Invalid Points Research Articles

Spot Invalid Point Repair Algorithm of Detector Array Measurement System Based on Image Correlation Coefficient

The detector array method has been widely used in the field of high-energy laser far-field spot parameter measurement due to its ability to directly measure the far-field spot of high-energy lasers, wide dynamic range of the detectors, high system sampling frequency, good real-time performance, and suitability for various testing environment requirements. However, during the measurement process, the irradiation of strong lasers or damage to other hardware systems can result in invalid points in the acquired spot images, thereby reducing the measurement accuracy of the system. In order to achieve accurate measurement of laser far-field spot parameters, this paper establishes an experimental model based on the analysis of the sampling spacing of the detector array target and proposes a laser far-field spot invalid point repair algorithm based on image correlation coefficient. Experimental results demonstrate that the algorithm proposed in this paper effectively reduces the impact of invalid points in the measurement system on the measurement accuracy, and achieves accurate measurement of high-energy laser measurement systems.

A Markov random field based method for removing invalid unwrapping phase points in 3D reconstruction

AbstractFringe projection profilometry is widely used in 3D structured light due to its fast speed and accuracy. However, in the process of phase unwrapping, it is easy to cause invalid points in the edges and shadows of objects, which leads to error points in 3D reconstruction. To solve this problem, we propose an invalid points removal method based on Markov random fields. Specifically, the proposed method formulates unwrapped phase and mask maps as energy functions and uses iterative methods to minimize them. Furthermore, we validate the proposed method in a monocular structured light system and compare it with existing algorithms. Results show that the proposed method effectively identifies edges and shadows while preserving valid points, and has strong robustness and correctness.

Effect of Probe Lifting Height in Jumping Mode AFM for Living Cell Imaging

Atomic force microscopy (AFM) is one of the effective methods for imaging the morphological and physical properties of living cells in a near-physiological environment. However, several problems caused by the adhesion of living cells and extension of the cell membranes seriously affect the image quality during living cell imaging, hindering the study of living cells. In this work, jumping mode AFM imaging was used to image living cells at varied probe lifting heights to meet image quality requirements, and image quality related to the probe lifting height is discussed in detail. The jumping mode was divided into three parts based on the varying heights of the lifted probe, namely near-contact mode, half-jumping mode, and full-jumping mode, and the causes of their imaging drawbacks were analyzed. At an appropriate lifting height, the probe can be completely free from the influence of cell adhesion and self-excited oscillation, thus avoiding the occurrence of “trail” phenomena and invalid points in the imaging of living cells and improving the image quality. Additionally, this work provides a new approach to calculating the lateral force through the adhesion of trace and retrace scanning at a low height, which is important for studying the extension characteristics of the cell membrane.

Generic saturation-induced phase-error correction algorithm for phase-measuring profilometry

Intensity saturation causes partial incorrect intensities in captured images, leading to obvious phase errors in high-dynamic-range phase-measuring profilometry. Most existing methods require numerous projected patterns or additional hardware equipment to retrieve the three-dimensional shape. This paper proposes a comprehensive saturation-induced phase-error correction method by combining an average-phase compensation method, applying four-step phase-shifting (PS) patterns, with a phase repair method employing a total variation minimization (TVM) model. The periodic characteristic of the saturation-induced phase error is analyzed. The phase error can be efficiently compensated by averaging the initial and auxiliary phase, which is calculated utilizing a set of PS patterns with a phase offset of /4. Furthermore, a judgment condition is provided to detect invalid points in overexposed shiny areas where the initial calculated phases are wrong. The corrected phases are repaired utilizing the TVM model from the compensated phase information surrounding invalid points. Simulations and experiments show that the proposed method can simultaneously correct the phase in non-uniform, high-reflectivity scenes and shiny areas with high accuracy using relatively few images. The phase error is reduced by nearly 80%.

Fast and accurate centerline extraction algorithm for a laser stripe applied for shoe outsole inspection.

