Center Extraction Method Research Articles

An Efficient Scattering Center Extraction Method Based on Stationary Phase Method With Quadratic Patch Models

An Efficient Scattering Center Extraction Method Based on Stationary Phase Method With Quadratic Patch Models

Center extraction method for reflected metallic surface fringes based on line structured light.

Using line structured light to measure metal surface topography, the extraction error of the stripe center is significant due to the influence of the optical characteristics of the metal surface and the scattering noise. This paper proposes a sub-pixel stripe center extraction method based on adaptive threshold segmentation and a gradient weighting strategy to address this issue. First, we analyze the characteristics of the stripe image of the measured metal's surface morphology. Relying on the morphological features of the image, the image is segmented to remove the effect of background noise and to obtain the region of interest in the image. Then, we use the gray-gravity method to get the rough center coordinates of the stripes. We extend the stripes in the width direction using the rough center coordinates as a reference to determine the center of the stripes for extraction after segmentation. Next, we adaptively determine the boundary threshold utilizing the region's grayscale. Finally, we use the gradient weighting strategy to extract the sub-pixel stripe center. The experimental results show that the proposed method effectively eliminates the interference of metal surface scattering on 3D reconstruction. The average height error of the measured standard block is 0.025mm, and the repeatability of the measurement accuracy is 0.026mm.

An Efficient Attributed Scattering Center Extraction Method Accelerated by Dictionary Reuse

An Efficient Attributed Scattering Center Extraction Method Accelerated by Dictionary Reuse

Image‐based crop row detection utilizing the Hough transform and DBSCAN clustering analysis

AbstractMore accurate methods for crop row detection benefit intelligent operation of agricultural machinery, especially avoiding mishandling or crushing crops. For achieving such a target, a traditional method combining the ExGR exponents, Otsu algorithm, Canny method, Hough transform and DBSCAN clustering analysis is proposed so that centerlines of crop rows can be detected effectively without manual intervention. Specifically, ExGR exponents are first adopted to gray green plants. The threshold of binarization will be further obtained by the Otsu algorithm. Further adopting the edge detection algorithm (Canny), edges of crops can be determined. Finally, combining the Hough transform and DBSCAN clustering analysis, the crop row detection is effectively available. Utilizing these methods, numerical simulation and their comparisons with existing methods are also achieved. For example, the Canny algorithm is relatively accurate than the Suzuki algorithm as well as their combinations with a geometric center extraction method if the density of weed is high. Compared with the K‐means clustering method, the DBSCAN algorithm is more suitable to characterize crop rows optimally in more complex conditions. It is validated from experiments that the combination of Canny algorithm, Hough transform and DBSCAN clustering is better than other mentioned traditional methods.

A Robust Scattering Center Extraction Method and Its Application in InISAR Imaging

A Robust Scattering Center Extraction Method and Its Application in InISAR Imaging

Laser stripe center extraction method base on Hessian matrix improved by stripe width precise calculation

The line laser measurement technology based on triangulation is widely used in the field of complex surface topography measurement. The key technology of line laser measurement is laser stripe center extraction. For high reflective surfaces, due to the low surface roughness, some areas of the laser stripe image are overexposed, resulting in uneven stripe width distribution, asymmetric light intensity distribution of the stripe cross section, and difficulty in extracting the center of the stripe. In this paper, Laser stripe center extraction method based on Hessian matrix method improved by stripe width is proposed. Firstly, the laser stripe image is preprocessed, the influence of noise in the image is alleviated by image filtering and adaptive threshold segmentation, and the region of interest of the image is extracted to reduce the amount of subsequent calculation. The laser stripe width is calculated based on BPNN and divided into different width intervals. The Gaussian kernel is calculated according to the light stripe width and the Hessian matrix is constructed. The Hessian matrix method is used to roughly extract the center coordinates of the light stripe. Based on the continuity of the light stripe, the center coordinates of the light stripe are weighted and corrected to realize the smooth optimization of the center coordinates of the light stripe. The experimental results show that the proposed light stripe center extraction method is superior to the traditional method under different surface roughness and exposure time conditions, which can effectively improve the measurement accuracy of the sensor.

