Multi-Angle Optical Image Automatic Registration by Combining Point and Line Features

Su, Jia; Meng, Juntong; Hou, Weimin; Wang, Rong; Luo, Xin

doi:10.3390/s22030739

Cited by 8 publications

(6 citation statements)

References 35 publications

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“…LSD (Line Segment Detector) was proposed by Rafael Grompone von Gioi in 2012 [ 26 ]. Based on Burns’ algorithm [ 27 ], this algorithm combines gradient information and direction information of the image to extract straight lines [ 28 , 29 ]. It is mainly divided into three steps as follows: The pixel gradient of the whole image is calculated to generate the corresponding gradient fields, and the pixels whose gradient is within a certain threshold are merged into a region, which is called the line-support region.…”

Section: Principle and Methods Of Mtf Measurementmentioning

confidence: 99%

MTF Measurement by Slanted-Edge Method Based on Improved Zernike Moments

Zhang¹,

Wang²,

Wu³

et al. 2023

Sensors

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The modulation transfer function (MTF) is an important parameter for performance evaluation of optical imaging systems in photogrammetry and remote sensing; the slanted-edge method is one of the main methods for measuring MTF. To solve the problem of inaccurate edge detection by traditional methods under the conditions of noise and blur, this paper proposes a new method of MTF measurement with a slanted-edge method based on improved Zernike moments, which firstly introduces the Otsu algorithm to automatically determine the Zernike moment threshold for sub-pixel edge detection to precisely locate the edge points, then obtains LSF through edge point projection, ESF sampling point acquisition, smoothing, fitting, taking ESF curve differential and Gaussian fitting, and finally, accurately obtaining MTF by LSF Fourier transform and modulo normalization. Based on simulation experiments and outdoor target experiments, the reliability of the proposed algorithm is verified by the deviations of slanted-edge angle and MTF measurement, and the tolerance degree of edge detection to noise and ambiguity are analyzed. The results show that compared with ISO 12233, OMNI-sine method, Hough transform method and LSD method, this algorithm has the highest edge detection accuracy, the maximum tolerance of noise and ambiguity, and also improves the accuracy of MTF measurement.

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Section: Principle and Methods Of Mtf Measurementmentioning

confidence: 99%

MTF Measurement by Slanted-Edge Method Based on Improved Zernike Moments

Zhang¹,

Wang²,

Wu³

et al. 2023

Sensors

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show abstract

“…The circumference of the crescent moon located below the nail was calculated as a square as shown in Figure 3(a), and the property of the midpoint was used for this purpose (Su et al, 2022). By repeating the previous procedure again, the nail bed is calculated as shown in Figure 3(b).…”

Section: Semantic Pointsmentioning

confidence: 99%

Implementation of Biometric Identification System Utilizing Fingernail Feature Points

2024

nano-ntp

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The biometric system has to differentiate each person's biometric data precisely. This study suggested the use of nail information in a biometric authentication system. We researched the makeup of human nails in order to develop a state-of-the-art biometric identification system that utilizes the finger lunula with a nail bed. It was established through analysis of the gathered nail photos that each person's nail differed in terms of length, breadth, and semicircle. Our choice for the study nail among the fingernails was the thumbnail. The thumb has the biggest nail, which accounts for the great image accuracy. The analytical methods employed in the system were SIFT, haar wavelet, independent component analysis, and principal component analysis. The support vector machine and the Naive Bayes approach were used to examine the algorithm's performance. It is possible to use genuine human nail data in experiments to create a nail-based biometric identification system.

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“…These can be treated as a unit to evaluate the performance [60] or as a group [61]. Alternatively, studies are utilizing fusing FAST and Harris [62], as well as combining point and line features [63].…”

Section: Related Workmentioning

confidence: 99%

Two-View Structure-from-Motion with Multiple Feature Detector Operators

Dippold

Tsai

2023

Remote Sensing

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This paper presents a novel two-view Structure-from-Motion (SfM) algorithm with the application of multiple Feature Detector Operators (FDO). The key of this study is the implementation of multiple FDOs into a two-view SfM algorithm. The two-view SfM algorithm workflow can be divided into three general steps: feature detection and matching, pose estimation and point cloud (PCL) generation. The experimental results, the quantitative analyses and a comparison with existing algorithms demonstrate that the implementation of multiple FDOs can effectively improve the performance of a two-view SfM algorithm. Firstly, in the Oxford test dataset, the RMSE reaches on average 0.11 m (UBC), 0.36 m (bikes), 0.52 m (trees) and 0.37 m (Leuven). This proves that illumination changes, blurring and JPEG compression can be handled satisfactorily. Secondly, in the EPFL dataset, the number of features lost in the processes is 21% with a total PCL of 27,673 pt, and this is only minimally higher than ORB (20.91%) with a PCL of 10,266 pt. Finally, the verification process with a real-world unmanned aerial vehicle (UAV) shows that the point cloud is denser around the edges, the corners and the target, and the process speed is much faster than existing algorithms. Overall, the framework proposed in this study has been proven a viable alternative to a classical procedure, in terms of performance, efficiency and simplicity.

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Multi-Angle Optical Image Automatic Registration by Combining Point and Line Features

Cited by 8 publications

References 35 publications

MTF Measurement by Slanted-Edge Method Based on Improved Zernike Moments

MTF Measurement by Slanted-Edge Method Based on Improved Zernike Moments

Implementation of Biometric Identification System Utilizing Fingernail Feature Points

Two-View Structure-from-Motion with Multiple Feature Detector Operators

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