Edge Tracing Research Articles

Mechanical characterization of hydrogen embrittlement in a gaseous environment: An innovative test setup using sub-size specimens

Hydrogen embrittlement (HE) of steels in the presence of gaseous hydrogen poses a challenge to the safe transportation of hydrogen through pipelines. The use of sub-size specimens machined from coupons extracted from pipes is proposed for monitoring the degradation of material properties over the entire operational life. The objective of this paper is to present a methodology based on the sub-size specimens using a machine developed to operate with a small volume of gaseous hydrogen, up to a pressure of 250 bars, and test control using Edge Tracing (ET) technique. Tensile and toughness mini-specimens extracted from a E355 mod. steel grade are employed for this study. Reliable results are obtained using tensile mini-specimens with 1.2 and 2.4 mm diameters, tested under both air and gaseous H2. Toughness tests are conducted using mini-specimens with a thickness of 5 mm. These tests monitored the Load Line Opening Displacement (LLOD) and the Opening Angle are monitored during the tests through ET technique, allowing for the determination of J−Δa and δ−Δa plots under both air and gaseous H2 conditions. The results uncover the impact of H2 pressure and specimen dimensions on hydrogen embrittlement, leading to the computation of Hydrogen Embrittlement Indexes through various methods. The insights gained from this study establish the necessary foundations for the utilization of mini-specimens in characterizing hydrogen embrittlement.

Quantitative analysis and comprehensive evaluation of coarse aggregate morphology for asphalt pavement

The morphological characteristics of coarse aggregate play a crucial role in the mechanical properties, durability, and construction performance of asphalt mixtures and pavements in highway engineering. This study aims to quantitatively analyze the morphological characteristics of coarse aggregate and establish comprehensive evaluation indicators for its quality and performance. Two-dimensional cross-sectional images of coarse aggregates are obtained using the self-developed laser scanning equipment, and edge features are extracted using the Canny operator and edge tracing algorithm. Six morphological characteristic indicators are selected, and factor analysis is employed to propose comprehensive shape and edge feature for coarse aggregates. The results show that the proposed evaluation indicators effectively capture the shape and edge feature of coarse aggregates, with the exception of roughness, which is deemed unsuitable for evaluation. Factor analysis of six indicator values in 2,700 two-dimensional images led to the proposal of Shape Factor (SF) and Edge Feature Factor (EFF) as significant factors affecting aggregate shape and edge characteristics, respectively. Following optimization, SF ranged from 0.041 to 1.793, while EFF ranged from 0.368 to 1.745. The spearman’s rank correlation coefficient revealed strong correlations between the evaluation of coarse aggregate shape and edge features using the line laser-based comprehensive evaluation index. Specifically, correlation coefficients of 0.864 and 0.805 exceeded those of commonly used methods such as AIMS and X-ray CT, indicating the rationality and accuracy of the self-developed laser scanning device in evaluating aggregate morphology. This research provides strong support for the construction of highway engineering and optimization of coarse aggregate production and supply.

An Open-Source Deep Learning Algorithm for Efficient and Fully Automatic Analysis of the Choroid in Optical Coherence Tomography.

To develop an open-source, fully automatic deep learning algorithm, DeepGPET, for choroid region segmentation in optical coherence tomography (OCT) data. We used a dataset of 715 OCT B-scans (82 subjects, 115 eyes) from three clinical studies related to systemic disease. Ground-truth segmentations were generated using a clinically validated, semiautomatic choroid segmentation method, Gaussian Process Edge Tracing (GPET). We finetuned a U-Net with the MobileNetV3 backbone pretrained on ImageNet. Standard segmentation agreement metrics, as well as derived measures of choroidal thickness and area, were used to evaluate DeepGPET, alongside qualitative evaluation from a clinical ophthalmologist. DeepGPET achieved excellent agreement with GPET on data from three clinical studies (AUC = 0.9994, Dice = 0.9664; Pearson correlation = 0.8908 for choroidal thickness and 0.9082 for choroidal area), while reducing the mean processing time per image on a standard laptop CPU from 34.49 ± 15.09 seconds using GPET to 1.25 ± 0.10 seconds using DeepGPET. Both methods performed similarly according to a clinical ophthalmologist who qualitatively judged a subset of segmentations by GPET and DeepGPET, based on smoothness and accuracy of segmentations. DeepGPET, a fully automatic, open-source algorithm for choroidal segmentation, will enable researchers to efficiently extract choroidal measurements, even for large datasets. As no manual interventions are required, DeepGPET is less subjective than semiautomatic methods and could be deployed in clinical practice without requiring a trained operator. DeepGPET addresses the lack of open-source, fully automatic, and clinically relevant choroid segmentation algorithms, and its subsequent public release will facilitate future choroidal research in both ophthalmology and wider systemic health.

