Sub-pixel Center Research Articles

Probability distribution maps of deposited energy with sub-pixel resolution for Timepix3 detectors

The Medipix3 Collaboration has developed the Timepix3 chip, which can provide information about individual particles interacting with a hybrid pixelated semiconductor detector. The Timepix3 generates a data packet including time and energy information for each pixel receiving a charge above the discriminator threshold. From a set of such packages generated by one particle the total deposited energy and the sub-pixel center of mass (c.m) position of the interaction can be calculated. However, sub-threshold losses will distort both the measured energy and c.m calculation. This work aims to generate probability maps for the most probable initial interaction position and energy for a given detected cluster. The correction maps were calculated for different energies and sub-pixel c.m positions. It is crucial to realistically simulate the front-end response of the Timepix3 detector to be able to match and understand the experimental data and generate accurate probability maps. The correction maps from the simulations using the simulation tool Allpix 2 are applied to experimental data utilizing an Am-241 source to irradiate a Minipix Timepix3, leading to sub-pixel resolution and improved energy resolution for Timepix3 detectors.

Displacement Measurement of Shaking Table Experiments Based on Binocular Vision and Subpixel Optimization

The shaking table experiment is a very useful tool to assess the seismic performance of structural models. However, there are many shortcomings in traditional displacement measurement methods. In this research, a binocular vision algorithm for accurately extracting the subpixel center of the circular target based on elliptic fitting is proposed. Firstly, the images are collected by two high-speed cameras and pre-processed to enhance their brightness and contrast. Then, an improved subpixel optimization method based on the Zernike moments and Canny operator is adopted to get accurate localization of extracted target points’ three-dimension coordinates. After comparing the displacement of target points with laser sensors, the results show that the proposed algorithm achieves high-precision displacement measurements and overcomes the problems that traditional methods hold.

Calibration Method for Line-Structured Light Three-Dimensional Measurement Based on a Simple Target

Line-structured light three-dimensional (3D) measurement is apt for measuring the 3D profile of objects in complex industrial environments. The light-plane calibration, which is a crucial link, greatly affects the accuracy of measurement results. This paper proposes a novel calibration method for line-structured light 3D measurement based on a simple target, i.e., a single cylindrical target (SCT). First, the line-structured light intersects with the target to generate a light stripe. Meanwhile, the camera captures the calibration images, extracts the refined subpixel center points of the light stripe (RSCP) and detects the elliptic profiles of the two ends of the cylindrical target (EPCT). Second, two elliptical cones defined by the EPCT and the camera optical center are determined. Combining the two defined elliptical cones and the known radius of the SCT, we can uniquely solve the axis equation of the SCT. Finally, because the coordinates of RSCP in the camera coordinate system fulfill both the camera model and the cylindrical equation, these coordinates can be solved and used to obtain the optimal solution of the light-plane equation. The results of simulations and experiments verify that our proposed method has higher accuracy and effectiveness.

Accurate stereo vision system calibration with chromatic concentric fringe patterns.

Camera calibration is used to determine the intrinsic and extrinsic parameters of a 3D imaging system based on structured light. Traditional methods like chessboard and circular dots usually employ an intensity-based feature point detection procedure, and are susceptible to noise, image contrast, and image blur. To address these issues, we proposed an active calibration method to accurately detect the centers of chromatic concentric fringe patterns (CCFP). Specifically, we first acquired the circular phase using a phase analysis algorithm, then extracted nine phase contours from the circular phase for the corresponding subpixel center coordinates using an ellipse fitting algorithm, and precisely calculated the final center with their weighted sum. We ran a simulation and evaluated the impacts of different degrees of Gaussian blur and noise on the calibrated parameters. The simulation demonstrates that our approach is more robust to noise and blur than previous ones, and our approach yields a higher calibration accuracy. Moreover, we carried out a comparison experiment to evaluate the performance of our method. It showed that the reprojection error can be reduced by at least 10% in the out-of-focus condition (i.e., the target is beyond the working distance of the camera) and the 3D reconstruction accuracy can be improved by nearly 10%.

Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern

Optical metrology has experienced a fast development in recent years—cross laser-pattern has become a common cooperative measuring marker in optical metrology equipment, such as infrared imaging equipment or visual 3D measurement system. The rapid and accurate extraction of the center point and attitude of the cross-marker image is the first prerequisite to ensure the measurement speed and accuracy. In this paper, a cross laser-pattern is used as a cooperative marker, in view of the high resolution of the cross laser-pattern image in the project and the vulnerability to adverse environmental effects, such as stray light, smoke, water mist and other interference in the environment, resulting in poor contrast, low signal-to-noise ratio (SNR), uneven energy distribution. As a result, a method is proposed to detect the center point and attitude of cross laser-pattern image based on Gaussian fitting and least square fitting. Firstly, the distortion of original image is corrected in real time, the corrected image is smoothed by median filter, and the noise is suppressed while preserving the edge sharpness and detail of the image. In order to adapt to different environments, the maximum inter-class variance method of threshold automatic selection is used to determine the threshold of image segmentation to eliminate the background interference caused by different illumination intensities. To improve the real-time performance of the algorithm, the four cross laser edge pixels are obtained by line search, and then fitted by least square. With the edge lines, the transverse and portrait line of the cross-laser image are separated, then we calculate Gaussian center points of all Gaussian sections of transverse and portrait lines based on Gaussian fitting method, respectively. Based on the traditional line fitting method, the sub-pixel center of the transverse and portrait laser strip images are fitted by removing the Outlying Points, and the center coordinates and attitude information of the cross laser-pattern are calculated by using the center equation of the laser strip, realizing cross laser-pattern center and attitude accurate positioning. The results show that the method is robust, the center positioning accuracy is better than 0.6 pixels, the attitude positioning accuracy is better than ±15” under smoke and water mist environment and the processing speed is better than 0.1 s, which meets the real-time requirements of the project.

Topological similarity measurement: a methodology for fast full-field deformation estimation

The correlation-based image subset matching methods such as digital image correlation (DIC) are widely used for measuring the deformation fields. The specimen surface must be covered with speckle patterns, or more exactly, random intensity distributions with sufficient contrast, which act as a carrier of deformation information, to acquire reliable and accurate matching in the correlation calculation. The requirement of the artificial speckles on the specimen surface inevitably contaminates the specimen. To maintain the cleanliness of the specimen surface, an in situ deformation estimation method without speckle fabrication is developed and its result is demonstrated. Topological similarity measurement (TSM) is a feature vector-based displacement estimating algorithm for full-field deformations that uses feature vectors that encode the relative positions of neighboring mark spots. Compared with the DIC method, this approach makes up for its deficiency in displacement estimation of speckle-free flat surface by incorporating a topological arrangement-based feature vector, subpixel center location method, and biharmonic spline interpolation method. Experimental results indicate that the proposed TSM method is feasible to become the preferred displacement estimation method for uniform deformation. As a complement to the DIC method, the TSM method is more suitable for uniform deformation distribution, which requires high measurement accuracy but cannot use DIC.

Sub-pixel Center Extraction Method for Line structured Light Stripe

The extraction of line structured light center is a key technology in line structured light measurement system. The precision of light stripe extraction affects the accuracy of the measurement system. Due to the differences of the materials and quality of the laser device, the light stripes is variable in different position that the energy distribution and morphological characteristics are easily changed. It is difficult to extract the center of the stripes by using traditional light stripe extraction methods accurately. Our study proposes an extraction algorithm for the light stripe center. First of all, separating a single stripe from the image according to the morphological characteristics of the light stripe, and the effective region of the light stripe was initially determined. Then, using the grayscale attribute method as well as the grayscale threshold method to determine the region of interest in the center of the light stripe. Finally, the sub-pixel center of the light stripe in the regions of interest was calculated by the grayscale barycenter method. The experimental results showed that the algorithm in the study can be used to extract light stripes with changeable characteristics. It is more accurate and stable comparing with the traditional extraction algorithm of light stripe center.

Sub-pixel extraction of laser stripe in complex background

The complex background and laser stripe noise affect laser stripe extraction. Adaptive double threshold segmentation method and the improved gray weight model are proposed in this study. First, the characteristics of the laser stripe and the source of noise in the image are investigated. Bilateral filter is applied to remove the noise of images. Subsequently, the gray histogram of laser image and the double threshold are computed. By sub-regional processing, initial stripe center and stripe width of binary images are obtained. Finally, the sub-pixel center of the laser strip is extracted by the proposed model. The double threshold segmentation method and the improved gray weight model are compared with the traditional algorithms. The results show that the double threshold method is more accuracy in extracting the laser stripe region than the extreme value method and the Otsu method. Comparing with the residual value of sub-pixel center, the improved gray weight model (0.23) has better results than the gray-gravity method (0.71), the extreme value method (0.86), and the Gaussian fitting method (0.86). The algorithms proposed in this study avoid the impacts of complex background and laser stripe noise, increase the accuracy of the laser stripe center positioning and extract the stripe center extraction fast and accurately in complex backgrounds.

