Accurate Shape Measurement Research Articles

Deep learning-based approach for high spatial resolution fibre shape sensing

Fiber optic shape sensing is an innovative technology that has enabled remarkable advances in various navigation and tracking applications. Although the state-of-the-art fiber optic shape sensing mechanisms can provide sub-millimeter spatial resolution for off-axis strain measurement and reconstruct the sensor’s shape with high tip accuracy, their overall cost is very high. The major challenge in more cost-effective fiber sensor alternatives for providing accurate shape measurement is the limited sensing resolution in detecting shape deformations. Here, we present a data-driven technique to overcome this limitation by removing strain measurement, curvature estimation, and shape reconstruction steps. We designed an end-to-end convolutional neural network that is trained to directly predict the sensor’s shape based on its spectrum. Our fiber sensor is based on easy-to-fabricate eccentric fiber Bragg gratings and can be interrogated with a simple and cost-effective readout unit in the spectral domain. We demonstrate that our deep-learning model benefits from undesired bending-induced effects (e.g., cladding mode coupling and polarization), which contain high-resolution shape deformation information. These findings are the preliminary steps toward a low-cost yet accurate fiber shape sensing solution for detecting complex multi-bend deformations.

Embedding and cross-sectioning as a sample preparation procedure for accurate and representative size and shape measurement of nanopowders

Reliable measurement of the size of polydisperse, complex-shaped commercial nanopowders is a difficult but necessary task, e.g., for regulatory requirements and toxicity risk assessment. Suitable methods exist for the accurate characterization of the size of non-aggregated, stabilized, spherical and monodisperse nanoparticles. In contrast, industrial nanoscale powders usually require dedicated sample preparation procedures developed for the analysis method of choice. These nano-powders tend to agglomerate and/or aggregate, a behavior which in combination with an innate broad particle size distribution and irregular shape often significantly alters the achievable accuracy of the measured size parameters. The present study systematically tests two commercially available nanoscale powders using different sample preparation methods for correlative analysis by scanning electron microscopy, dynamic light scattering, Brunauer–Emmet–Teller method and differential mobility analysis. One focus was set on the sample preparation by embedding nanoparticles in carbon-based hot-mounting resin. Literature on this topic is scarce and the accuracy of the data extracted from cross sections of these particles is unclearly stated. In this paper systematic simulations on the deviation of the size parameters of well-defined series of nanoparticles with different shapes from the nominal value were carried out and the contributing factors are discussed.

Evaluation of Photogrammetry System with Constraints on Camera Position and Attitude for Accurate Shape Measurement of Space Structures

Evaluation of Photogrammetry System with Constraints on Camera Position and Attitude for Accurate Shape Measurement of Space Structures

Using surround DIC to extract true stress–strain curve from uniaxial tension experiments

The exact stress–strain curve can be directly identified from uniaxial tension experiments up to the point of onset of diffuse necking. To gain insight into the post-necking hardening response of metals, other experimental methods such as the bulge, compression or torsion experiments are typically employed. Here, we introduce the idea of using surround DIC to obtain accurate specimen shape measurements of tensile specimens with rectangular gage section. Based on the results from a series of detailed three-dimensional finite element simulations of ASTM E8 type of uniaxial tension experiments on a wide spectrum of steel and aluminum behaviors, it is proposed to combine the history of the axial strain on the surface at the specimen center with the average axial stress to extract the stress–strain curve for strains of up to axial true strain of 1. The estimation uncertainty of this stress–strain curve estimation procedure is of the same order as that of the associated experimental uncertainties. This result is validated through an additional computational study for more than 100 distinct hardening behaviors. Furthermore, a surround DIC system composed of four stereo DIC systems is built and used to determine the stress–strain curve for a 1.5 mm thick DP780 steel. In addition, bulge experiments are performed on the same material revealing a good agreement of the post-necking hardening behavior determined through uniaxial and bulge testing.

Optimization of wavefront reconstruction accuracy for conjugate shift differential absolute testing

The conjugate shift differential method, based on Fourier transforms, is critical for surface error testing of high-precision optical elements. However, this common approach is also prone to periodic spectrum loss. As such, this paper proposes conjugate double shift differential (CDSD) absolute testing, which can effectively compensate for spectrum loss and achieve accurate wavefront reconstructions. Spectrum loss in the single shift differential method is analyzed through a study of the Fourier reconstruction process. A calculation model for the proposed CDSD method is then established and constraint conditions for shift quantities are provided by analyzing double shear effects observed in transverse shear interference. Finally, the reconstruction accuracies of various spectrum compensation methods are compared. Results showed that spectrum loss became more evident with increasing shift amounts. However, the CDSD method produced the smallest measurement error compared with conventional direct zero filling and adjacent point averaging, suggesting our approach could effectively improve absolute shape measurement accuracy for planar optical elements.

