High-precision automatic measurement of two-dimensional geometric features based on machine vision

To realize high-precision automatic measurement of two-dimensional geometric features on parts, a cooperative measurement system based on machine vision is constructed. Its hardware structure, functional composition and working principle are introduced. The mapping relationship between the feature image coordinates and the measuring space coordinates is established. The method of measuring path planning of small field of view （FOV） images is proposed. With the cooperation of the panoramic image of the object to be measured, the small FOV images with high object plane resolution are acquired automatically. Then, the auxiliary measuring characteristics are constructed and the parameters of the features to be measured are automatically extracted. Experimental results show that the absolute value of relative error is less than 0. 03% when applying the cooperative measurement system to gauge the hole distance of 100 mm nominal size. When the object plane resolving power of the small FOV images is 16 times that of the large FOV image, the measurement accuracy of small FOV images is improved by 14 times compared with the large FOV image. It is suitable for high-precision automatic measurement of two-dimensional complex geometric features distributed on large scale parts.

Feature-based sequential partial vision measurement method for large scale machine parts

To realize the high-precision vision measurement for large scale machine parts, a new vision measurement method based on dimension features of sequential partial images is proposed. Instead of mosaicking the partial images, extracting the dimension features of the sequential partial images and deriving the part size according to the relationships between the sequential images is a novel method to realize the high- precision and fast measurement of machine parts. To overcome the corresponding problems arising from the relative rotation between two sequential partial images, a rectifying method based on texture features is put forward to effectively improve the processing speed. Finally, a case study is provided to demonstrate the analysis procedure and the effectiveness of the proposed method. The experiments show that the relative error is less than 0. 012% using the sequential image measurement method to gauge large scale straight-edge parts. The measurement precision meets the needs of precise measurement for sheet metal parts.

Feature Selection Based on Distance Measurement

Every day we receive a large amount of information through different social media and software,and this data and information can be realized with the advent of data mining methods.In the process of data mining,to solve some high-dimensional problems,feature selection is carried out in limited training samples,and effective features are selected.This paper focuses on two Relief feature selection algorithms:Relief and ReliefF algorithm.The differences between them and their respective applicable scopes are analyzed.Based on Relief algorithm,the high weight feature subset is obtained,and the correlation between features is calculated according to the mutual information distance measure,and the high redundant features are removed to obtain the feature subset with higher quality.Experimental results on six datasets show the effectiveness of our method.

Triple-path feature transform network for ring-array photoacoustic tomography image reconstruction

Photoacoustic imaging(PAI)is a noninvasive emerging imaging method based on the photoacoustic effect,which provides necessary assistance for medical diagnosis.It has the characteristics of large imaging depth and high contrast.However,limited by the equipment cost and reconstruction time requirements,the existing PAI systems distributed with annular array transducers are difficult to take into account both the image quality and the imaging speed.In this paper,a triple-path feature transform network(TFT-Net)for ring-array photoacoustic tomography is proposed to enhance the imaging quality from limited-view and sparse measurement data.Specifically,the network combines the raw photoacoustic pressure signals and conventional linear reconstruction images as input data,and takes the photoacoustic physical model as a prior information to guide the reconstruction process.In addition,to enhance the ability of extracting signal features,the residual block and squeeze and excitation block are introduced into the TFT-Net.For further efficient reconstruction,the final output of photoacoustic signals uses‘filter-then-upsample’operation with a pixel-shuffle multiplexer and a max out module.Experiment results on simulated and in-vivo data demonstrate that the constructed TFT-Net can restore the target boundary clearly,reduce background noise,and realize fast and high-quality photoacoustic image reconstruction of limited view with sparse sampling.

Similarity Measures of Satellite Images Using an Adaptive Feature Contrast Model

Similarity measurement is one of key operations to retrieve “desired” images from an image database. As a famous psychological similarity measure approach, the Feature Contrast (FC) model is defined as a linear combination of both common and distinct features. In this paper, an adaptive feature contrast (AdaFC) model is proposed to measure similarity between satellite images for image retrieval. In the AdaFC, an adaptive function is used to model a variable role of distinct features in the similarity measurement. Specifically, given some distinct features in a satellite image, e.g., a COAST image, they might play a significant role when the image is compared with an image including different semantics, e.g., a SEA image, and might be trivial when it is compared with a third image including same semantics, e.g., another COAST image. Experimental results on satellite images show that the proposed model can consistently improve similarity retrieval effectiveness of satellite images including multiple geo-objects, for example COAST images.