Super-resolution image restoration for microlens array imaging system

Microlens array (MLA) has been widely applied in optical imaging fields. However, the existing MLA imaging system (MLAIS) often suffers the problems of low imaging resolutions and luminance contrast due to the limitation of the sensor size and microlens aperture. To address these problems, we propose a super-resolution (SR) image restoration method for the microlens array imaging system (MLAIS). The proposed method uses an Otsu-Kmeans gravity-based center extraction method to segment the image captured by MLAIS into many sub-images and an enhanced correlation coefficient-based approach to align the sub-images. A maximum a posteriori probability algorithm is developed to recover the SR image from the aligned sub-images. A two-step gray transformation algorithm is designed to enhance the SR image. Numerical simulations and practical experiments are implemented to verify the effectiveness of the proposed method, and its SR restoration performance is evaluated by comparing the results with classical and state-of-the-art methods. The results indicate that the proposed method can achieve high-quality SR image restoration for the MLAIS, and its SR performance is better than the comparison methods. The proposed method may find applications in multispectral SR imaging, remote sensing SR imaging, muti-sensor SR imaging, and so on.

Narrow Weld Joint Recognition Method Based on Laser Profile Sensor

Intelligent manufacturing technology has recently experienced rapid development, where robot welding systems are an essential component. A laser profile sensor is commonly utilized in intelligent robot welding systems. However, when the laser stripe is projected onto a narrow weld joint, the deformation of the laser stripe is not sufficiently apparent, and the traditional weld joint recognition methods based on the laser profile sensors cannot detect the narrow weld joint position. Meanwhile, the strong arc and spatter noise during welding and the expansion of weld joint width caused by welding thermal expansion are likely to interfere with weld joint recognition. In addition, many common weld seam recognition methods can only recognize certain weld joints and have limited flexibility and robustness for other types of weld joints. This study proposes a wide range of weld joint recognition algorithms based on the laser profile sensor to overcome these challenges. The gray value histogram analysis method, the adaptive threshold method, the internal propulsion center and boundary extraction (IPCBE) method, the coordinate analysis method, and the stripe end center detection method proposed in this study are used primarily in the algorithm. The algorithm solves the problem of identifying narrow weld joints and allows for the identification of larger conventional weld joints. Furthermore, the algorithm has good a anti-spatter and anti-arc interference ability and a fast speed, allowing it to meet the demand for real-time welding identification.

A real-time laser stripe center extraction method for line-structured light system based on FPGA

A real-time laser stripe center extraction method for line-structured light system based on FPGA

Otsu-Kmeans gravity-based multi-spots center extraction method for microlens array imaging system

We propose an Otsu-Kmeans gravity-based center extraction method for multi-spot images captured by the microlens array imaging system. We develop a 4-connected-region-based region of interest location method to recognize and segment light spot areas. We design an Otsu-Kmeans gravity-based threshold selection algorithm to realize the multi-spot center accurate position. Both numerical and actual experiments are implemented. The effectiveness and feasibility of the proposed method are validated by comparing with the results from the traditional centroid method, the Hough circle method, the Gaussian fitting method, and the Otsu thresholding centroid method. The results show that the proposed method can realize high-precision positioning and can be used for the sub-image segmentation of the microlens array imaging system.

A ball-shaped target development and pose estimation strategy for a tracking-based scanning system

This paper describes a pose measurement and tracking strategy for an automatic tracking-based scanning system through developing a ball-shaped target. The target structure is introduced. Then, to determine the target pose, a coded marker and accurate center extraction method is proposed using subpixel and cross-ratio invariance principle. Furthermore, the target coordinate system is established by vision-based target calibration. A confidence-based tracker is proposed, enabling higher tracking confidence and accuracy, and keeping a high tracking speed. The stereo vision estimates the 3D position and orientation of the moving target in real-time. The trajectory is smoothed dynamically based on the Kalman filter. Experiments are implemented to verify the tracking speed, accuracy, and spatial accessibility in robot tracking applications. Additionally, the proposed target and strategy have good performance in full-space and fall-orientation tracking.

