Control Points For Registration Research Articles

Research on blade statistics measurement registration planning technology

Blade has the characteristics of high machining quality of complex curved surface. If there is no benchmark before machining, it is impossible to judge whether the blades before machining are qualified. Therefore, it is necessary to analyze the blade measurement data. Due to the large measurement point error and disordered distribution, it is necessary to optimize the blade registration. Therefore, blade model registration and positioning is particularly important in blade shape detection and analysis. First, preregistration is carried out based on the six point optimization selection of the blade. After preregistration, the selection method of registration control point set based on theoretical model and statistical error is proposed, planning the registration datum point set on the blade model. The registration control point set is obtained through the registration operation between the measurement data and the registration reference point set. Finally, based on the stability and reliability of important sampling sensitivity and statistical error, obtain the probability density function of error normal distribution statistics samples and important samples. The selection of statistical control points and the rationality of the objective function were verified. The stability/reliability of the statistical alignment point selection is proved to be feasible. The statistical registration deviation is [0.015,0.026] mm, and the ICP registration deviation is [0.031,0.035] mm. The average deviation of statistics registration is about 0.013 mm smaller than the average deviation of ICP registration. The deviation of statistical sampling points is about 0.0214 mm, and that of traditional sampling points is about 0.0275 mm. The deviation of statistical sampling points is about 0.0061 mm smaller than that of traditional sampling points. It meets the requirements of rapid, high efficiency and high precision measurement for aeroengine blades.

A Topology Based Automatic Registration Method for Infrared and Polarized Coupled Imaging

In multi-source camera collaborative imaging research, it is known that the differences in size and resolution of the sensor chip, the angle of view and field of view when imaging, and the imaging characteristics of optical systems between cameras, makes image registration a topic that can never be avoided in data analysis and post-processing. Additionally, lacking common features between multi-source images means that the accurate registration of multi-modal images can only be completed manually. Aiming at the registration problem of the polarization parameter image and infrared image, this study takes advantage of the invariant feature of the imaging target topology and introduces the image texture-based segmentation method to obtain the target topology structure. Subsequently, the registration control points are extracted based on the target topology skeleton, which can break through the limitation of feature differences, improve the robustness of the algorithm to target transformation, and realize the automatic registration of multi-source images.

Registration of Terrestrial LiDAR and Panoramic Imagery Using the Spherical Epipolar Line and Spherical Absolute Orientation Model

Registration between terrestrial LiDAR and optical imagery plays a crucial role in information fusion. However, it is difficult to find reliable correlations among the different feature information of optical imagery and LiDAR point clouds. Therefore, in order to achieve high-precision registration of heterogeneous sensors, a method based on spherical epipolar line and spherical absolute orientation is proposed in this paper. The method firstly projects the LiDAR point clouds into spherical images based on the spherical imaging model and derives the spherical epipolar line equation. Then the relative and absolute orientations of the spherical LiDAR images and the optical images are performed based on manually selected control points. Finally, based on Harris corner extraction, combined with the geometric constraints of the spherical epipolar line and absolute orientation, dense matching between optical and LIDAR images are achieved, and all matching points are used as control points for registration to improve the accuracy of manually selected points registration. Multiple sets of test data are acquired outdoors using a FARO Focus S laser scanner, a Z + F IMAGER 5010C laser scanner, and a Ladybug5+ panoramic camera. The experimental results show that the method in this paper is practical and improves the accuracy of manual points selection registration, and the degree of improvement is related to the number of successfully matched corner points.

Multispectral registration method based on stellar trajectory fitting

Image registration techniques for detecting moving targets in space require high temporal resolution and multispectral images to improve the target detection probability and reduce the false positive rate. At present, image registration accuracy is affected by the effective number of common multispectral registration control points as well as its stability. Image registration based on on-orbit stellar trajectory measurement can be used to perform on-orbit modifications of registration deviation caused by thermal distortions during launch. This study proposes a new image registration method based on the on-orbit detection of stars by using the stellar trajectory on a camera’s focal plane. A generated 12 × 12 data template and the Lagrange interpolation method are used in the registration model. Multispectral image registration based on stellar trajectory fitting can achieve high-precision image registration among different spectra.

STUDY OF AUTOMATIC IMAGE RECTIFICATION AND REGISTRATION OF SCANNED HISTORICAL AERIAL PHOTOGRAPHS

Abstract. Historical aerial photographs directly provide good evidences of past times. The Research Center for Humanities and Social Sciences (RCHSS) of Taiwan Academia Sinica has collected and scanned numerous historical maps and aerial images of Taiwan and China. Some maps or images have been geo-referenced manually, but most of historical aerial images have not been registered since there are no GPS or IMU data for orientation assisting in the past. In our research, we developed an automatic process of matching historical aerial images by SIFT (Scale Invariant Feature Transform) for handling the great quantity of images by computer vision. SIFT is one of the most popular method of image feature extracting and matching. This algorithm extracts extreme values in scale space into invariant image features, which are robust to changing in rotation scale, noise, and illumination. We also use RANSAC (Random sample consensus) to remove outliers, and obtain good conjugated points between photographs. Finally, we manually add control points for registration through least square adjustment based on collinear equation. In the future, we can use image feature points of more photographs to build control image database. Every new image will be treated as query image. If feature points of query image match the features in database, it means that the query image probably is overlapped with control images.With the updating of database, more and more query image can be matched and aligned automatically. Other research about multi-time period environmental changes can be investigated with those geo-referenced temporal spatial data.