Line laser 3D reconstruction technology is widely used in industrial applications. As a key step of this technology, line laser midline extraction directly affects the accuracy of the 3D reconstructed model. In reconstructing the shoe outsole, the traditional algorithm based on the threshold method to determine the laser position may result in a large amount of information loss and miscellaneous point misjudgment owing to the irregularity of the shoe outsole surface, which critically affects the laser imaging quality. To address this problem, an algorithm based on the QQ plot inspection of the laser has been proposed. The QQ plot is a scatter plot, the abscissa is usually the quantile of the standard normal distribution, and the ordinate is the quantile of the data to be tested. If the points on the scatter plot tend to be straight lines, the data to be tested is in a normal distribution. Based on this property, the proposed algorithm aims to check whether the pixels of the image column tend to be normally distributed, rather than using traditional thresholding methods to locate the laser. The objective is to examine whether the image column pixel distribution is normal, instead of using the traditional threshold method to locate the laser. However, the calculation speed of this method is extremely low. To enhance the efficiency of testing the normality of the QQ plot, a quantile-repetition (Q-R) test method is proposed. In this approach, the degree of repetition of quantiles and the position of Q-R values are used to replace the QQ plot based evaluation of the points being on a straight line, and the exact center position is determined by the GGM. The experimental results show that the proposed algorithm can extract more effective points and fewer invalid points of the laser compared to those obtained using the traditional approach, in a rapid, stable, and accurate manner.

Optimization of point cloud preprocessing algorithm for equipped vehicles

In view of the fact that the point cloud 3D model will be interfered by environmental factors, measurement methods and other random factors in the process of data scanning and acquisition, there will be some invalid points, outliers and internal noise points. In this paper, a point cloud denoising method based on adaptive density clustering and statistical filtering is proposed to process vehicle point cloud data. which can effectively preserve vehicle features while obtaining optimal denoising effect. Compared with the existing point cloud noise processing algorithms, this algorithm can remove noise better, and has shorter time-consuming and good applicability.

Scenario modeling and operation path planning of autonomous driving tower cranes

Combined with the operation specification of building tower cranes, the three-dimensional path planning technology of tower cranes and the optimization of path points are studied. The improved RRT-Connect algorithm is proposed. By introducing heuristic information in the path search, the path search efficiency and quality of the algorithm are improved. By eliminating invalid points in the path, the control burden of tower crane is reduced, and the optimized lifting point movement path is transformed into tower crane joint movement, and finally the calculation result is uploaded to PLC through LAN. The running trajectory of the tower crane lifting point is visually displayed in the scene model, and the validity and safety of the path are verified. The real tower crane is controlled. The operation is completed and the automatic driving of the tower crane is realized.

Improved Particle Swarm Optimization Screening Iterative Algorithm in Gravity Matching Navigation

The promotion of matching precision is one of the key technical problems of gravity-assisted inertial navigation. In this study, first, the viability of the particle swarm optimization (PSO) algorithm applied to gravity matching inertial navigation is analyzed, and the original PSO algorithm is improved. The similarity of single-point matching is used to round off the matching result points, and the difference between the matching result coordinates and the corresponding inertial coordinates is calculated over a certain period to filter out the relatively poorer matching points. Finally, by calculating the coordinate distance of the first and last points between the consecutive valid matching segments and setting the limit difference, the coordinates of the invalid points between two adjacent valid matching points that meet the limit difference are projected to realize the correction of matching points. The test results show that the improved PSO-based screening iterative gravity matching algorithm can avoid the additional matching error caused by the setting of the search resolution compared with the PSO, new self-organizing hierarchical PSO (NHPSO), and adaptive PSO (APSO) matching algorithm and has significant improvement in the matching accuracy.

