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RISS 인기검색어
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- 저자 : Xu Hongling (검색결과 4 건)
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Research on the Basic Rodrigues Rotation in the Conversion of Point Clouds Coordinate System
Maolin Xu,Jiaxing Wei,Hongling Xiu 한국정보처리학회 2020 Journal of information processing systems Vol.16 No.1
In order to solve the problem of point clouds coordinate conversion of non-directional scanners, this paperproposes a basic Rodrigues rotation method. Specifically, we convert the 6 degree-of-freedom (6-DOF) rotationand translation matrix into the uniaxial rotation matrix, and establish the equation of objective vector conversionbased on the basic Rodrigues rotation scheme. We demonstrate the applicability of the new method by using abar-shaped emboss point clouds as experimental input, the three-axis error and three-term error as validateindicators. The results suggest that the new method does not need linearization and is suitable for optionalrotation angle. Meanwhile, the new method achieves the seamless splicing of point clouds. Furthermore, thecoordinate conversion scheme proposed in this paper performs superiority by comparing with the iterativeclosest point (ICP) conversion method. Therefore, the basic Rodrigues rotation method is not only regarded asa suitable tool to achieve the conversion of point clouds, but also provides certain reference and guidance forsimilar projects.
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Harris Scale Invariant Corner Detection Algorithm Based on the Significant Region
Wu Peng,Xu Hongling,Li Wenlin,Song Wenlong 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.3
The traditional Harris corner detection algorithm is sensitive to scale change, corners detected throughout the entire image under complex background, thus extracting more false corners, lead to the follow-up of large amount of calculation and a high rate of error matching. To solve this problem, this paper proposes an optimized Harris corner detection algorithm. First, a significant region detection method is used to extract the target area, and take closing operation for the result figure, can effectively achieve target and background segmentation; second, scale invariant describing methods is applied to Harris algorithm, at the same time, combined with the non-maximum suppression methods to extract corners, get more right corners. Through experiment contrasts, the algorithm used in this paper can be improved more corner detection performance.
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Wu Peng,Xu Hongling,Xie Pengcheng 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.5
Aiming at the problem of ground penetrating radar image denoising, a new adaptive image denoising algorithm based on nonsubsampled Contourlet transform is proposed. The algorithm firstly performs nonsubsampled Contourlet transform to the noise image, to obtain the coefficients of each directional sub band and each scale, then, according to the energy of the coefficient, the denoising threshold value is adjusted adaptively. Simulation results show that, compared with the wavelet threshold denoising algorithm, the proposed algorithm can effectively remove the Gauss white noise in the image, improve the peak signal to noise ratio (PNSR), while preserving the edge details of the image, it can improve the PSNR value and reduce the Gibbs phenomenon.
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A Point Clouds Fast Thinning Algorithm Based on Sample Point Spatial Neighborhood
Jiaxing Wei,Maolin Xu,Hongling Xiu 한국정보처리학회 2020 Journal of information processing systems Vol.16 No.3
Point clouds have ability to express the spatial entities, however, the point clouds redundancy always involvessome uncertainties in computer recognition and model construction. Therefore, point clouds thinning is an indispensablestep in point clouds model reconstruction and other applications. To overcome the shortcomingsof complex classification index and long time consuming in existing point clouds thinning algorithms, thispaper proposes a point clouds fast thinning algorithm. Specifically, the two-dimensional index is established inplane linear array (x, y) for the scanned point clouds, and the thresholds of adjacent point distance differenceand height difference are employed to further delete or retain the selected sample point. Sequentially, the indexof sample point is traversed forwardly and backwardly until the process of point clouds thinning is completed. The results suggest that the proposed new algorithm can be applied to different targets when the thresholds arebuilt in advance. Besides, the new method also performs superiority in time consuming, modelling accuracyand feature retention by comparing with octree thinning algorithm.
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