [23], gradient distribution [15,24], and their variants [25][26][27][28]. Those methods above are robust in response to geometrical changes, occlusion, background clutter, and noise.…”

Section: Related Workmentioning

confidence: 99%

A Novel Affine and Contrast Invariant Descriptor for Infrared and Visible Image Registration

Liu

Zhang

et al. 2018

Remote Sensing

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Infrared and visible image registration is a very challenging task due to the large geometric changes and the significant contrast differences caused by the inconsistent capture conditions. To address this problem, this paper proposes a novel affine and contrast invariant descriptor called maximally stable phase congruency (MSPC), which integrates the affine invariant region extraction with the structural features of images organically. First, to achieve the contrast invariance and ensure the significance of features, we detect feature points using moment ranking analysis and extract structural features via merging phase congruency images in multiple orientations. Then, coarse neighborhoods centered on the feature points are obtained based on Log-Gabor filter responses over scales and orientations. Subsequently, the affine invariant regions of feature points are determined by using maximally stable extremal regions. Finally, structural descriptors are constructed from those regions and the registration can be implemented according to the correspondence of the descriptors. The proposed method has been tested on various infrared and visible pairs acquired by different platforms. Experimental results demonstrate that our method outperforms several state-of-the-art methods in terms of robustness and precision with different image data and also show its effectiveness in the application of trajectory tracking.

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Image Matching Using Phase Congruency and Log-Gabor Filters in the SAR Images and Visible Images

Liu

Hong-xia

et al. 2020

Advances in Intelligent Systems and Computing

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Registration of infrared and visible images based on the correlation of the edges

Cited by 9 publications

References 4 publications

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

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

A Novel Affine and Contrast Invariant Descriptor for Infrared and Visible Image Registration

Image Matching Using Phase Congruency and Log-Gabor Filters in the SAR Images and Visible Images

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