Preliminary Study on Structured Light Strip Center Extraction Method Based on Underwater Structured Light Scanning System

Preliminary Study on Structured Light Strip Center Extraction Method Based on Underwater Structured Light Scanning System

Attributed Scattering Center Extraction Method for Microwave Photonic Signals Using DSM-PMM-Regularized Optimization

Attributed Scattering Center Extraction Method for Microwave Photonic Signals Using DSM-PMM-Regularized Optimization

Robot line structured light vision measurement system: light strip center extraction and system calibration

We investigate the light strip center extraction and system calibration method of a robot line structured light vision measurement system to realize fast on-line measurement of complex objects. First, the model of a robot vision measurement system is established to analyze the key technologies involved for improving the measurement accuracy of the system. Second, on the basis of the traditional gray gravity method, a gray gravity method based on the normal direction is proposed to extract the center of the laser stripe. Considering the influence of camera distortion, the accuracy of extracting the center stripe can be further improved. Third, a two-dimensional checkerboard is applied to synchronously calibrate the camera parameters, laser plane parameters, and hand-eye relationship of the robot, thus improving the efficiency and accuracy of system calibration. Finally, three-dimensional topography and the geometric dimensions of a complex part are measured by the constructed system with high accuracy. The experimental results show that the average measurement error is <0.2 mm and the measurement accuracy is <1.3 % , indicating that the system can be effectively used in actual industrial measurement.

Adaptive center extraction method for reflective surface stripes of nuclear fuel rods

Adaptive center extraction method for reflective surface stripes of nuclear fuel rods

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.

Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

Ellipse center extraction is the important basis of camera calibration with circle arrays calibration board, which directly affects the measurement accuracy of machine vision system. Aiming at the problem of high noise sensitivity of the most ellipse center extraction methods, a high robust multi-ellipse center extraction method is proposed. The idea is to transform the problem of multi-ellipse center extraction into the problems of ellipse sub-pixel edge segmentation and edge based ellipse center clustering. Firstly, the flowchart of the proposed method is introduced. Then, the theory of fuzzy quotient space is introduced to establish the dynamic granularity matrix space model, which describes image segmentation problem as the jumping and transformation of the image by hierarchical structure. Finally, an adaptive multi-ellipse sub-pixel edge segmentation method and an optimal ellipse center extraction method are proposed based on the dynamic granularity matrix space model. Experimental results show that the proposed method has higher accuracy, robustness and lower camera calibration error.

基于椭圆拟合的多光斑/重叠光斑中心提取方法

基于椭圆拟合的多光斑/重叠光斑中心提取方法

Improving the extracting precision of stripe center for structured light measurement

The accuracy of the stripe center extraction is affected by the uneven reflectance and large curvature change of the object surface in the structured light three-dimensional measurement system. Aiming to eliminate the stripe distortion and improve the accuracy of stripe center extraction, an improved stripe center extraction method is proposed for structured light measurement. By analyzing the principle of stripe distortion and the types of stripe noises, the mean filtering is used to eliminate the noises. Then, a stripe preprocessing method is presented to adjust the gray scale of stripe distortion. Finally, stripe centers are extracted through the curve fitting method by preprocessing. Furthermore, the proposed method addressed the problem of local discontinuity of the stripe center caused by the unevenness of brightness in the measurement of complex surface profiles. The proposed method has been validated and verified by experiments. A high accuracy in the extraction of strip centers could be obtained.

Development of a novel line structured light measurement instrument for complex manufactured parts.

This paper studies a novel structured light measurement instrument for complex manufactured parts. In this research, a quick and accurate light plane calibration method and a novel light strip center extraction method were used to establish the measurement instrument. First, the intrinsic and external parameters of the camera are calibrated by Zhang's method. Second, a novel light plane calibration technique is proposed by using the original parameters and images in the process of camera calibration, which can be used to improve the accuracy of the measurement system because both the camera calibration and light plane calibration are based on the same target. Third, a subpixel extraction method is presented to extract the light strip center on the basis of the geometric center method. Finally, the constructed instrument is used to detect the geometric information of the complex manufactured parts. The experimental results show that the maximum measurement error is 0.1 mm under a measurement range of 50 mm and the accuracy is 0.2%, which indicates that the measurement instrument is fast, robust, and accurate enough to be applied to many kinds of measurements, such as railroad track deformation, the inner wall of a pipe, and so on.