The Light Plane Calibration Method of the Laser Welding Vision Monitoring System

According to the aerospace and automobile industry, the sheet steels are the very important parts. In the recent years, laser welding technique had been used to weld the sheet steel part. The seam width between the two parts is usually less than 0.1mm. Because the error of the fixture fixed can’t be eliminated, the welding parts quality can be greatly affected. In order to improve the welding quality, the line structured light is employed in the vision monitoring system to plan the welding path before welding. In order to improve the weld precision, the vision system is located on Z axis of the computer numerical control (CNC) tool. The planar pattern is placed on the X-Y plane of the CNC tool, and the structured light is projected on the planar pattern. The vision system stay at three different positions along the Z axis of the CNC tool, and the camera shoot the image of the planar pattern at every position. Using the calculated the sub-pixel center line of the structure light, the world coordinate of the center light line can be calculated. Thus, the structured light plane can be calculated by fitting the structured light line. Experiment result shows the effective of the proposed method.

Development of a modeling method for monitoring tunnel deformation based on active panoramic vision technique

Aiming at the characteristics of long-distance tunnel, long maintenance intervals, limited repairing time and small changes in the deformation data, this paper presents a detection method of tunnel deformation based on active omni-directional vision tunnel section deformation. Firstly, the active panoramic vision sensor (ASODVS) is installed at the tunnel and detecting device scans the cross section of tunnel to acquire tunnel section panoramic images. Secondly, the sub-pixel center of the panoramic image is extracted by the improved Gauss curve fitting method, and the smooth processing is performed by using the Bezier curves. Thirdly, the system analyzes the geometry information of the tunnel cross-section of the inner wall through calibration results of omnidirectional vision sensor. The tunnel cross section 3D point cloud data are used for 3D reconstruction. Finally, the precision of the tunnel reconstruction model is analyzed. The experimental results show the method has the advantages of high speed acquisition, real-time, comprehensive data and good visualization. It can meet the needs of the rapid qualitative and quantitative analyses.

结合分层处理的激光光条亚像素中心提取方法

A sub-pixel center extraction algorithm based on hierarchical processing was proposed for the implementation of laser stripe processing in the large aerospace parts measurement with high speed and precision. Firstly, the high-resolution image was compressed into low-resolution image with structural invariance. Then, the normal vector of the center of laser stripe was calculated by using quadratic curve fitting in the low-resolution image. The normal vector in the low-resolution image was reproduced to the high-resolution image based on the theory of normal regression. The judgment of gray center on normal direction was established to determine calculated pixels. The sub-pixel center of calculated pixels on the normal direction was accurately calculated by the gray weighted centroid method. Finally, the presented method was utilized to measure a composite material standard part and complex aviation parts in the laboratory and assembly testing machine, respectively. Experimental results show that the precision of the reconstruction of a single stripe on the surface of object is 0.269 mm and the precision of three-dimension surface measurement is 0.268 mm. It indicates that the proposed method can improve the speed and precision of the engineering measurement of large part. Moreover, the method can satisfy the requirements of the in-site measurement of large aerial parts.

