Portable optical target measurement systems have widespread applications in various fields, including mechanical manufacturing, aerospace, industrial inspection, and clinical medicine. This study aims to address the marker point matching problem in binocular measurement systems. Through image preprocessing and mathematical algorithms, we have successfully achieved robust matching of marker points with uniqueness. We first converted color images to grayscale and applied the Otsu algorithm to adaptively select a global threshold, successfully extracting marker points. Using the nearest neighbor algorithm, we simplified 28 marker points to 7. Then, we employed the k-means clustering algorithm to achieve a second fitting, obtaining marker points located on the central line of the target. With these marker points, we computed key features for describing the target pose, including the direction vector of the target’s central line and its angle with respect to the image’s x-axis. Finally, we designed different algorithm modules to successfully achieve the robust matching of marker points across 360 degrees of free postures.
Semiconductor packaging lead-frame has the characteristics of high density, high precision, refinement, and miniaturization. However, traditional manual detection has a series of problems such as difficulty, low efficiency, and high miss rate. Aiming at this industry problem, this paper develops a full-scale detection system and corresponding detection method of semiconductor packaging lead frame based on machine vision. The developed system is composed of a motion control platform and system, hardware control systems such as light source camera, and image algorithm platform. Through the optimized visual detection algorithm and the accurate correction method of workpiece attitude under motion measurement conditions, it can realize the one-click adjustment of several key dimensions such as frame length, width, loading area thickness, pin thickness, bending height, and aperture Micron level measurement, high precision, and fast speed, effectively ensure the genuine product rate and control the scrap rate, to assist semiconductor packaging enterprises to save labor costs and improve production efficiency.
Both intensity and phase information of images have been the most important similarity measures in solving the general stereo matching problem. Intensity contains most of the imaging information of the scene/object, yet the phase information could reflect the local structure of images, which is more robust than the grayscale value. Plenty of work has been done in intensity-based or phase-based stereo matching methods. However, neither of them could work well enough when process images were taken under varied illuminations. A robust depth recovery method by making use of both intensity and phase information of stereo images properly is proposed. Firstly, 2D signal analysis is conducted by using the multiscale monogenic wavelet transform, from which local phase and intensity amplitude information are extracted into different scales. Secondly, disparity maps are estimated in different scales based on the intensity information. Thirdly, the optimal disparity is obtained by weighted-combining the disparity maps in different scales. The weighted coefficients are computed by making use of the phase information. Extensive experimental evaluation demonstrates the benefits of the proposed method.
Three-dimensional (3-D) reconstruction of a shiny surface is a great challenge for existing structured light techniques because serious measurement errors will occur in image saturation regions in the captured image. To this end, an adaptive microphase measuring profilometry (AMPMP) is proposed. In this method, an optimal frequency selection strategy is introduced to select the frequencies of phase-shifted fringe patterns. The intensity relation between the projected pattern and the captured image is estimated by fitting a nonlinear function after projecting a series of uniform gray-level patterns onto the shiny surface, and the mapping regions of saturated areas in the captured image are computed with the correspondence between the projector image plane and the camera image plane, which is established through MPMP. Based on the estimated intensity relation and the mapping regions, the optimal projection intensities of saturated areas are adaptively adjusted. Then, the adjusted phase-shifted fringe patterns are projected to obtain the absolute phase distribution of the shiny surface. Therefore, accurate 3-D shape reconstruction is realized. The experimental results demonstrated that the proposed AMPMP can well tackle saturated areas of shiny surfaces and has greater performance than other commonly used adaptive fringe projection techniques.
High dynamic range object has large surface reflectivity variations, which easily produces saturated and low-contrast regions in the captured images, further resulting in large calculation errors of gray value or phase value, then seriously affecting the reconstruction accuracy. An auto-exposure-based structured light technique is proposed. In this method, the relationship between noise-induced phase error and intensity modulation is first analyzed. It is demonstrated that once the intensity modulation of a pixel is larger than a threshold, its phase error can be considered acceptable. Then, the surface reflectivity of the measured target is estimated by projecting a series of uniform gray-level patterns and classified into several groups. The exposure time for each group is automatically determined after establishing its mathematical model based on the modulation threshold. Phase-shifted images are then captured at the computed exposure times, and a set of composite phase-shifted images is acquired by extracting the brightest unsaturated pixels in the raw fringe images. The experiments show that the proposed method can automatically calculate the exposure times and capture the fringe images without human intervention. In addition, it can well tackle saturated and dark areas of the measured target and the reconstructed three-dimensional shape is complete and accurate.
