Test sieves with dense grid structure are widely used in many fields, accurate gird size calibration is rather critical for success of grading analysis and test sieving. But traditional calibration methods suffer from the disadvantages of low measurement efficiency and shortage of sampling number of grids which could lead to quality judgment risk. Here, a fast and precise test sieve inspection method is presented. Firstly, a coaxial imaging system with low and high optical magnification probe is designed to capture the grid images of the test sieve. Then, a scaling ratio between low and high magnification probes can be obtained by the corresponding grids in captured images. With this, all grid dimensions in low magnification image can be obtained by measuring few corresponding grids in high magnification image with high accuracy. Finally, by scanning the stage of the tri-axis platform of the measuring apparatus, whole surface of the test sieve can be quickly inspected. Experiment results show that the proposed method can measure the test sieves with higher efficiency compare to traditional methods, which can measure 0.15 million grids (gird size 0.1mm) within only 60 seconds, and it can measure grid size range from 20μm to 5mm precisely. In a word, the presented method can calibrate the grid size of test sieve automatically with high efficiency and accuracy. By which, surface evaluation based on statistical method can be effectively implemented, and the quality judgment will be more reasonable.
Fringe projection 3D microscopy (FP-3DM) plays an important role in micro-machining and micro-fabrication. FP-3DM may be realized with quite different arrangements and principles, which make people confused to select an appropriate one for their specific application. This paper introduces the ray-based general imaging model to describe the FP-3DM, which has the potential to get a unified expression for different system arrangements. Meanwhile the dedicated calibration procedure is also presented to realize quantitative 3D imaging. The validity and accuracy of proposed calibration approach is demonstrated with experiments.
In this paper, a method for constructing photorealistic textured model using 3D structured light digitizer is presented. Our method acquisition of range images and texture images around object, and range images
are registered and integrated to construct geometric model of object. System is calibrated and poses of
texture-camera are determined so that the relationship between texture and geometric model is established. After that, a global optimization is applied to assign compatible texture to adjacent surface and followed with
a level procedure to remove artifacts due to vary lighting, approximate geometric model and so on. Lastly,
we demonstrate the effect of our method on constructing a real model of world.
A method for real-time three-dimensional (3D) imaging based on Hilbert transform is proposed. Based on the properties
of Hilbert transform and De Bruijn sequence, we design an encoding technique based on color fringe patterns to realize
3-D reconstruction of the phase distribution and range images. The calculation of phase map is implemented by using
two sinusoidal fringe patterns with phase shifting 0 and π / 2 each other. Two phase-shifted fringe patterns are assigned
to the red and blue channel of a color pattern, respectively. The phase unwrapping is accomplished with aid of the De
Bruijn sequence pattern stored in the green channel. The experiment results show that the proposed method can not only
acquire 3D data in real-time and one-shot fashion, but also obtain high-resolution and high-density range image data
without any error propagation.
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: Cameras, Image processing, Error analysis, Digital image processing, Imaging systems, Machine vision, Device simulation, Signal to noise ratio, Current controlled current source, Computer simulations
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.
It is usually difficult to calibrate the 3-D vision inspection system that may be employed to measure the large-scale
engineering objects. One of the challenges is how to in-situ build-up a large and precise calibration target. In this paper,
we present a calibration target reconstruction strategy to solve such a problem. First, we choose one of the engineering
objects to be inspected as a calibration target, on which we paste coded marks on the object surface. Next, we locate and
decode marks to get homologous points. From multiple camera images, the fundamental matrix between adjacent images
can be estimated, and then the essential matrix can be derived with priori known camera intrinsic parameters and
decomposed to obtain camera extrinsic parameters. Finally, we are able to obtain the initial 3D coordinates with
binocular stereo vision reconstruction, and then optimize them with the bundle adjustment by considering the lens
distortions, leading to a high-precision calibration target. This reconstruction strategy has been applied to the inspection
of an industrial project, from which the proposed method is successfully validated.
Texture blending is an important technique for generating a photorealistic appearance of a physical model or scene. In
this paper, we present an efficient texture blending algorithm that can be utilized to register and merge multiple
texture-mapped range images of physical objects acquired from different view points, resulting in a 3-D photorealistic
model. The technique details with respect to the proposed algorithm are described and verified by experiment results.
Proc. SPIE. 7799, Mathematics of Data/Image Coding, Compression, and Encryption with Applications XII
KEYWORDS: Image registration, Error analysis, Optimization (mathematics), Range image registration, Metrology, 3D metrology, Detection and tracking algorithms, Optoelectronics, 3D image processing, 3D modeling
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.
Advanced measurement techniques for the structural diagnostics of artwork are increasingly providing more complex
data that needs to be conveyed to conservators in a meaningful way. Holography and speckle interferometry based
sensors are commonly used for this application and of these shearography is quite suitable for measurements outside the
optics laboratory, due to the stability of using a common path interferometer configuration. Shearography provides noncontact
full-field displacement gradient data on surface and sub-surface defects in the form of phase maps. The display
of this data in the form of wrapped phase maps is only suitable for experienced users. A further image processing step
generates unwrapped phase maps, which in an engineering environment are generally colour coded for display. For
artwork measurement applications, the colour variation of the painting itself is important reference for the conservator to
locate defect locations. In this manuscript the displacement gradient data is presented as false height on the flat painting
surface. A virtual reality viewer, freely downloadable from the internet, is used to display the data and allow the user to
interact with it by rotating the object in virtual space. The effect is rather similar to viewing a raked light photograph,
however with the advantage of remote or online viewing.
A novel method for the coarse registration of range images is proposed. This approach is based on texture-feature
recognition. As the development of optical digitizing technique, it is now able to acquire the range images and associated
texture images sequentially or simultaneously. It's possible to identify the range feature points through texture feature
points. Scale Invariant Feature Transform (SIFT) is an efficient method for texture feature generation. SIFT transforms
texture image into a large collection of local feature vectors, each of which is invariant to image scaling, translation, and
rotation. The mismatched correspondence pairs can be discarded using random sample consensus algorithm based on
epipolar geometry constraint. We select more than three well-registered texture-feature pairs, with which we could find
the associated range-feature pairs of the range images. Initial pose estimation of the two involved range images can be
computed by these range pairs, and the fine registration is implemented using iterative closest point (ICP) algorithm. Our
approach utilizes the texture information to register the range images, leading to a technique that can be automatically
performed while the influence of 3D noise can be avoided. The experiment results demonstrate that the proposed
approach is efficient and robust for the registration of multiple range images.
With the improvements in range image acquisition by optical metrology of our group, we also developed a novel method
for the registration and integration of range images. The registration approach is based on texture-feature recognition.
Texture-feature pairs in two texture images are identified by cross-correlation, and the validity-checking is implemented
through Hausdorff distance comparison. The correspondence between the texture image and range image helped acquire
the range point-pairs, and the initial transformation of two range images was computed by least-squares technique. With
this initial transformation, the fine registration was achieved by ICP algorithm. The integration of the registered range
images is based on ray casting. An axis-aligned bounding box for all range images is computed. Three bundles of
uniform-distributed rays are cast and pass through the faces of the box along three orthogonal coordinate axes
respectively. The intersections between the rays and the range images are computed and stored in Dexels. The KD-tree
structure is used to accelerate computation. Those data points in overlapped region are identified with specific criteria
based on the distance and the angle of normals. We can obtain a complete non-redundant digital model after removing
the overlapped points. The experimental results illustrate the efficiency of the method in reconstructing the whole three dimensional