STUDY OF AUTOMATIC IMAGE RECTIFICATION AND REGISTRATION OF SCANNED HISTORICAL AERIAL PHOTOGRAPHS

Historical aerial photographs directly provide good evidences of past times. The Research Center for Humanities and Social Sciences (RCHSS) of Taiwan Academia Sinica has collected and scanned numerous historical maps and aerial images of Taiwan and China. Some maps or images have been geo-referenced manually, but most of historical aerial images have not been registered since there are no GPS or IMU data for orientation assisting in the past. In our research, we developed an automatic process of matching historical aerial images by SIFT (Scale Invariant Feature Transform) for handling the great quantity of images by computer vision. SIFT is one of the most popular method of image feature extracting and matching. This algorithm extracts extreme values in scale space into invariant image features, which are robust to changing in rotation scale, noise, and illumination. We also use RANSAC (Random sample consensus) to remove outliers, and obtain good conjugated points between photographs. Finally, we manually add control points for registration through least square adjustment based on collinear equation. In the future, we can use image feature points of more photographs to build control image database. Every new image will be treated as query image. If feature points of query image match the features in database, it means that the query image probably is overlapped with control images.With the updating of database, more and more query image can be matched and aligned automatically. Other research about multi-time period environmental changes can be investigated with those geo-referenced temporal spatial data.

A Fast Algorithm to Estimate the Deepest Points of Lakes for Regional Lake Registration.

When conducting image registration in the U.S. state of Alaska, it is very difficult to locate satisfactory ground control points because ice, snow, and lakes cover much of the ground. However, GCPs can be located by seeking stable points from the extracted lake data. This paper defines a process to estimate the deepest points of lakes as the most stable ground control points for registration. We estimate the deepest point of a lake by computing the center point of the largest inner circle (LIC) of the polygon representing the lake. An LIC-seeking method based on Voronoi diagrams is proposed, and an algorithm based on medial axis simplification (MAS) is introduced. The proposed design also incorporates parallel data computing. A key issue of selecting a policy for partitioning vector data is carefully studied, the selected policy that equalize the algorithm complexity is proved the most optimized policy for vector parallel processing. Using several experimental applications, we conclude that the presented approach accurately estimates the deepest points in Alaskan lakes; furthermore, we gain perfect efficiency using MAS and a policy of algorithm complexity equalization.

Patient specific respiratory motion modeling using a limited number of 3D lung CT images.

To build a patient specific respiratory motion model with a low dose, a novel method was proposed that uses a limited number of 3D lung CT volumes with an external respiratory signal. 4D lung CT volumes were acquired for patients with in vitro labeling on the upper abdominal surface. Meanwhile, 3D coordinates of in vitro labeling were measured as external respiratory signals. A sequential correspondence between the 4D lung CT and the external respiratory signal was built using the distance correlation method, and a 3D displacement for every registration control point in the CT volumes with respect to time can be obtained by the 4D lung CT deformable registration. A temporal fitting was performed for every registration control point displacements and an external respiratory signal in the anterior-posterior direction respectively to draw their fitting curves. Finally, a linear regression was used to fit the corresponding samples of the control point displacement fitting curves and the external respiratory signal fitting curve to finish the pulmonary respiration modeling. Compared to a B-spline-based method using the respiratory signal phase, the proposed method is highly advantageous as it offers comparable modeling accuracy and target modeling error (TME); while at the same time, the proposed method requires 70% less 3D lung CTs. When using a similar amount of 3D lung CT data, the mean of the proposed method's TME is smaller than the mean of the PCA (principle component analysis)-based methods' TMEs. The results indicate that the proposed method is successful in striking a balance between modeling accuracy and number of 3D lung CT volumes.

Model Registration Control Point Set Selection for Turbine Blade Shape Inspection

Model Registration Control Point Set Selection for Turbine Blade Shape Inspection

Registration of the expression patterns of Drosophila segmentation genes by two independent methods

To construct an integrated map of Drosophila segmentation gene expression from partial data taken from individual embryos. Spline and wavelet based registration techniques were developed to register Drosophila segmentation gene expression data. As ground control points for registration we used the locations of extrema on gene expression patterns, represented in 1D. The registration method was characterized by unprecedented high accuracy. A method for constructing the integrated pattern of gene expression at cellular resolution was designed. These patterns were constructed for 9 segmentation genes belonging to gap and pair-rule classes.

Geometric correction of airborne scanner imagery using orthophotos and triangulated feature point matching

Abstract We present here a new scheme that performs geometrical correction for airborne multi-spectral scanner imagery provided that an adequate reference image, i.e., an orthophoto, is available. Instead of the ray-tracing method, we determine the corresponding ground coordinates for each pixel of an input image by integrating feature point matching under Delaunay triangulated networks. The major components of the scheme include progressive generation of registration control points, and image registration. Comprehensive investigation for quantitative error analysis is also included. Experimental results indicate that the corrected images attain an accuracy of better than 09 pixels.