In a structured light-based 3D scanning system, the overall 3D information of to-be-measured objects cannot be retrieved at one time automatically. Currently the 3D registration algorithms can be divided into the auxiliary objects-based method and the feature points-based method. The former requires extra calibration objects or positioning platforms, which limits its application in free-form 3D scanning task. The latter can be conducted automatically, however, most of them tried to recover the motion matrix from extracted 2D features, which has been proved to be inaccurate. This paper proposed an automatic and accurate full-view registration method for 3D scanning system. Instead of using the 3D information of detected feature points to estimate the coarse motion matrix, 3D points reconstructed by the 3D scanning system were utilized. Firstly, robust SIFT features were extracted from each image and corresponding matching point pairs are achieved from two adjacent left images. Secondly, re-project all of the 3D point clouds onto the image plane of each left camera and corresponding 2D image points can be obtained. Filter out correct matching points from all 2D reprojection points under the guidance of the extracted SIFT matching points. Then, the covariance method was adopted to estimate the coarse registration matrix of adjacent positions. This procedure was repeated among every adjacent viewing position of the 3D scanning system. Lastly, fast ICP algorithm was performed to conduct fine registration of multi-view point clouds. Experiments conducted on real data have verified the effectiveness and accuracy of the proposed method.
The defect detection of nonwoven fabrics is one of the most important steps of fabric quality assurance on production lines. For a long time, fabric defects detection has been carried out manually by human vision with an accuracy of about 60–70%, which not only affects the health of the inspectors, but also has high inspection cost. How to automatically detect crease defects of various forms at a high accuracy has been a challenging task in the field of machine vision. At present, Fourier transform and wavelet transform have been adopted to solve this problem. However, both of them can hardly detect stochastic textured in local region from different scales and directions. This paper adopted a 2D Gabor filter-based method to detect the crease defects, which has tunable angular and axial frequency bandwidth, tunable center frequencies, and could achieve optimal joint resolution in spatial and frequency domain. Firstly the fabric crease images are transformed from the spatial domain to the frequency domain. Secondly the frequency domain images are filtered by the Gabor filter with adjustable central frequency, bandwidth and azimuth, and the frequency domain images of the crease pattern are selected in the frequency domain image. Then they are reversed to the spatial domain. Finally the crease area of nonwoven fabric is obtained by the blob analysis. Experiments conducted on various forms of crease defects have shown that by adopting the proposed method, the nonwoven fabric’s crease defects can be detected effectively and accurately.
3D measurement of underwater targets could recover 3D morphology of objects/scenes in water, which has extensive application prospects in the fields of submarine map drawing, underwater resource exploration, and marine archaeology et al. 3D reconstruction based on stereoscopic vision is playing a more and more important role in the field of measurement due to its incomparable advantages, such as high automation, rapid accuracy and non-contact. However, its application in underwater target detection is limited by the complex underwater environment, the absorption and scattering of light in the water and so on, which will seriously affect the quality of the image collection. In this paper, a 3D reconstruction method of underwater target based on multi view stereo vision technology was studied. A 3D profilometry system which works underwater was set up. The collection of multi-view image data is completed by a single camera and a rotating device. Firstly, camera’s back projection model is used to calibrate the motion and parameters of the underwater vision system. Secondly, the underwater target is fixed on the rotating device, and a series of images under different viewpoints are collected. Then, feature detection and matching were carried out, and dense surface point clouds were generated by several steps of expansion and filtering operations. Finally, based on the generated dense point cloud, the 3D geometric mesh model of the target is obtained by using the Poisson reconstruction method. Color and texture are fused into the 3D mesh model to get the target with high fidelity.
With the development of marine economy, the demand of underwater 3D imaging technology has become more insistent. Due to the absorption and scattering of water, the projection distance and the imaging range are shortened, which directly affects the applications and effectiveness of structured light techniques in underwater detection. In this paper, the imaging model of underwater camera is studied. An underwater structured light imaging system is established. A binary coding algorithm is investigated. The experimental results show that the proposed system can achieve a high accuracy measurement and has great potentials in underwater observing and engineering applications.
In this paper, the applications of polarimetric imaging for rust preventing oil film detection and characterization are discussed. A three-channel polarimetric imaging system is introduced, which can obtained the degree of linear polarization images at one shoot. The experimental results show that the proposed three-channel polarimetric imaging system can identify the oil film on the steel strip quickly and effectively, which is a fast and reliable detection method.
This paper proposed to apply the Bi-dimensional Empirical Mode Decomposition (BEMD) to the dense disparity estimation problem. The BEMD is a fully data-driven method and does not need predetermined filter and wavelet functions. It is locally adaptive and has obvious advantages in analyzing non-linear and non-stationary signals. Firstly we decompose the original stereo images by 2D-sifting process of the BEMD respectively. Through this procedure, a serial of Intrinsic Mode Functions (IMFs) and a residue are achieved. The residue denotes the DC component of the signal. Secondly, subtract the residue from original image. The resulting two dimensional signals can be thought of being free of disturbing frequencies, such as noise and illumination components. Subsequently, to obtain robust local structure information of the images, the plural Riesz transformation is utilized to achieve corresponding 2D analytic signals of the images. Thirdly, extract local phase information of the analytic signals. The similarity of local phase of stereo images, instead of local intensity information, are taken as the basis of calculating matching cost, which could reveal local structure with more robustness. At last, dense disparity map is estimated based on the proposed method. The winnertakes-all (WTA) strategy is applied to compute disparity of each pixel separately. Comparative experiment is conducted to compare the performance of the method with intensity-based methods. Rather good results have been achieved.
