Visible and Infrared Image Registration Employing Line-Based Geometric Analysis

Han, Jungong; Pauwels, Eric J.; Zeeuw, Paul de

doi:10.1007/978-3-642-32436-9_10

Cited by 11 publications

(4 citation statements)

References 9 publications

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“…In order to better illustrate the performance of LMLG detector, we compared LMLG based registration method with the nine detectors and three other registration algorithms on a same dataset in visual and quantitative ways. The three other algorithms used for comparison are Hrkać's method based on corners and hausdorff distance [11], Han's method based on line-based geometric analysis [50] and Liu's method based on SIFT flow [51]. All of the three methods are representative feature-based methods for infrared-visible image registration.…”

Section: B Infrared-visible Image Registrationmentioning

confidence: 99%

Interest Point Detection by Limiting Form of Median Log Filter

Chen

Lyu

Wang³

et al. 2019

IEEE Access

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Interest point detection has been widely used in image analysis applications. However, some interest points, including small structures and large angle corners, could not be effectively extracted. This paper proposes a limiting form of median Laplacian of Gaussian (LMLG) filter, which combines the superiority of the traditional Laplacian of Gaussian (LoG) filter and a limiting form of the weighted median LoG filter. A detector is also proposed based on the LMLG filter. The LMLG filter aims to improve the detection of LoG-based methods for interest points, especially small structures and large angle corners. Also, it could detect blobs, edges, and local structures. We conduct the repeatability and discrimination experiments on the Oxford dataset. Moreover, we conduct the recall rate experiment on the DTU dataset. The experiments show that the proposed method achieves comparable performance with state-of-the-art methods. In order to verify the utility of the LMLG detector, we carry out a series of interest point detector-based applications: face recognition, infrared-visible image registration, and image classification. The results demonstrate that the LMLG detector performs better than the nine detectors in face recognition. The LMLG detector outperforms the nine detectors and Hrkać's, Han's and Liu's methods in infrared-visible image registration. Our method also gives a comparable result on image classification. The source code of the proposed LMLG detector is made publicly available at https://github.com/chenjzBUAA/LMLG-detector.INDEX TERMS Feature extraction, image registration, interest point detection, Laplacian of Gaussian filter.

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Section: B Infrared-visible Image Registrationmentioning

confidence: 99%

Interest Point Detection by Limiting Form of Median Log Filter

Chen

Lyu

Wang³

et al. 2019

IEEE Access

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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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“…The metric k,k 2 denotes the Euclidean distance between the two lines, and the error for a line is bounded by a maximum value e m . More details about this procedure can be found in (Han et al, 2012).…”

Section: Line Matchingmentioning

confidence: 99%

Visible and infrared image registration in man-made environments employing hybrid visual features

Han

Pauwels

Zeeuw

2013

Pattern Recognition Letters

Self Cite

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a b s t r a c tWe present a new method to register a pair of images captured in different image modalities. Unlike most of existing systems that register images by aligning single type of visual features, e.g., interest point or contour, we try to align hybrid visual features, including straight lines and interest points. The entire algorithm is carried out in two stages: line-based global transform approximation and point-based local transform adaptation. In the first stage, straight lines derived from edge pixels are employed to find correspondences between two images in order to estimate a global perspective transformation. In the second stage, we divide the entire image into non-overlapping cells with fixed size. The point having the strongest corner response within each cell is selected as the interest point. These points are transformed to other image based on the global transform, and then used to bootstrap a local correspondence search. Experimental evidence shows this method achieves better accuracy for registering visible and long wavelength infrared images/videos as compared to state-of-the-art approaches.

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Visible and Infrared Image Registration Employing Line-Based Geometric Analysis

Cited by 11 publications

References 9 publications

Interest Point Detection by Limiting Form of Median Log Filter

Interest Point Detection by Limiting Form of Median Log Filter

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

Visible and infrared image registration in man-made environments employing hybrid visual features

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