Spiral scanning nano-profiler using normal vector tracing method.

Unlike interferometers that obtain data from the entire sample, three-dimensional (3D) measuring instruments acquire data from multiple points. A nano-profiler measures slopes at different points on the surface to determine the sample shape, while a coordinate measuring machine (CMM) utilizes contact or non-contact height displacement probes for taking measurements. Therefore, the 3D measurement time increases if the area to be measured is wide. This leads to a reduced measurement accuracy owing to environmental changes. To reduce the 3D measurement time, a nano-profiler that rotates measured objects at a constant velocity and quickly scans each measurement point by following a spiral trajectory was designed. The developed nano-profiler measures angular distribution instead of height distribution. This makes it possible to reduce the drive error during rotation. Compared to an interferometer, the measurement time was reduced from 163 to 17minwith the nano-profiler and a 10nm peak-to-valley difference was achieved. The slope measurement with spiral scanning was confirmed to be a quick and accurate shape measurement technique that can be used while manufacturing high-precision optical components.

ShapePipe: A new shape measurement pipeline and weak-lensing application to UNIONS/CFIS data

Context. The Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) is an ongoing collaboration that will provide the largest deep photometric survey of the northern sky in four optical bands to date. As part of this collaboration, the Canada-France Imaging Survey (CFIS) is observing r-band data with an average seeing of 0.65 arcsec, which is complete to magnitude 24.5 and thus ideal for weak-lensing studies. Aims. We perform the first weak-lensing analysis of CFIS r-band data over an area spanning 1700 deg2 of the sky. We create a catalogue with measured shapes for 40 million galaxies, corresponding to an effective density of 6.8 galaxies per square arcminute, and demonstrate a low level of systematic biases. This work serves as the basis for further cosmological studies that will use the full UNIONS survey of 4800 deg2 when completed. Methods. Here we present SHAPEPIPE, a newly developed weak-lensing pipeline. This pipeline makes use of state-of-the-art methods such as NGMIX for accurate galaxy shape measurement. Shear calibration is performed with metacalibration. We carry out extensive validation tests on the point spread function (PSF) and on the galaxy shapes. In addition, we create realistic image simulations to validate the estimated shear. Results. We quantify the PSF model accuracy and show that the level of systematics is low as measured by the PSF residuals. Their effect on the shear two-point correlation function is sub-dominant compared to the cosmological contribution on angular scales < 100′. The additive shear bias is below 5 × 10−4, and the residual multiplicative shear bias is at most 10−3 as measured on image simulations. Using complete orthogonal sets of E-/B-mode integrals (COSEBIs), we show that there are no significant B-modes present in second-order shear statistics. We present convergence maps and see clear correlations of the E-mode with known cluster positions. We measure the stacked tangential shear profile around Planck clusters at a significance higher than 4σ.

On-machine self-calibration of rotary encoders in the normal vector tracing method.

We developed a nano-profiler to determine the shape from slope distribution data for highly accurate free-form surface shape measurement; for example, high-precision optical systems are required in the x-ray and semiconductor fields. An accuracy of ±0.2 µrad is required to achieve a shape measurement accuracy of 30nm. The angle of the rotary stage is controlled by the rotary encoder, and the calibration curve of the rotary encoder is guaranteed by the national standard machine. However, the error associated with assembling the rotary encoder to the device is not included in the national standard machine's calibration data. Therefore, we propose a method that combines self-calibration with six encoder heads and calibration with a national standard machine. Using this method reduces the extent of calibration by the national standard machine and minimizes the influence of the assembly error. Furthermore, to verify whether the calibration in the proposed method is appropriate, a new encoder for evaluation was installed and evaluated. The results revealed that the influence of the assembly error was reduced to the minimum, and the difference in the calibration between the encoder for evaluation and the national standard machine was 0.027 µrad.