Invalid point removal method based on error energy function in fringe projection profilometry

Invalid points, such as shadow and background, in the captured fringe patterns of fringe projection profilometry (FPP) are often inevitable due to the limited field of view measurements of three-dimensional (3D) imaging equipment. To ensure the quality of 3D reconstruction data, these invalid points must be identified and removed. FPP captures co-frequency-based fringe pattern sequences and approximately distributes this data along an ideal cosine curve. We propose an invalid points removal method based on an error energy function. By analyzing the relationship between the pixel values of a series of captured co-frequency patterns and an ideal cosine curve, we quantize the error energy by using a Gaussian weighted Euclidean distance. An improved Gaussian filtering method based on modulation intensity is used to significantly increase the error energy difference between the valid points of the target and the invalid points of the shadow/background area. Finally, the points whose error energy is greater than a certain error threshold are removed as invalid points. Experimental results indicate that this method can efficiently remove invalid points in fringe patterns and performs better than existing traditional methods.

Enhanced Edge Detection for 3D Crack Segmentation and Depth Measurement with Laser Data

With the usage of computer visual technology in civil engineering, pavement crack survey with imaging sensor gained wide attention in the past years. Unfortunately, it is still a challenge to achieve the satisfying results. This paper presents a pavement crack survey approach based on edge detection for laser data. At first, the LS-40 line-laser scanner is implemented to achieve 3D pavement surface data. With the advantage of the various depth information exhibited in the pavement data, an enhanced edge detection based on fractional differential is proposed for 3D crack segmentation. The proposed method could effectively enhance the crack boundary and maintain texture details, which can guarantee the high accuracy in crack segmentation. Moreover, a novel plane fitting method based on dynamic threshold is studied to calculate crack depth information. It can not only identify and remove invalid points effectively, but also accomplish plane calculation with errors in three directions. Experiments verify that the proposed approach can achieve the satisfying result and can work well in F-measure system.

A Point Cloud-Based Feature Recognition and Path Planning Method

Based on the research background of in situ automatic ultrasonic phased array inspection of irregular porous castings, due to the limited in situ inspection stations, complex shape of irregular porous castings, and extreme multireflection structural features, it is necessary to identify the positioning inspection features among multiple features to be inspected and plan the optimal inspection path. This research is interested in the porous location recognition and its detection path planning of irregular porous castings. For this, a point cloud-based multifeature contour recognition and location algorithm were proposed to simultaneously extract and locate the hole feature and cylindrical feature from an irregular porous castings. Furthermore, a detection path planning method was put forward to search the shortest robot’s detection path based on the above acquired features by visual recognition and positioning technology. First, through the calibration of the industrial robot tool coordinate system and the internal and external parameters of the camera, the “EyeinHand” hand-eye conversion relationship was established. Second, the robot vision system collects the point cloud information of the area to be inspected and performs point cloud splicing, the accuracy of the original data, the removal of noise such as invalid points, outliers and internal noise points, on this basis, the boundary curve of the hole to be inspected was extracted, the cylindrical equation was fitted, its geometric center was calculated, and the central coordinates and axis direction of the contour of the hole to be inspected were obtained. Finally, all the detection paths were traversed through the multibranch tree to obtain the optimal detection path of the detection points of multiple targets. The experimental results show that the positioning accuracy of the feature of the hole to be inspected by the vision system is 0.107 mm, the aperture extraction accuracy is 0.002 mm, the cylinder fitting accuracy is 0.04 mm, and the calculation accuracy of the angle between the two axes is within 0.4. When the number of features to be inspected is different, the average moving distance can be saved by 10.7% by using the end effector after path optimization. The feasibility of in situ automatic ultrasonic phased array detection for irregular porous castings using by visual positioning is verified.

Depth image restoration based on bimodal joint sequential filling

In this paper, we propose a depth image restoration algorithm based on the bimodal joint sequential filling. Basically, the types, causes, and characteristics of invalid points in the depth image are thoroughly analyzed. Besides, using the off-line calibration, accurate registration between the depth and the color image is effectively achieved. Finally, filling priority estimation based on the bimodal joint conditional entropy and the depth value prediction depended on the hypothesis testing are put forward and integrated to realize the depth image restoration under the framework of sequential filling. In the experiments, a comparison between different settings is carried out under the framework of sequential filling. Then the quantitative and qualitative comparisons between other competing methods and ours are implemented. The results demonstrate that our algorithm can restore various types of invalid points in the depth images, and it has significant improvements over several popular competing algorithms in terms of both robustness and preciseness.