UnfoldIR: Tactile Robotic Unfolding of Cloth

Robotic unfolding of cloth is challenging due to the wide range of textile materials and their ability to deform in unpredictable ways. Previous work has focused almost exclusively on visual feedback to solve this task. We present UnfoldIR (“unfolder”), a dual-arm robotic system relying on infrared (IR) tactile sensing and cloth manipulation heuristics to achieve in-air unfolding of randomly crumpled rectangular textiles by means of edge tracing. The system achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\gt$</tex-math></inline-formula> 85% coverage on multiple textiles of different sizes and textures. After unfolding, at least three corners are visible in 83.3 up to 94.7% of cases. Given these strong “tactile-only” results, we argue that the fusion of both tactile and visual sensing can bring cloth unfolding to a new level of performance.

Caching in Dynamic Environments: A Near-Optimal Online Learning Approach

The rapid growth of rich multimedia data in today’s Internet, especially video traffic, has challenged the content delivery networks (CDNs). Caching serves as an important means to reduce user access latency so as to enable faster content downloads. Motivated by the dynamic nature of the real-world edge traces, this paper introduces a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">provably well</i> online caching policy in dynamic environments where: 1) the popularity is highly dynamic; 2) no regular stochastic pattern can model this dynamic evaluation process. First, we design an online optimization framework, which aims to minimize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic regret</i> that finds the distance between an online caching policy and the best dynamic policy in hindsight. Second, we propose a dynamic online learning method to solve the non-stationary caching problem formulated in the previous framework. Compared to the linear dynamic regret of previous methods, our proposal is proved to achieve a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sublinear dynamic regret</i> , from which it is guaranteed to be nearly optimal. We verify the design using both synthetic and real-world traces: the proposed policy achieves the best performance in the synthetic traces with different levels of dynamicity, which verifies the dynamic adaptation; our proposal consistently achieves at least 9.4% improvement than the baselines, including LRU, LFU, Static Online Learning based replacement, and Deep Reinforcement Learning based replacement, in random edge areas from real-world traces (from iQIYI), further verifying the effectiveness and robustness on the edge.

Edge tracing technique to study post‐necking behavior and failure in Al alloys and anisotropic plasticity in line pipe steels

AbstractThe recently developed edge tracing (ET) method allows to estimate the radial deformation in axisymmetric tensile specimens via analysis of digital images recorded during the experiments. Images are processed to detect the sample's contours and therefore estimate the minimal cross‐section diameter. This technique was mainly developed to characterize the plastic behavior well beyond the necking strain. The aim of this work is to apply the ET method to two novel case studies. Firstly, the post‐necking behavior and failure of a low ductility Al alloy are investigated. Low ductility alloys tend to fail brutally after reaching the maximum load. The major result is the capture of the sharp load drop which allowed to calibrate parameters of a GTN damage model. Secondly, the anisotropic elastic–plastic behavior of a “vintage” line pipe steel is characterized by a direct measurement of the Lankford coefficient. Assembled experimental data allowed to model the anisotropic plasticity beyond necking in different loading directions.

Nanotube formation of Ti-6Al-4V alloy and its corrosion behavior

In this study, the nanotube formation and corrosion behavior of Ti-6Al-4V alloy with α- and β-phases were researched with various experimental instruments. For this study, nanotube formation on the sample was performed using the anodic oxidation method by applying a voltage of 30 V using a direct current power supply for 1 h using a 1.0 M H3PO4 + 0.8 wt.% NaF solution at 25 °C. The corrosion properties of specimens were examined by potentiodynamic testing (potential range of -1000–1500 mV) in a 0.9% NaCl solution using a potentiostat. The nanotube morphology of the alloys was observed by field-emission scanning electron microscopy, X-ray diffractometry, and energy dispersive X-ray spectroscopy. The nanotubes in the α-phase were uniformly formed on the surface of the Ti-6Al-4V alloy, but nanotubes in the β-phase were irregularly formed. The nanotube-formed alloy had a wider passivation range and higher corrosion current density than the bulk alloy. The morphologies of the corrosion surfaces show that the edge trace around the β-phase was reduced, and the anatase phase increased on the corroded surface. In cross-sectioned nanotube layer after corrosion test, a deep pit was observed in the part where the nanotubes were removed. XRD analysis showed that the anatase and rutile phases were not formed in bulk alloys, but anatase and rutile phases were detected in the nanotube formed alloy, and the peak of the anatase increased on the corroded surface. From the result of potentiostatic test, the variation of current density from the beginning is almost the same, but the current density of the nanotube formed surface was slightly higher than that of non-nanotube formed surface with time.