RadioLensfit: An HPC tool for accurate galaxy shape measurement with SKA

The new generation radio telescopes, such as the Square Kilometre Array (SKA), are expected to reach sufficient sensitivity and resolution to provide large number densities of resolved faint sources, and therefore to open weak gravitational lensing observations to the radio band. In this paper we present RadioLensfit, an open-source tool for an efficient and fast galaxy shape measurement for radio weak lensing shear. It performs a single source model fitting in the Fourier domain, after isolating the source visibilities with a sky model and a faceting technique. This approach makes real sized radio datasets accessible to an analysis in this domain, where data is not yet affected by the systematics introduced by the non-linear imaging process. We detail the implementation of the code and discuss limitations of the source extraction algorithm. We describe the hybrid parallelization MPI+OpenMP of the code, implemented to exploit multi-node HPC infrastructures for accelerating the computation and dealing with very large datasets that possibly cannot entirely be stored in the memory of a single processor. Finally, we present performance results both in terms of measurement accuracy and code scalability on SKA-MID simulated datasets. In particular, we compare shape measurements of 1000 sources at the expected source density in SKA Phase 1 with the ones obtained from the same dataset in a previous work by a joint fitting of the raw visibility data, and show that results are comparable while the computational time is highly reduced.

Matching of CAD model projections and X-ray projection images for shape inspection of metal assemblies

X-ray computed tomography (CT) facilitates non-destructive inspection of the interior of mechanical assemblies. However, industrial materials often contains heavy metals; which causes in CT artifacts in CT images and prevents accurate segmentation or shape measurement. To overcome this issue, in this study, we proposed a new inspection method that uses X-ray projection images and CAD meshes. We extracted characteristic features as edges and curved surfaces as feature images and used those for component alignments and shape measurements. The idea behind this is that these characteristic 3D features are captured as crease pixels in 2D projection images; thus, they can be extracted and compared with CAD meshes directly without conducting actual CT reconstructions. Furthermore, we conducted preliminary tests with some metal parts, which indicated that our approach can effectively inspect multi-material mechanical assemblies.

Experimental Detection and Measurement of Crack-Type Damage Features in Composite Thin-Wall Beams Using Modal Analysis.

An experimental proof-of-concept for damage detection in composite beams using modal analysis has been conducted. The purpose was to demonstrate that damage features can be detected, located, and measured on the surface of a relatively complex thin-wall beam made from composite material. (1) Background: previous work has been limited to the study of simple geometries and materials. (2) Methods: damage detection in the work is based on the accurate measurement of mode shapes and an appropriate design of the detection mesh. Both a method requiring information about the healthy structure and a baseline-free method have been implemented. (3) Results: short crack-type damage features, both longitudinal and transverse, were detected reliably, and the true length of the crack can be estimated from the damage signal. Simultaneous detection of two cracks on the same sample is also possible. (4) This work demonstrates the feasibility of automated damage detection in composite beams using sensor arrays.

High-speed and accurate 3D shape measurement using DIC-assisted phase matching and triple-scanning

Structured-light (SL) three-dimensional (3D) shape measurements technique has been extensively studied and applied in various fields, but the real-time capability of SL system is limited by the number of projected SL patterns. This paper presents a digital image correlation (DIC) assisted phase matching and triple-scanning method for high-speed and accurate 3D shape measurements based on the combined projection of three-step phase-shifting patterns and one speckle pattern. Benefiting from the triple-view geometric constraints and DIC technique, stereo correspondence and absolute fringe order of each valid pixel in the left wrapped phase map can be explored without requiring spatial phase unwrapping process or any priori information, the initial disparity map and absolute phase map can be generated. Subsequently, an efficient disparity refinement scheme based on connectivity area segmentation and neighbor disparity check is applied to the initial disparity map and absolute phase map to compensate the pixels with wrong disparities. Several efficient strategies are developed in the pipeline of data processing to speed up the algorithm. To improve the measurement accuracy, stereo SL model is proposed to make adequate use of triple-view information to calculate 3D coordinates using the disparity map and absolute phase map, and the measurement error has been reduced by 33% compared with the conventional methods. The experimental results demonstrate that the proposed method can perform high-speed and accurate 3D shape measurements for arbitrary surfaces and dynamic scenes.

부싱 가공 불량 감소를 위한 내경 정밀측정 기반 인라인 가공품질 예측 시스템 개발

A single defect in the bushings of a tank track can cause track breakaway and thus it is important to evaluate the forming accuracy of the bushing during its mass production. In this research, an inline bushing measurement system, comprising of two linear variable differential transformer (LVDT) probes, precision alignment modules, and a stepping motor, was developed for an accurate inner shape prediction and measurement. In addition, an error prediction algorithm was developed for monitoring and controlling the machining quality of the bushing. The inner diameter was calculated by considering the best-fit circle obtained using the LVDT measurement data and the least squares method. The changes in the inner circle diameter were considered to be a machining error due to tool (insert) wear. The measured uncertainty of inner circle diameter was 0.015 mm. The proposed system was verified using the quality decision experimental results of the sample bushing.