A Rapid and High-Precision Mountain Vertex Extraction Method Based on Hotspot Analysis Clustering and Improved Eight-Connected Extraction Algorithms for Digital Elevation Models

Digital Elevation Model (DEM)-based mountain vertex extraction is one of the most useful DEM applications, providing important information to properly characterize topographic features. Current vertex-extraction techniques have considerable limitations, such as yielding low-accuracy results and generating false mountain vertices. To overcome these limitations, a new approach is proposed that combines Hotspot Analysis Clustering and the Improved Eight-Connected Extraction algorithms that would quickly and accurately provide the location and elevation of mountain vertices. The use of the elevation-based Hotspot Analysis Clustering Algorithm allows the fast partitioning of the mountain vertex area, which significantly reduces data and considerably improves the efficiency of mountain vertex extraction. The algorithm also minimizes false mountain vertices, which can be problematic in valleys, ridges, and other rugged terrains. The Eight-Connected Extraction Algorithm also hastens the precise determination of vertex location and elevation, providing a better balance between accuracy and efficiency in vertex extraction. The proposed approach was used and tested on seven different datasets and was compared against traditional vertex extraction methods. The results of the quantitative evaluation show that the proposed approach yielded higher efficiency, considerably minimized the occurrence of invalid points, and generated higher vertex extraction accuracy compared to other traditional methods.

3D driver pose estimation based on joint 2D–3D network

Three‐dimensional (3D) driver pose estimation is a promising and challenging problem for computer–human interaction. Recently convolutional neural networks have been introduced into 3D pose estimation, but these methods have the problem of slow running speed and are not suitable for driving scenario. In this study, the proposed method is based on two types of inputs, infrared image and point cloud obtained from time‐of‐flight camera. The authors propose a joint 2D–3D network incorporating image‐based and point‐based feature to promote the performance of 3D human pose estimation and run on a high speed. For point cloud with invalid points, the authors first do preprocess and then design a denoising module to handle this problem. Experiments on private driver data set and public Invariant‐Top View data set show that the proposed method achieves efficient and competitive performance on 3D human pose estimation.

Complete gas-brine imbibition relative permeability curves increase confidence in gas field performance

The standard methods used for measuring gas relative permeability during brine imbibition have been found to be inadequate as the resulting curves are incomplete and may include invalid points. CSI...

Research on denoising optimization algorithm of point cloud in 3d reconstruction

3 d reconstruction have become increasingly important in today’s information age, has become a bridge between real world and computer, usually in the access to the image due to factors such as equipment or environment are people hope there are a large number of high frequency signal, the pretreatment of the 3 d reconstruction is the most basic and crucial step in the technology. In this paper, an optimized adaptive filtering de-noising algorithm is proposed. The concept of minimum intra-class dispersion is introduced into the algorithm, and the genetic algorithm is used to find the optimal threshold, so as to eliminate the disadvantages of the algorithm in searching the scattered point cloud data and promote the overall performance of the algorithm. In this paper, the algorithm improves the speed, simplifies the initial point cloud model, and eliminates a large number of invalid points from the subsequent filtering algorithm.