View factors-when is ray tracing a good idea?

In this paper a number of variants of the Monte-Carlo method and quasi-Monte-Carlo method are used to evaluate three view factor configurations. The key question asked is “Is the ever-popular Monte-Carlo method combined with ray tracing the best approach for evaluating view factors?”. For two of the view factor configurations a Monte-Carlo method and quasi-Monte-Carlo method based on the numerical integration of the view factor integral are much more accurate than a Monte-Carlo method and quasi-Monte-Carlo method based on ray tracing. An interesting conclusion is that when ray tracing is inefficient, i.e., the view factor is small, numerical integration works very well, exactly when ray tracing has poor accuracy.For the third view factor where the two surfaces have a common edge the view factor integral has a singularity at the common edge and ray tracing works well relative to numerical integration. A new hybrid Monte-Carlo method and quasi-Monte-Carlo method that combines the advantages of ray tracing and numerical integration for the view factor configuration with a common edge is presented. The hybrid quasi-Monte-Carlo method is shown to be the most efficient method of evaluating the view factor integral.

Edge Tracing Using Gaussian Process Regression.

We introduce a novel edge tracing algorithm using Gaussian process regression. Our edge-based segmentation algorithm models an edge of interest using Gaussian process regression and iteratively searches the image for edge pixels in a recursive Bayesian scheme. This procedure combines local edge information from the image gradient and global structural information from posterior curves, sampled from the model's posterior predictive distribution, to sequentially build and refine an observation set of edge pixels. This accumulation of pixels converges the distribution to the edge of interest. Hyperparameters can be tuned by the user at initialisation and optimised given the refined observation set. This tunable approach does not require any prior training and is not restricted to any particular type of imaging domain. Due to the model's uncertainty quantification, the algorithm is robust to artefacts and occlusions which degrade the quality and continuity of edges in images. Our approach also has the ability to efficiently trace edges in image sequences by using previous-image edge traces as a priori information for consecutive images. Various applications to medical imaging and satellite imaging are used to validate the technique and comparisons are made with two commonly used edge tracing algorithms.

Fingerprint core point detection using connected component approach and orientation map edge tracing approach

Fingerprint core point detection using connected component approach and orientation map edge tracing approach

Fingerprint core point detection using connected component approach and orientation map edge tracing approach

Fingerprint recognition has always played a key role in forensic and commercial applications. The most challenging part of this recognition is to detect the accurate position of the fingerprint's c...

A vision based method for automated measurement of circular fiber cross-sections

Abstract There are many synthetic fibers with circular cross-sections, and characteristics of the fiber cross-sections have a major influence on the fiber properties. Direct measurements made with microscopic techniques on the fiber cross-sections can output maximum information on transverse dimensions. However, measuring parameters of the fiber sections in the microscopic images is a laborious task. In this paper, a hybrid method based on machine vision is proposed to measure the diameter and ellipticity of the circular fiber cross-sections. The hybrid method consists of modified Circle Hough Transform (CHT) and edge tracing algorithm, and the two sub-algorithms are performed successively. The modified CHT is used for detection and initial measurement of the fiber sections. Then the results are refined by the edge tracing algorithm, outputting elliptical results along with the ellipticity information of each fiber section. Experimental results show that the proposed method outperforms existing methods in circular fiber cross-section measurement.

Real-time extraction of water surface boundary using shipborne radar

ABSTRACTReal-time water boundary extraction with high accuracy is still a challenging problem in remote-sensing field. This article presents a shipborne water surface boundary extraction technique by integrating civilian marine radar and GPS compass. Meanwhile, a framework is developed to automatically extract water boundary from radar image. In this framework, direct transformation is adopted to rectify the geometric distortion of the radar image, morphological operation is used to fill holes and filter speckles in image, an edge tracing algorithm is design to extract image boundaries, visibility analysis and optimizations are conducted to recognize the real water surface boundary and to produce a vector map. A case study in Nanjing reach of Yangtze River are intended to validate performances of the method, and the final output of this method is verified using synchronous and detailed GPS-RTK (real-time kinematics) survey points. The experimental results demonstrate that the proposed water boundary extraction method can reach a positioning accuracy within ±1 m, which is significantly better than that of satellite-based optical remote-sensing and synthetic aperture radar (SAR) techniques, and the algorithm is computationally efficient and has the capability of real-time, full-automatic water boundary generation for various applications.