The handheld target greatly expands the fields of the vision measurement systems. However, it introduces extraction errors and position errors, which degrades the positioning precision of the vision measurement systems. In order to evaluate the influence of the handheld targets on the accuracy of T-VMS, we first analyzed the positioning principle of the visual measurement system and established the precision model under two typical structures of the T-VMS. We then studied the extraction errors and position errors introduced by the handheld targets and quantified the errors. Finally, we discussed the influence of the said errors on the positioning in 3D space with system precision model. We applied the precision model in an actual T-VMS to confirm its feasibility and effectiveness, and found that it indeed estimate the errors introduced by the handheld targets effectively.
The global stereo matching algorithms are of high accuracy for the estimation of disparity map, but the time-consuming in the optimization process still faces a curse, especially for the image pairs with high resolution and large baseline setting. To improve the computational efficiency of the global algorithms, a disparity range estimation scheme for the global stereo matching is proposed to estimate the disparity map of rectified stereo images in this paper. The projective geometry in a parallel binocular stereo vision is investigated to reveal a relationship between two disparities at each pixel in the rectified stereo images with different baselines, which can be used to quickly obtain a predicted disparity map in a long baseline setting estimated by that in the small one. Then, the drastically reduced disparity ranges at each pixel under a long baseline setting can be determined by the predicted disparity map. Furthermore, the disparity range estimation scheme is introduced into the graph cuts with expansion moves to estimate the precise disparity map, which can greatly save the cost of computing without loss of accuracy in the stereo matching, especially for the dense global stereo matching, compared to the traditional algorithm. Experimental results with the Middlebury stereo datasets are presented to demonstrate the validity and efficiency of the proposed algorithm.
The measurement accuracy of phase shifting shadow moiré is affected by both the spatially nonuniform phase shift error and random phase shift error. Substantial work has been done to overcome this difficulty, but few works can deal with the two error sources simultaneously. In the presented paper, we describe a solution that can compensate for the two error sources at the same time. In our method, a binocular stereovision system is integrated into a shadow moiré configuration to calibrate the setting parameters. Thus, a precise measurement setup is built. Then, we developed an iterative least-squares algorithm to analyze the acquired three fringe patterns. The proposed algorithm can extract the measurement phase without nonuniform phase shift error. On the other hand, it can compensate the random phase shift error by calibrating the distance of grating translation using the least-squares fitting in spatial. Optical experiments show that the proposed method can effectively minimize the phase-shift error and that it possesses a superior performance than the existing typical phase shifting algorithm.
A ball-based intermediary target technique is presented to position moving machine vision measurement system and
to realize data registration under different positions. Large-sized work-piece measurement based on machine vision faces
several problems: limited viewing angle, range and accuracy of measurement inversely proportional. To measure the
whole work-piece conveniently and precisely, the idea that using balls as registration target is proposed in this paper.
Only a single image of the ball target is required from each camera then the vision system is fully calibrated (intrinsic
and extrinsic camera parameters). When the vision system has to be moved to measure the whole work-piece, one
snapshot of the ball target in the common view can position the system. Then data registration can be fulfilled. To
achieve more accurate position of ball’s center, an error correction model is established.
Sub-aperture stitching (SAS) testing method is an effective way to extend the lateral and vertical dynamic range of a
conventional interferometer. However, the center of each sub-aperture could be in error because of the complex motion
of the mechanical platform. To eliminate the affection of lateral location error in the final stitching result, a lateral
location error compensation algorithm is introduced and the ability of the algorithm to compensate the lateral location
error is analyzed. Finally, a 152.4mm concave parabolic mirror is tested using SAS method with the compensation
algorithm. The result showed that the algorithm can effectively compensate the lateral location error caused by the
mechanical motion. The proposal of the algorithm can reduce high requirement of mechanical platform, which provides
a feasible method for the practical application of the engineering.
The measurement accuracy of phase shifting shadow moiré is limited by the spatially non-uniform and random phase shift error. Substantial work has been developed to overcome this difficulty. But few works are proposed to deal with the two error sources above simultaneously. In the presented paper, we describe a solution that can compensate the both error sources at the same time. In our proposed method, a binocular stereovision system is integrated into our test configuration. By measuring the coordinates of marks attached to the measurement grating, the stereovision system obtains the position and the setting parameters are calibrated. Then, the acquiring three fringe patterns are analyzed by iterative least squares method in temporal and the phase shift is calibrated by the least squares fitting in spatial. Because a local cost function is used, the proposed calibration technique is insensitive to spatial variations in detector response. Numerical simulations and optical experiments show that the proposed method can effectively minimize the two phase-shift error sources and possess a superior performance than the existing typical phase shifting algorithm.
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