Accurate and dynamic 3D shape measurement with digital image correlation-assisted phase shifting

Phase-shifting profilometry (PSP) has been widely used in structured-light (SL) systems for three-dimensional (3D) shape measurements, but the speed of the PSP technique is limited by increased phase-shifting patterns. This paper proposes an accurate and dynamic 3D shape measurement method by projecting only four patterns, including three-step phase-shifting patterns and one speckle pattern. Three-step phase-shifting images are used to obtain the initial unwrapped phase map with phase ambiguity. Based on the principle of digital image correlation and multi-view geometry, the absolute phase can be recovered reliably without requiring any embedded features or pre-defined information of the object. To improve the measurement accuracy, the projector coordinate is used as the measuring coordinate to establish a novel stereo SL system model. By solving a least square solution using the triple-view information, accurate 3D surface data can be reconstructed. The experimental results indicate that the proposed method can perform high-speed and accurate 3D shape measurements with an accuracy of 10.64 μm, which is superior to conventional methods and has certain instructive significance for 3D profilometry and measurement engineering.

A coarse-to-fine leaf detection approach based on leaf skeleton identification and joint segmentation

Plant leaf detection and segmentation are challenging tasks for in-situ plant image analysis. Here, a novel leaf detection scheme is proposed to detect individual leaves and accurately determine leaf shapes in natural scenes. A leaf skeleton-extraction method was developed by analysing local image features of skeleton pixels. Approximate positions of individual leaves were determined according to the main leaf skeleton. Sub-images containing only single target leaves were extracted from whole plant images according to position and size of the main skeleton. Accurate leaf analysis was conducted on the sub-images of individual leaves. Leaf direction was calculated by examining the structure of the main leaf skeleton. Joint segmentation by combining region and active shape model was presented to accurately elucidate leaf shape. Leaf detection was implemented using deep learning approach, Faster R–CNN. A plant leaf image dataset containing four types of leaf images of different complexity was built to evaluate detection algorithms. Plant leaves with occlusions and complex backgrounds were effectively detected and their shapes accurately determined. Detection accuracy of the proposed method was 81.10%–100%, and 86.75%–100% for Faster R–CNN. The method demonstrated a comparable detection ability to that of Faster R–CNN. Furthermore, the rates of success to determine leaf direction by our method ranged between 89.06% and 100%, while the average measurement difference was 1.29° compared with manual measurement. The accuracy of shape measurement was 75.95%–100% for all types of plant images. Therefore, this method is accurate and stable for precise leaf measurements in agricultural applications. • A coarse-to-fine leaf detection scheme was presented to determine leaf shapes in situ. • Leaf detection and direction estimation were developed by examining main leaf skeleton. • Individual leaf shape is determined by combining region growing and deformable models. • A leaf image dataset with different complexity was built for performance evaluation. • Our method showed comparable detection accuracy to Faster RCNN between 81.10%~100%.

Coupling effects of particle shape and cyclic shear history on shear properties of coarse-grained soil–geogrid interface

Accurate measurement of particle shape is necessary to properly investigate the cyclic shear behavior of a soil–geogrid interface. In this study, the particle shapes of crushed limestone, quartz sand, round gravel and spherical granular media were described and quantified in terms of particle regularity. Cyclic direct shear tests were then performed with particles of various shapes for different relative densities and shear displacement amplitudes. Monotonic direct shear tests and post cyclic direct shear tests were performed to compare the relationship between particle regularity and interface friction angle. The test results showed that the interface underwent cyclic shear hardening for relative densities between 0.453 and 0.672. The area of the hysteresis loop, cyclic peak shear strength and maximum vertical displacement increased with increasing shear displacement amplitude, and decreased with increasing particle regularity. In monotonic and post cyclic direct tests, the friction angle decreased linearly as particle regularity increased. Comparing to the monotonic direct shear tests, the slope and intercept of the regression lines increased in the post cyclic direct shear tests after undergoing cyclic shearing. This study shows that it is important to analyze the interface shear behavior in the monotonic, cyclic and post cyclic direct shear tests using parameterized particle shapes.

Self-registration shape measurement based on fringe projection and structure from motion.