Comparison of Cardiac Output of Both 2-Dimensional and 3-Dimensional Transesophageal Echocardiography With Transpulmonary Thermodilution During Cardiac Surgery

To compare agreement and variability of cardiac output measurement of 2-dimensional (2D) and 3D transesophageal echocardiography (TEE) with thermodilution before and after bypass. Prospective observational study. Two tertiary hospitals. Cardiac output (CO) was measured simultaneously with thermodilution and TEE by multiplying either the left ventricular outflow tract area (LVOTA) or aortic valve area (AVA), the velocity-time integral (VTI) of flow at the same site, and heart rate. The LVOTA was calculated using diameter for 2D TEE. Planimetry was used for 3D TEE. The AVA was measured using planimetry. The study comprised 82 adult patients undergoing coronary or valve surgery. One hundred fifty-four complete sets of measurements were obtained (82 prebypass and 72 postbypass). All TEE methods had acceptable correlation and absence of proportional or fixed bias except for the left ventricular outflow tract (LVOT) VTI modal trace method, which had poor correlation and proportional but not fixed bias (regression coefficient [95% confidence interval], bias [percentage of mean CO]): 2D LVOT VTI modal trace 0.67 (0.54-0.80), -36.4%; 2D LVOT VTI outer edge trace 0.96 (0.80-1.12), -15.3%; 2D AVA planimetry 0.96 (0.75-1.18), +4.9%; 3D LVOT area planimetry 1.18 (0.96-1.41), +0.8%; 3D AVA planimetry 1.20 (0.93-1.46), +0.4%. All TEE methods had wide levels of agreement compared with thermodilution (-3.94 to +0.23 L/min, -2.83 to +1.28 L/min, -2.23 to +2.73 L/min, -2.35 to +2.42 L/min, and -2.57 to +2.61 L/min, respectively). Measurement variability was superior for all TEE methods compared with thermodilution before but not after bypass. Although limits of agreement of CO measurement with 3D TEE and thermodilution are wide, 2D planimetry of the AVA and continuous wave Doppler may be substituted for thermodilution before and after bypass.

Predict Vehicle Collision by TTC From Motion Using a Single Video Camera

The objective of this paper is the instantaneous computation of time-to-collision (TTC) for potential collision only from the motion information captured with a vehicle borne camera. The contribution is the detection of dangerous events and degree directly from motion divergence in the driving video, which is also a clue used by human drivers. Both horizontal and vertical motion divergence are analyzed simultaneously in several collision sensitive zones. The video data are condensed to the motion profiles both horizontally and vertically in the lower half of the video to show motion trajectories directly as edge traces. Stable motion traces of linear feature components are obtained through filtering in the motion profiles. As a result, this avoids object recognition and sophisticated depth sensing in prior. The fine velocity computation yields reasonable TTC accuracy so that a video camera can achieve collision avoidance alone from the size changes of visual patterns. We have tested the algorithm for various roads, environments, and traffic, and shown results by visualization in the motion profiles for overall evaluation.

Development of measurement system of cutting load response in nipper blade, and estimation of wedge angle effect on cutting characteristics and shear edge trace of polystyrene bars

This research was aiming to investigate cutting characteristics of Polystyrene square bars that were cut by three different type nippers. A pair of rotational-linked fixture of nipper arm was designed and developed for measuring the cutting load and displacement of the nipper. After discussing the calibration of nipper’s inside spring force and the stiffness of fixture, three types of nippers which had the representative apex angles of 72° (the big nipper), 42° (the medium nipper), and 13° (the small nipper), were examined with a 3 mm square bar of polystyrene under keeping the indentation velocity at V = 1 mm·s−1. It was clarified that the capability of designed fixture JIG was consistent for measuring and comparing the cutting response of several nippers. Using the fixture JIG and three kinds of nippers, the cutting peak maximum force, the cutting energy and the sheared profile (shear edge trace) of the PS bar specimen were investigated. When using the big and medium nippers, there were unstable cracks and dynamic large force drop (as a breaking down) during the cutting process after passing the necked stage, while the small nipper had the lowest peak line in the early stage (at the pushed stage) and any complicated cracks and large force drop were not detected, due to the wedge effect of blade apex angle and frictional restriction of counter anvil. Furthermore, the asymmetric effects of upper/lower wedge indentation were detected by using a microscope CCD camera during the cutting process.

Segmentation of yeast cell's bright-field image with an edge-tracing algorithm.

