scholarly journals Efficient coarse registration method using translation- and rotation-invariant local descriptors towards fully automated forest inventory

2021 ◽  
pp. 100007
Author(s):  
Eric Hyyppä ◽  
Jesse Muhojoki ◽  
Xiaowei Yu ◽  
Antero Kukko ◽  
Harri Kaartinen ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Rotation Invariant ◽  
Registration Method ◽  
Local Descriptors ◽  
Coarse Registration

scholarly journals A Novel Coarse-to-Fine Scheme for Remote Sensing Image Registration Based on SIFT and Phase Correlation

2019 ◽  
Vol 11 (15) ◽  
pp. 1833 ◽  
Author(s):  
Han Yang ◽  
Xiaorun Li ◽  
Liaoying Zhao ◽  
Shuhan Chen
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Phase Correlation ◽  
Mean Square ◽  
Extended Phase ◽  
Registration Method ◽  
Coarse Registration ◽  
Feature Based ◽  
Coarse To Fine ◽  
Fine Registration

Automatic image registration has been wildly used in remote sensing applications. However, the feature-based registration method is sometimes inaccurate and unstable for images with large scale difference, grayscale and texture differences. In this manuscript, a coarse-to-fine registration scheme is proposed, which combines the advantage of feature-based registration and phase correlation-based registration. The scheme consists of four steps. First, feature-based registration method is adopted for coarse registration. A geometrical outlier removal method is applied to improve the accuracy of coarse registration, which uses geometric similarities of inliers. Then, the sensed image is modified through the coarse registration result under affine deformation model. After that, the modified sensed image is registered to the reference image by extended phase correlation. Lastly, the final registration results are calculated by the fusion of the coarse registration and the fine registration. High universality of feature-based registration and high accuracy of extended phase correlation-based registration are both preserved in the proposed method. Experimental results of several different remote sensing images, which come from several published image registration papers, demonstrate the high robustness and accuracy of the proposed method. The evaluation contains root mean square error (RMSE), Laplace mean square error (LMSE) and red–green image registration results.

Complex Image Registration Method Based on Correlative Coefficient Interpolation

2011 ◽  
Vol 65 ◽  
pp. 514-517
Author(s):  
Rui Niu ◽  
Xiao Tao Tang ◽  
Yu Wang
Keyword(s):  
Image Registration ◽  
High Efficiency ◽  
Interpolation Method ◽  
Interferometric Synthetic Aperture Radar ◽  
Registration Method ◽  
The Earth ◽  
Complex Image ◽  
Interferometric Phase ◽  
Coarse Registration ◽  
Coefficient Method

Interferometric Synthetic Aperture Radar is a kind of technology to acquire the DEM information on the surface of the earth. It is concerned and researched by all over the world. Complex image registration of high precision is the key step in InSAR data processing, its results directly influence on the quantity of interferometric phase , even to the DEM precision.This paper introduces the complex image registration plans which is used the correlative coefficient method to make the coarse registration, and is used the correlative coefficient interpolation method to make the high precise registration.The experiments with spaceborne and aeroplane InSAR data prove that this method is with feasibility, high efficiency and practicability.

scholarly journals COARSE POINT CLOUD REGISTRATION BY EGI MATCHING OF VOXEL CLUSTERS

2016 ◽  
Vol III-5 ◽  
pp. 97-103 ◽  
Author(s):  
Jinhu Wang ◽  
Roderik Lindenbergh ◽  
Yueqian Shen ◽  
Massimo Menenti
Keyword(s):  
Laser Scanning ◽  
Principal Component ◽  
Point Clouds ◽  
Data Sampling ◽  
Surface Geometry ◽  
Registration Method ◽  
Input Point ◽  
Coarse Registration ◽  
Point Distance ◽  
Fine Registration

Laser scanning samples the surface geometry of objects efficiently and records versatile information as point clouds. However, often more scans are required to fully cover a scene. Therefore, a registration step is required that transforms the different scans into a common coordinate system. The registration of point clouds is usually conducted in two steps, i.e. coarse registration followed by fine registration. In this study an automatic marker-free coarse registration method for pair-wise scans is presented. First the two input point clouds are re-sampled as voxels and dimensionality features of the voxels are determined by principal component analysis (PCA). Then voxel cells with the same dimensionality are clustered. Next, the Extended Gaussian Image (EGI) descriptor of those voxel clusters are constructed using significant eigenvectors of each voxel in the cluster. Correspondences between clusters in source and target data are obtained according to the similarity between their EGI descriptors. The random sampling consensus (RANSAC) algorithm is employed to remove outlying correspondences until a coarse alignment is obtained. If necessary, a fine registration is performed in a final step. This new method is illustrated on scan data sampling two indoor scenarios. The results of the tests are evaluated by computing the point to point distance between the two input point clouds. The presented two tests resulted in mean distances of 7.6 mm and 9.5 mm respectively, which are adequate for fine registration.

scholarly journals AN AUTOMATIC OPTICAL AND SAR IMAGE REGISTRATION METHOD USING ITERATIVE MULTI-LEVEL AND REFINEMENT MODEL

2016 ◽  
Vol XLI-B7 ◽  
pp. 593-600
Author(s):  
C. Xu ◽  
H. G. Sui ◽  
D. R. Li ◽  
K. M. Sun ◽  
J. Y. Liu
Keyword(s):  
Image Registration ◽  
Feature Matching ◽  
Visual Saliency ◽  
Specific Area ◽  
Image Feature ◽  
Sar Image ◽  
Registration Method ◽  
Adaptive Parameter ◽  
Coarse Registration ◽  
Multi Level