As an accurate and efficient shape measurement method, fringe-projection-based three-dimensional (3D) reconstruction has been extensively studied. However, patchwise point cloud registration without extra assistance is still a challenging task. We present a flexible and robust self-registration shape measurement method based on fringe projection and structure from motion (SfM). Other than ordinary structured-light measurement devices in which the camera and the projector are rigidly connected together, the camera and the projector in our method can be moved independently. An image-capturing scheme and underlying image-matching strategy are proposed. By selectively utilizing some sparse correspondence points across the fringe images as virtual markers, the global positions of the camera and the projector corresponding to each image are calculated and optimized under the framework of SfM. Dense global 3D points all over the object surface are finally calculated via forward intersection. Experimental results on different objects demonstrate that the proposed method can obtain a self-registered 3D point cloud with comparable accuracy to the state-of-the-art techniques by using only one camera and one projector, requiring no post-registration procedures and no assistant markers.

Non-invasive single-cell morphometry in living bacterial biofilms

Fluorescence microscopy enables spatial and temporal measurements of live cells and cellular communities. However, this potential has not yet been fully realized for investigations of individual cell behaviors and phenotypic changes in dense, three-dimensional (3D) bacterial biofilms. Accurate cell detection and cellular shape measurement in densely packed biofilms are challenging because of the limited resolution and low signal to background ratios (SBRs) in fluorescence microscopy images. In this work, we present Bacterial Cell Morphometry 3D (BCM3D), an image analysis workflow that combines deep learning with mathematical image analysis to accurately segment and classify single bacterial cells in 3D fluorescence images. In BCM3D, deep convolutional neural networks (CNNs) are trained using simulated biofilm images with experimentally realistic SBRs, cell densities, labeling methods, and cell shapes. We systematically evaluate the segmentation accuracy of BCM3D using both simulated and experimental images. Compared to state-of-the-art bacterial cell segmentation approaches, BCM3D consistently achieves higher segmentation accuracy and further enables automated morphometric cell classifications in multi-population biofilms.

Parametric Shape Estimation of Human Body Under Wide Clothing

The shape of the human body plays an important role in many applications, such as those involving personal healthcare and virtual clothing try-ons. However, accurate body shape measurements typically require the user to be wearing a minimal amount of clothing, which is not practical in many situations. To resolve this issue using deep learning techniques, we need a paired dataset of ground-truth naked human body shapes and their corresponding color images with clothes. As it is practically impossible to collect enough of this kind of data from real-world environments to train a deep neural network, in this paper, we present the Synthetic dataset of Human Avatars under wiDE gaRment (SHADER). The SHADER dataset consists of 300,000 paired ground-truth naked and dressed images of 1,500 synthetic humans with different body shapes, poses, garments, skin tones, and backgrounds. To take full advantage of SHADER, we propose a novel silhouette confidence measure and show that our silhouette confidence prediction network can help improve the performance of state-of-the-art shape estimation networks for human bodies under clothing. The experimental results demonstrate the effectiveness of the proposed approach. The code and dataset are available at https://github.com/YCL92/SHADER .

Accurate phase analysis of interferometric fringes by the spatiotemporal phase-shifting method

Phase-shifting interferometry (PSI) has been widely applied in the field of accurate optical methodology. However, the fluctuation of background and amplitude intensities due to the instability of laser source, and phase-shifting error or vibration are significant problems for the PSI. In this study, the spatiotemporal phase-shifting method (ST-PSM) (Ri S et al 2019 J. Opt. 21 095702), which is a highly accurate and robust phase analysis method using spatial and temporal intensities information simultaneously, is first applied to laser interferometry to achieve a stable measurement. Through several simulations, three effects of fluctuations in background and amplitude intensities, phase-shifting error were investigated. As a result, we clarified that the periodic phase error with fundamental or second harmonic frequencies occurs in the conventional PSM method, whereas no periodic error occurs in the ST-PSM. Besides, the ST-PSM is also robust to the noise either for uniform or distorted interference fringe images. In the Michelson laser interferometer experiment, the ST-PSM realizes a much more stable measurement of phase and phase gradient distributions than the PSM. We have revealed the excellent performance and the striking advantage that ST-PSM is entirely free of periodic errors in a similar manner to the simulations. Therefore, laser interferometry using the ST-PSM can be expected to apply to various applications, including the extremely accurate non-contact shape and deformation measurement, as well as thickness measurement of transparent materials in life and material sciences.