Research on Feature Extraction and Classification Method of Vibration Signal of Escalator Sprocket Bearing

In order to improve the accuracy of escalator sprocket bearing fault diagnosis,the problem of the feature extraction method of bearing vibration signal is addressed.In this paper,empirical mode is used to decompose the original signal,and the optimal modal component among the multiple modal components is obtained after the optimization decomposition is selected by the envelope spectrum method,and the multi-angle feature measure is introduced to extract the fault characteristic value.According to the vibration characteristics of the bearing vibration signal data,a bearing signal feature group that is more inclined to the fault feature category information is established,which avoids the absolute problem of extracting a single metric feature.The fuzzy C-means clustering algorithm is used to cluster the sample data with similar characteristics into the same cluster area,which effectively solves the problem that a single measurement analysis cannot characterize the complex internal characteristics ofthe bearing vibration signal.

Aerodynamics of indirect thrust measurement by the impulse method

The aerodynamic aspects of indirect thrust measurement by the impulse method have been studied both experimentally and numerically. The underlying basic aerodynamic principle is outlined, the phenomena in subsonic, supersonic and arc-heated jets are explored, and factors affecting the accuracy of the method are studied and discussed. Results show that the impulse method is reliable for indirect thrust measurement if certain basic requirements are met, and a simple guideline for its proper application is given.

CLUSTERING PROPERTIES OF FUZZY KOHONEN'S SELF-ORGANIZING FEATURE MAPS

A new clustering algorithm called fuzzy self-organizing feature maps is introduced. It can process not only the exact digital inputs, but also the inexact or fuzzy non-digital inputs, such as natural language inputs. Simulation results show that the new algorithm is superior to original Kohonen’s algorithm in clustering performance and learning rate.

Research on Visual Detection Algorithm for Groove Feature Sizes by Means of Structured Light Projection

The visual inspection is an economical and effective method for welding. For measuring the feature sizes of grooves,a method based on line structured light is presented. Firstly,an adaptive algorithm to extract the subpixel centerline of structured light stripes is introduced to deal with the uneven width and grayscale distributions of laser stripes,which is based on the quadratic weighted grayscale centroid. By means of region-of-interest(ROI)division and image difference,an image preprocessing algorithm is developed for filtering noise and improving image quality. Furthermore,to acquire geometrical dimensions of various grooves and groove types precisely,the subpixel feature point extraction algorithm of grooves is designed. Finally, experimental results of feature size measuring show that the absolute error of measurement is 0.031—0.176 mm,and the relative error of measurement is 0.2%—3.6%.

Feature Selection for Intrusion Detection Using Random Forest

An intrusion detection system collects and analyzes information from different areas within a computer or a network to identify possible security threats that include threats from both outside as well as inside of the organization. It deals with large amount of data, which contains various ir-relevant and redundant features and results in increased processing time and low detection rate. Therefore, feature selection should be treated as an indispensable pre-processing step to improve the overall system performance significantly while mining on huge datasets. In this context, in this paper, we focus on a two-step approach of feature selection based on Random Forest. The first step selects the features with higher variable importance score and guides the initialization of search process for the second step whose outputs the final feature subset for classification and in-terpretation. The effectiveness of this algorithm is demonstrated on KDD’99 intrusion detection datasets, which are based on DARPA 98 dataset, provides labeled data for researchers working in the field of intrusion detection. The important deficiency in the KDD’99 data set is the huge number of redundant records as observed earlier. Therefore, we have derived a data set RRE-KDD by eliminating redundant record from KDD’99 train and test dataset, so the classifiers and feature selection method will not be biased towards more frequent records. This RRE-KDD consists of both KDD99Train+ and KDD99Test+ dataset for training and testing purposes, respectively. The experimental results show that the Random Forest based proposed approach can select most im-portant and relevant features useful for classification, which, in turn, reduces not only the number of input features and time but also increases the classification accuracy.

Pose measurement using quaternion and Kalman filter

In this paper,a method combining perspective-n-point(PnP) and novel iteration algorithm is developed to measure the pose of a target in high precision for Tele-LightSaber game.The PnP algorithm is used to obtain a rough pose,which is taken as the initial value of the iteration algorithm.The iteration algorithm utilizes the unit quaternions to represent the rotations.Then the result is optimized with Kalman filter.Considering the real-time and accuracy of the pose measurement,a fast feature extraction algorithm including object location,edge detection and corner detection is adopted to get the corners in high precision.The experiments and results verify the effectiveness of the proposed method.