Extraction of laser stripe subpixel center in highnoise background

Extraction of laser stripe subpixel center in highnoise background

线结构光光条中心亚像素精确提取方法

线结构光光条中心亚像素精确提取方法

Fast star centroid extraction algorithm with sub-pixel accuracy based on FPGA

Spacecraft's attitude information plays an important role in celestial navigation. The attitude is mainly determined by matching the star's centroid in the obtained image with its corresponding information in star catalog. Generally, the star image can be regarded as a spot with a diameter <5 pixels. Therefore, it is very difficult to extract the star centroid with sub-pixel accuracy, especially in the hardware system, such as FPGAs. The existing spot centroid extraction methods with high accuracy require plenty of pixels to realize the complex computations. Limited to the star's diameter and hardware requirements, such methods are not suitable for star centroid extraction in hardware system. To solve the problem, a two-step extraction method for star centroid with sub-pixel accuracy is proposed. The maximum pixel-level center can be located through zero crossing of the first derivative in a small region. Taking the pixel-level center as the middle of the window with fixed size, the sub-pixel offsets to the sub-pixel center can be calculated using fixed window weighted centroid method. The sub-pixel center of the star is then obtained by adding the offsets to the pixel-level center. This method can be implemented in hardware to increase processing speed, using Verilog hardware description languages. A simulation is performed on computer and FPGA. Experimental results show the excellent performance in accuracy and processing speed of two-step method. In addition, two-step method has strong ability of resisting noise and good robustness compared to other methods.

A Sub-Pixel Extraction Method for the Center of the Circular Structure from Spot Array Calibration Target

The imaging measurement system must be calibrated before application. In calibration procedure, sub-pixel center extraction is the crucial step for the final accuracy. In this paper, a new sub-pixel extraction method is proposed. Edge detection at pixel level is obtained using LOG operator, then sub-pixel edge detection is realized using the Facet model. Finally, the least squares ellipse fitting is operated on the sub-pixel edge to determine the sub-pixel center. Experiment on a series of simulated images indicates that this algorithm is able to realize the center location accuracy of 0.01-0.02 pixel, and meets the requirement of sub-pixel level.

Research on a New Technique of No Mark Overprint

There are some problems with traditional mark replacement and accurate detection of overprint deviation, this paper presents a new method which can detect the parameter of the no mark overprint. The method is based on the basis of mathematical morphology to determine the sign of the multi-structural elements and multi-scale morphological edge, by using detection algorithm to extract image edge; and then the image edge was fitted by using least square fitting algorithm, so the sub-pixel center coordinates of the overprint mark can be obtained; Finally, accurate data of the overprint deviation can be obtained by using deviation operation formula. Theoretical analysis and experimental results show that the overprint deviation detection has high precision, which can help to improve the accuracy, real-time and automatic control of overprint. So it can ensure that the whole picture's tone and hue of printing image can be reproduced accurately.

On-Machine Inspection Method for Superduty Lathe Based on Improved Laser Triangulation

In the development and application of superduty lathe process, the size measurement of a large workpiece is a key issue of quality control because the cutting process of large workpiece is high linear speed and time-consuming . A novel surface position information acquisition method for rotational object is proposed. The method uses high speed linear CCD to acquire image sequences, adopts image restoration technique to resolve clear images of laser spot from the image sequences, identifies sub-pixel center point of laser spot with correlation information among the clear frame-to-frame images, then resolves surface position information of rotational object with triangulation principle. Therefore, defects of existing laser triangulation for high-speed rotational object measurement are overcome. According to the resolved surface position information of rotational workpiece, the stability of accuracy, tool wear and vibration characteristics of superduty lathe can be evaluated.

Method of Physical-based Computer Vision Detection for Tread Surface Flats of Wheelsets

The method based on computer vision for fast detecting the tread surface flats of railway wheelsets is presented. The line structured light is used to scan the tread surface of wheelsets, thus producing the corresponding deformation light stripe at where it scans. To detect the tread surface flats of wheelsets, the light stripe images collected by the charge coupled device(CCD) camera are processed to get the outline of the tread surface of wheelsets. Based on the characteristics of the light stripe, a new method based on physical model for extracting the center line of light stripe is proposed. Using the energy optimization method, the sub-pixel B-spline center of light stripe can be found when energy is minimized, while greatly reducing the volume of data storage. The proposed approach extracts strips central points at subpixel accuracy successfully and restrains noise, broken lines and divarication efficiently. The center line of light stripe is with a single connectivity. With the rotation of wheelsets, the three-dimensional surface model can be obtained through camera model, thereby realizing non-contact detection of the tread surface flats of wheelsets.

Complete approach to automatic identification and subpixel center location for ellipse feature

To meet the need of automatic image features extraction with high precision in visual inspection, a complete approach to automatic identification and sub-pixel center location for similar-ellipse feature is proposed. In the method, the feature area is identified automatically based on the edge attribute, and the sub-pixel center location is accomplished with the least-square algorithm. It shows that the method is valid, practical, and has high precision by experiment. Meanwhile this method can meet the need of instrumentation of visual inspection because of easy realization and without man-machine interaction.