Phenotype analysis of yeast cell requires high-throughput imaging and automatic analysis of abundant image data. At first, each cell needs to be segmented and labeled in the bright-field images. However, the ambiguous boundary of bright-field yeast cell images leads to the failure of traditional segmentation algorithms. We propose a segmentation algorithm based on the morphological characteristics of yeast cells. Seed points are first identified along the cell contour and then connected by an edge tracing approach. In this way, "ill-detected" noise points are removed so that edges of yeast cells can be successfully extracted in bright-field images with sparsely distributed cells. In densely packed images, yeast cells with normal morphology can also be correctly segmented and labeled.

Edge detection Using Histogram Localization

Detection of edges under noisy environments has been gaining lot of prominence in the recent past in most of the image and video processing applications. In this work a novel approach based on the distribution of intensity values and their corresponding positions has been proposed for distinguishing the edge pixels from the grey scale images. Separate histogram has been maintained for X and Y coordinates. The first order derivative is applied over these histograms to distinguish the edge pixels. The pixel with gradient distribution below a specific threshold value is selected as an edge pixel. This method is found to work well in case of both noiseless and noisy images. Hence this method is able to perceive the underlying information in case of noisy images also. The proposed algorithm can be used for both low and high resolution images. However, the performance of the algorithm is more evident in high resolution image. A general analysis of the proposed method has been conducted for arbitrary images. The major application of the proposed work can be used for the applications that doesn’t need any preprocessing or to avoid any loss of information like in medical image analysis as it contemplate towards every intensity bin to trace the edges present in the histogram of the image rather than the overall image concerning for direct edge tracing. The results have been compared with canny algorithm which is most commonly used for edge detection.

Flame : A flexible data reduction pipeline for near-infrared and optical spectroscopy

We present flame, a pipeline for reducing spectroscopic observations obtained with multi-slit near-infrared and optical instruments. Because of its flexible design, flame can be easily applied to data obtained with a wide variety of spectrographs. The flexibility is due to a modular architecture, which allows changes and customizations to the pipeline, and relegates the instrument-specific parts to a single module. At the core of the data reduction is the transformation from observed pixel coordinates (x, y) to rectified coordinates (lambda, gamma). This transformation consists in the polynomial functions lambda(x,y) and gamma(x,y) that are derived from arc or sky emission lines and slit edge tracing, respectively. The use of 2D transformations allows one to wavelength calibrate and rectify the data using just one interpolation step. Furthermore, the gamma(x,y) transformation includes also the spatial misalignment between frames, which can be measured from a reference star observed simultaneously with the science targets. The misalignment can then be fully corrected during the rectification, without having to further resample the data. Sky subtraction can be performed via nodding and/or modeling of the sky spectrum; the combination of the two methods typically yields the best results. We illustrate the pipeline by showing examples of data reduction for a near-infrared instrument (LUCI at the Large Binocular Telescope) and an optical one (LRIS at the Keck telescope).

The Functional Organization and Integration of Purkinje Cells in the Cerebellum

The overall object of this project is to create a 3D model of the cerebellum in order to illustrate the anatomical organization of Purkinje neurons, and to provide a better understanding of how they are integrated into other locations of the brain. Because the communication between the cerebrum and cerebellum is a closed system of polysynaptic circuits, understanding the underlying anatomy and physiology is a significant challenge. Classic staining techniques do not have the resolution nor the sophistication to capture the complete organization of the cerebellar cortex, let alone the entire cerebrocerebellar circuit. To overcome this challenge, a more precise staining technique has been developed to understand the complex functional organization of the cerebellum. Using a genetically modified variant of the rabies virus, neural connectivity can be imaged with extreme detail. The rabies is modified so that the glycoprotein required for the trans‐synaptic jump is deleted and replaced with a GFP protein, thus creating the ability to accurately label the connections to a single neuron. As a result of this staining technique, the Purkinje neurons can be traced using the cutting edge tracing software Neurolucida. The traced Purkinje cell somas and their axons can then be rendered in 3D and used for analysis and quantification. Having the ability to visualize the origin of the axons of specific cell types that communicate with the deep cerebellar nuclei in high 3D resolution may begin to provide an understanding of how the input of Purkinje cells is integrated with other excitatory inputs from elsewhere in the brain. Moreover, a 3D model of the cerebellum could also provide a more complete explanation of how the cerebellum quickly and accurately coordinates the inputs and outputs into movement, and is able to predict body movements in space.Support or Funding Informationhttps://www.sciencedirect.com/science/article/pii/0306452284901933This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.