Automatic image registration is a vital yet challenging task, particularly for multi-sensor remote sensing images. Given the diversity of the data, it is unlikely that a single registration algorithm or a single image feature will work satisfactorily for all applications. Focusing on this issue, the mainly contribution of this paper is to propose an automatic optical-to-SAR image registration method using –level and refinement model: Firstly, a multi-level strategy of coarse-to-fine registration is presented, the visual saliency features is used to acquire coarse registration, and then specific area and line features are used to refine the registration result, after that, sub-pixel matching is applied using KNN Graph. Secondly, an iterative strategy that involves adaptive parameter adjustment for re-extracting and re-matching features is presented. Considering the fact that almost all feature-based registration methods rely on feature extraction results, the iterative strategy improve the robustness of feature matching. And all parameters can be automatically and adaptively adjusted in the iterative procedure. Thirdly, a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features, and Voronoi diagram is introduced into Spectral Point Matching (VSPM) to further enhance the matching accuracy between two sets of matching points. Experimental results show that the proposed method can effectively and robustly generate sufficient, reliable point pairs and provide accurate registration.

scholarly journals Efficient Coarse Registration of Pairwise TLS Point Clouds Using Ortho Projected Feature Images

2020 ◽  
Vol 9 (4) ◽  
pp. 255
Author(s):  
Hua Liu ◽  
Xiaoming Zhang ◽  
Yuancheng Xu ◽  
Xiaoyong Chen
Keyword(s):  
Parameter Estimation ◽  
Degrees Of Freedom ◽  
High Efficiency ◽  
Point Clouds ◽  
Azimuth Angle ◽  
Translation Vector ◽  
Scale Invariant ◽  
Registration Method ◽  
Coarse Registration ◽  
Vertical Translation

The degree of automation and efficiency are among the most important factors that influence the availability of Terrestrial light detection and ranging (LiDAR) Scanning (TLS) registration algorithms. This paper proposes an Ortho Projected Feature Images (OPFI) based 4 Degrees of Freedom (DOF) coarse registration method, which is fully automated and with high efficiency, for TLS point clouds acquired using leveled or inclination compensated LiDAR scanners. The proposed 4DOF registration algorithm decomposes the parameter estimation into two parts: (1) the parameter estimation of horizontal translation vector and azimuth angle; and (2) the parameter estimation of the vertical translation vector. The parameter estimation of the horizontal translation vector and the azimuth angle is achieved by ortho projecting the TLS point clouds into feature images and registering the ortho projected feature images by Scale Invariant Feature Transform (SIFT) key points and descriptors. The vertical translation vector is estimated using the height difference of source points and target points in the overlapping regions after horizontally aligned. Three real TLS datasets captured by the Riegl VZ-400 and the Trimble SX10 and one simulated dataset were used to validate the proposed method. The proposed method was compared with four state-of-the-art 4DOF registration methods. The experimental results showed that: (1) the accuracy of the proposed coarse registration method ranges from 0.02 m to 0.07 m in horizontal and 0.01 m to 0.02 m in elevation, which is at centimeter-level and sufficient for fine registration; and (2) as many as 120 million points can be registered in less than 50 s, which is much faster than the compared methods.

Point Cloud Registration Method Based on Face-Mating after Denosing

2014 ◽  
Vol 536-537 ◽  
pp. 131-135 ◽  
Author(s):  
Tian Fan Chen ◽  
Cheng Hui Gao ◽  
Bing Wei He
Keyword(s):  
Least Squares ◽  
Point Cloud ◽  
Convergence Speed ◽  
Least Square ◽  
Point Cloud Registration ◽  
Registration Method ◽  
Least Squares Fitting ◽  
Noise Point ◽  
Coarse Registration

A method is presented to accurate face-mating point cloud registration after dealing with noise point. Point cloud registration is divided into two parts,firstly,coarse registration is applied for visual point cloud,then three local sufaces of overlap cloud region are selected to be mating calculated after denosing base on least squares fitting , at last accurate splicing parameters of translation and rotation are acquired by nonlinear least square .This algorithm is easy to deal with the denosing, has faster convergence speed and higher registration accuracy.Its feasibility is proved by samples.

ADAPTION OF INVARIANT FEATURES IN IMAGE FOR POINT CLOUDS REGISTRATION

2015 ◽  
Vol 75 (10) ◽  
Author(s):  
Mohd Azwan Abbas ◽  
Halim Setan ◽  
Zulkepli Majid ◽  
Albert K. Chong ◽  
Lau Chong Luh ◽  
...  
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Scale Invariant ◽  
Registration Method ◽  
Flat Surfaces ◽  
Invariant Features ◽  
Limited Class ◽  
Coarse Registration ◽  
Straight Lines ◽  
Scale Invariant Feature

Currently, coarse registration methods for scanner are required heavy operator intervention either before or after scanning process. There also have an automatic registration method but only applicable to a limited class of objects (e.g. straight lines and flat surfaces). This study is devoted to a search of a computationally feasible automatic coarse registration method with a broad range of applicability. Nowadays, most laser scanner systems are supplied with a camera, such that the scanned data can also be photographed. The proposed approach will exploit the invariant features detected from image to associate point cloud registration. Three types of detectors are included: scale invariant feature transform (SIFT), 2) Harris affine, and 3) maximally stable extremal regions (MSER). All detected features will transform into the laser scanner coordinate system, and their performance is measured based on the number of corresponding points. Several objects with different observation techniques were performed to evaluate the capability of proposed approach and also to evaluate the performance of selected detectors.  

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