Topology Structure Implied in β-Hilbert Space, Heisenberg Uncertainty Quantum Characteristics and Numerical Simulation of the DE Algorithm

The differential evolutionary ( D E ) algorithm is a global optimization algorithm. To explore the convergence implied in the H i l b e r t space with the parameter β of the D E algorithm and the quantum properties of the optimal point in the space, we establish a control convergent iterative form of a higher-order differential equation under the conditions of P - ε and analyze the control convergent properties of its iterative sequence; analyze the three topological structures implied in H i l b e r t space of the single-point topological structure, branch topological structure, and discrete topological structure; and establish and analyze the association between the H e i s e n b e r g uncertainty quantum characteristics depending on quantum physics and its topological structure implied in the β -Hilbert space of the D E algorithm as follows: The speed resolution Δ v 2 of the iterative sequence convergent speed and the position resolution Δ x β ε of the global optimal point with the swinging range are a pair of conjugate variables of the quantum states in β -Hilbert space about eigenvalues λ i ∈ R , corresponding to the uncertainty characteristics on quantum states, and they cannot simultaneously achieve bidirectional efficiency between convergent speed and the best point precision with any procedural improvements. Where λ i ∈ R is a constant in the β -Hilbert space. Finally, the conclusion is verified by the quantum numerical simulation of high-dimensional data. We get the following important quantitative conclusions by numerical simulation: except for several dead points and invalid points, under the condition of spatial dimension, the number of the population, mutated operator, crossover operator, and selected operator are generally decreasing or increasing with a variance deviation rate + 0 . 50 and the error of less than ± 0 . 5 ; correspondingly, speed changing rate of the individual iterative points and position changing rate of global optimal point β exhibit a inverse correlation in β -Hilbert space in the statistical perspectives, which illustrates the association between the H e i s e n b e r g uncertainty quantum characteristics and its topological structure implied in the β -Hilbert space of the D E algorithm.

Invalid-point removal based on epipolar constraint in the structured-light method

In structured-light measurement, there unavoidably exist many invalid points caused by shadows, image noise and ambient light. According to the property of the epipolar constraint, because the retrieved phase of the invalid point is inaccurate, the corresponding projector image coordinate (PIC) will not satisfy the epipolar constraint. Based on this fact, a new invalid-point removal method based on the epipolar constraint is proposed in this paper. First, the fundamental matrix of the measurement system is calculated, which will be used for calculating the epipolar line. Then, according to the retrieved phase map of the captured fringes, the PICs of each pixel are retrieved. Subsequently, the epipolar line in the projector image plane of each pixel is obtained using the fundamental matrix. The distance between the corresponding PIC and the epipolar line of a pixel is defined as the invalidation criterion, which quantifies the satisfaction degree of the epipolar constraint. Finally, all pixels with a distance larger than a certain threshold are removed as invalid points. Experiments verified that the method is easy to implement and demonstrates better performance than state-of-the-art measurement systems.

Improved 3D measurement with a novel preprocessing method in DFP

Shadow and background are two common factors in digital fringe projection, which lead to ambiguity in three-dimensional measurement and thereby need to be seriously considered. Preprocessing is often needed to segment the object from invalid points. The existing segmentation approaches based on modulation normally perform well in pure dark background circumstances, which, however, lose accuracy in situations of white or complex background. In this paper, an accurate shadow and background removal technique is proposed, which segments the shadow by one threshold from modulation histogram and segments the background by the threshold in intensity histogram. Experiments are well designed and conducted to verify the effectiveness and reliability of the proposed method.

Contour-based next-best view planning from point cloud segmentation of unknown objects

A novel strategy is presented to determine the next-best view for a robot arm, equipped with a depth camera in eye-in-hand configuration, which is oriented to autonomous exploration of unknown objects. Instead of maximizing the total size of the expected unknown volume that becomes visible, the next-best view is chosen to observe the border of incomplete objects. Salient regions of space that belong to the objects are detected, without any prior knowledge, by applying a point cloud segmentation algorithm. The system uses a Kinect V2 sensor, which has not been considered in previous works on next-best view planning, and it exploits KinectFusion to maintain a volumetric representation of the environment. A low-level procedure to reduce Kinect V2 invalid points is also presented. The viability of the approach has been demonstrated in a real setup where the robot is fully autonomous. Experiments indicate that the proposed method enables the robot to actively explore the objects faster than a standard next-best view algorithm.