A Multisource Contour Matching Method Considering the Similarity of Geometric Features

The existing multi-source contour matching studies have focused on the matching methods with consideration of topological relations and similarity measurement based on spatial Euclidean distance,while it is lack of taking the contour geometric features into account,which may lead to mismatching in map boundaries and areas with intensive contours or extreme terrain changes.In light of this,it is put forward that a matching strategy from coarse to precious based on the contour geometric features.The proposed matching strategy can be described as follows.Firstly,the point sequence is converted to feature sequence according to a feature descriptive function based on curvature and angle of normal vector.Then the level of similarity among multi-source contours is calculated by using the longest common subsequence solution.Accordingly,the identical contours could be matched based on the above calculated results.In the experiment for the proposed method,the reliability and efficiency of the matching method are verified using simulative datasets and real datasets respectively.It has been proved that the proposed contour matching strategy has a high matching precision and good applicability.

Feature Selection for Classificatory Analysis Based on Information-theoretic Criteria

由选择为类别的分析减少模式的维数的特征选择目的而不是无关或冗余的特征最增进知识。在这研究，为特征评价的二项新奇信息理论上的措施被介绍：一个人是一个改进公式估计在候选人特征 fi 和给选择特征 S 的子集的目标班 C 之间的有条件的相互的信息，即，我(C；fi|S ) ，在假设下面，特征的那个信息一致地被散布；其它是基于的一个相互的信息(MI ) 能捕获无关、冗余的输入的建设性的标准在特征的信息的任意的分布下面展示。与这二项措施，二个新特征选择算法，叫了二次的 基于MI 的特征选择( QMIFS )途径和 基于MI 的建设性的标准( MICC )途径分别地，被建议，在哪个在 Battiti 的 MIFS 相似的没有参数并且( Kwak 和 Choi )的 MIFS-U 方法需要是预设。因此，怎么选择适当价值为的难处理的问题完全被避免与已经选择的特征做在关联之间的折衷到目标班和冗余性。试验性的结果表明 QMIFS 和 MICC 的好表演在上合成并且基准数据集合。

Voidage Measurement of Air-Water Two-phase Flow Based on ERT Sensor and Data Mining Technology

Based on an electrical resistance tomography(ERT) sensor and the data mining technology,a new voidage measurement method is proposed for air-water two-phase flow.The data mining technology used in this work is a least squares support vector machine(LS-SVM) algorithm together with the feature extraction method,and three feature extraction methods are tested:principal component analysis(PCA),partial least squares(PLS) and independent component analysis(ICA).In the practical voidage measurement process,the flow pattern is firstly identified directly from the conductance values obtained by the ERT sensor.Then,the appropriate voidage measurement model is selected according to the flow pattern identification result.Finally,the voidage is calculated.Experimental results show that the proposed method can measure the voidage effectively,and the measurement accuracy and speed are satisfactory.Compared with the conventional voidage measurement methods based on ERT,the proposed method doesn't need any image reconstruction process,so it has the advantage of good real-time performance.Due to the introduction of flow pattern identification,the influence of flow pattern on the voidage measurement is overcome.Besides,it is demonstrated that the LS-SVM method with PLS feature extraction presents the best measurement performance among the tested methods.

Open Contours Extraction of Rotational Surface Oriented to Layer Measurement

With layer-measured contours, an algorithm that can extract the contour segments from a rotational surface is presented. The extraction can be divided into two stages, i. e. the rough segmentation and the refinement. In the rough segmenting stage, an optimal contour matching method is put forward to find similar contour segment from another closed contour with respect to the seed contour. In the refining stage, an iterative way that can extract a circular arc precisely is presented based on parameters identification and contour-ends expanding/shrinking operation. The algorithm can extract the open contour segments from a rotational surface precisely, as demonstrated in the examples. Based on the work of this paper, further research, such as parameter identification of 3 - D surface and CAD model creation, can be conducted.

