Automatic registration is a key researcher issue in 3D measurement field. In this work, we developed the
automatic registration system, which is composed of a stereo system with structured light and two axis
turntables. To realize the fully automatically 3D point registration, the novel method is proposed for
calibration the stereo system and the two turntable direction vector simultaneously. The plane calibration
rig with marked points was placed on the turntable and was captured by the left and right cameras of the
stereo system with different rotation angles of the two axis turntable. By the shot images, a stereo system
(intrinsically and extrinsically) was calibrated with classics camera model, and reconstruction 3D
coordinates of the marked points with different angle of the two turntable. The marked point in different
angle posted the specific circle, and the normal line of the circle around the turntable axis direction vector.
For the each turntable, different points have different circle and normal line, and the turntable axis
direction vector is calculated by averaging the different normal line. And the result show that, the
proposed registration system can precisely register point cloud under the different scanning angles. In
addition, there are no the ICP iterative procedures, and that make it can be used in registration of the
point cloud without the obvious features like sphere, cylinder comes and the other rotator.
Dedicated prototype systems for 3D imaging and modeling (3DIM) are presented. The 3D imaging systems are based on the principle of phase-aided active stereo, which have been developed in our laboratory over the past few years. The reported 3D imaging prototypes range from single 3D sensor to a kind of optical measurement network composed of multiple node 3D-sensors. To enable these 3D imaging systems, we briefly discuss the corresponding calibration techniques for both single sensor and multi-sensor optical measurement network, allowing good performance of the 3DIM prototype systems in terms of measurement accuracy and repeatability. Furthermore, two case studies including the generation of high quality color model of movable cultural heritage and photo booth from body scanning are presented to demonstrate our approach.
A new method for phase unwrapping is proposed, which makes the unwrapping of phase images realistic without binary
codes or more frequency fringe images produced by projection systems, uses only one additional digital speckle pattern
projected to help finding correspondence points. It means that the novel method is by the use of the additional speckle
pattern to achieve a unique point correspondence. The proposed method to get unwrapped phase will save images
recorded time. Experiment results demonstrated the proposed method is effective and robust.
Proc. SPIE. 8499, Applications of Digital Image Processing XXXV
KEYWORDS: Signal to noise ratio, Digital image processing, Imaging systems, Cameras, Image processing, Error analysis, Computer simulations, Machine vision, Device simulation, Current controlled current source
Circular targets are widely used in machine vision. The localization of circle center plays a crucial role in machine vision applications. In the process of camera imaging, the circles change to the ellipses in the image plane of camera because of perspective transformation. The center of ellipse usually does not coincide with the projected center of the circle, leading to a deviation of circle center. Based on perspective transformation and analytic geometry we present a new approach, in which the concentric circular targets are adopted and the true projective position of the circular target can be determined accurately. Both simulation and experiment results show that the proposed method is valid and robust. The true positions of the circular centers can be localized by proposed method without the center deviations.
In this paper, we propose an approach for the automatic fast registration of range images which are captured by the 3D optical measurement system. The measurement system consists of multiple 3D sensors distributed from the top to the bottom separately, which are used to measure object from different views. And a one-axis turntable is constructed to drive object revolve around the axis with eight angles. In each orientation, we can obtain multiple range images of object with the measurement system. And then all range images of object are needed to register to uniform coordinate frame. Firstly, we establish an in-situ 3-D calibration target in a measurement volume, which consists of a number of marker points. The coordinates of those marker points are obtained from the photogrammetry technique and they are thereafter employed for the determination of the locations and orientations of 3D sensors, which will be used to implement the registration among the range images taken from multi-sensors in one angle view. In addition, the registration of range images of eight angles can be achieved by the calibration of the rotation axis. In the end, the global iterative closest points method is proposed to attain the fine registration of all range images. The experimental results demonstrate the validity of the registration approach.
In this paper, we proposed a novel method for correcting the 2D calibration target. Firstly, we captured
multiple images of the inaccurate calibration target from multi-views and located the coordinates of
those circle landmarks in these images. Secondly, homonymous landmarks in different images could be
detected by a scheme for a special topology relation. Thirdly, we could accurately reconstruct the 3D
coordinates of landmarks with a scale constraint using bundle adjustment strategy. And finally, the
scale was computed from an accurate distance between any two landmarks. Then we could obtain the
truly coordinates of landmarks, which multiplied by the scale. The experimental results validated that
our method is efficient, high-precision, low-cost and easy-implementation, which can be widely
applied in vision measurement and system calibration.
Proc. SPIE. 7799, Mathematics of Data/Image Coding, Compression, and Encryption with Applications XII
KEYWORDS: Metrology, Detection and tracking algorithms, Error analysis, 3D modeling, Image registration, Optoelectronics, 3D metrology, Optimization (mathematics), 3D image processing, Range image registration
With the improvements in range image registration techniques, this paper focuses on error analysis of two registration methods being generally applied in industry metrology including the algorithm comparison, matching error, computing complexity and different application areas. One method is iterative closest points, by which beautiful matching results with little error can be achieved. However some limitations influence its application in automatic and fast metrology. The other method is based on landmarks. We also present a algorithm for registering multiple range-images with non-coding landmarks, including the landmarks' auto-identification and sub-pixel location, 3D rigid motion, point pattern matching, global iterative optimization techniques et al. The registering results by the two methods are illustrated and a thorough error analysis is performed.
Circular targets are commonly used in vision measurement and photogrammetry. Due to the asymmetric projection, the geometric centroid of the ellipse projection and the true projection of the target center are not identical, which leads to a systematic center location error. A method to correct the center location error is presented in this paper. Surface normal directions of circular targets are determined by camera calibration in advance. Then the correction values of the geometric centroids are calculated with space analytic geometry. The experimental results show the improvement of accuracy can be achieved after error correction by our method.