A SEMI-OPEN-LOOP CODING MODE SELECTION ALGORITHM BASED ON EFM AND SELECTED AMR-WB+ FEATURES

To solve the problems of the AMR-WB+(Extended Adaptive Multi-Rate-WideBand) semi-open-loop coding mode selection algorithm,features for ACELP(Algebraic Code Excited Linear Prediction) and TCX(Transform Coded eXcitation) classification are investigated.11 classifying features in the AMR-WB+ codec are selected and 2 novel classifying features,i.e.,EFM(Energy Flatness Measurement) and stdEFM(standard deviation of EFM),are proposed.Consequently,a novel semi-open-loop mode selection algorithm based on EFM and selected AMR-WB+ features is proposed.The results of classifying test and listening test show that the performance of the novel algorithm is much better than that of the AMR-WB+ semi-open-loop coding mode selection algorithm.

Study on the Technology of Image Measurement with High Accuracy based on Sobel Operator

According to the basic problem of industrial production, the paper presents a monocular camera and automatic annotation ranging scheme with a ruler. The scheme eliminates a series of complex operation steps frequently used for image location of the calibration, image correction, it has simple implementation and has successfully realized precise ranging by using digital image processing technology. From the beginning of the analysis of image edge detection, edge detection principle and some common edge detection operators, and proposes an improved multi-scale edge detection algorithm, then according to the particularity of the edge proposed new edge thinning algorithm, finally mark the target point through the feature extraction, calculate the final results of fault location.

猴痘临床特征的早期识别及其对疫情防控的意义

近日,猴痘病毒(MPXV)新出现的亚分支Ⅰb引发了新一轮猴痘(Mpox)疫情。2024-08-14,Mpox再次被WHO列为国际关注的突发公共卫生事件(PHEIC)。Mpox病例的早发现、早诊断是阻断其在医院内传播的关键。本文将从Mpox的病原学特征、流行病学特征、临床特征、诊断标准、防控措施等方面进行综述,以期为Mpox的早期识别和科学防控提供参考。

移动激光扫描在地铁车站限界测量中的应用

为满足限界要求,地铁车站站台边缘与地铁列车之间存在一定的间隙,但可能发生乘客踏空风险。现需对车站区段的站台边缘至线路中心线距离现状进行快速普查,为更新改造提供决策数据。文章利用移动激光扫描技术快速获取车站段全断面点云,通过点云数据处理提取关键特征点,实现了对空间距离的非接触测量,经传统方法对比复核,验证了该方法的准确性。

Segmentation and measurement scheme for fish morphological features based on Mask R-CNN

The morphological features of fish,such as the body length,the body width,the caudal peduncle length,the caudal peduncle width,the pupil diameter,and the eye diameter are very important indicators in smart mariculture.Therefore,the accurate measurement of the morphological features is of great significance.However,the existing measurement methods mainly rely on manual measurement,which is operationally complex,low efficiency,and high subjectivity.To address these issues,this paper proposes a scheme for segmenting fish image and measuring fish morphological features indicators based on Mask R-CNN.Firstly,the fish body images are acquired by a home-made image acquisition device.Then,the fish images are preprocessed and labeled,and fed into the Mask R-CNN for training.Finally,the trained model is used to segment fish image,thus the morphological features indicators of the fish can be obtained.The experimental results demonstrate that the proposed scheme can segment the fish body in pure and complex backgrounds with remarkable performance.In pure background,the average relative errors(AREs)of all indicators measured all are less than 2.8%,and the AREs of body length and body width are less than 0.8%.In complex background,the AREs of all indicators are less than 3%,and the AREs of body length and body width is less than 1.8%.2020 China Agricultural University.Production and hosting by Elsevier B.V.on behalf of KeAi.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).1.Introduction With the advancing of its scientific and technological capabilities,China has made great achievements in the mariculture.The production accounts for more than 70%of the world’s overall mariculture output[1].The measurement of body length,body width and other morphological features of fish have wide application prospects in smart mariculture.Due to the difference in the quality and feeding ability of the Juvenile fish,the growth of the fish in the same pond is significantly different after a period of growth.Then,the fish needs to be classified.Grading can make fish grow better and improve feed utilization[2].The fish body length and body width are closely related to the weight of the fish.In the mariculture,the fishermen judge the growth of the fish by collecting the morphological feature of the fish,and use the information as an important reference for feeding,fishing and classification[3].At present,most of the measurement methods of fish body morphological features are manual,the operator usesmeasuring ruler to measure manually.It requires high technical level,and has high labor intensity and low efficiency.Furthermore,https://doi.