Image Points Research Articles

Effect of light source wavelength on surface defect imaging in deep-water concrete dams

This study explores the relationship between surface defect imaging in deep-water concrete dams and the wavelength of the light source, aiming to improve concrete defect image quality by varying the wavelength. The underwater optical imaging model of concrete dam surfaces was developed by applying the principle of object reflection and the underwater optical propagation model. This model reflects the effect of different light wavelengths on underwater concrete defect imaging. Through a self-designed underwater optical darkroom test platform, defect image acquisition tests were conducted under various color light conditions. The results reveal the significant influence of light source wavelength on images captured in both air and water. Furthermore, under the same initial illuminance, blue light significantly enhanced the average gray value, sharpness, contrast, and the number of feature points in underwater defect images, exhibiting increments of 2.97 times, 0.48 times, 2.70 times, and 2.95 times, respectively, compared to white light. These findings can serve as a reference for the selection of color-light for the non-destructive testing of surface defects in actual underwater concrete dams.

Color image hybrid noise filtering algorithm based on deep convolution neural network

To solve the problems of the classical color image hybrid noise filtering method, a deep convolutional neural network improved by evolutionary strategy and jump connection is proposed and applied to the filtering noise reduction of color images. First, the color information of the image is described quantitatively by digital means. The common method is to build color space model. According to the characteristics of color and the needs of human vision, mathematical algorithms are used to convert images into machine recognizable data. The distance between pixels is measured according to the difference of pixels in the color image determined above. Then, the probability density function and noise probability density function of Gaussian noise are calculated to determine the hybrid noise feature points of color image. The filtering algorithm structure designed this time is as follows: A color image hybrid noise filter is used to map the noise points in the mapped image to the feature space, and linear regression is performed on the noise point data. Relaxation variables are introduced in the network to improve the denoising ability. The experimental results show that the Peak Signal to Noise Ratio and structural similarity index values of the filtering algorithm designed in this study are higher than the two methods in the literature. The color image hybrid noise filtering model designed in this study has good filtering performance, good image cleanliness, and high filtering efficiency.

Intelligent Extraction of Surface Cracks on LNG Outer Tanks Based on Close-Range Image Point Clouds and Infrared Imagery

Intelligent Extraction of Surface Cracks on LNG Outer Tanks Based on Close-Range Image Point Clouds and Infrared Imagery

A calibration method of line-structured light system for measuring outer circle dimension during machining

The line-structured light system is widely utilized for various 3D profile measurements due to its convenience and high efficiency. However, the existing calibration methods primarily focus on establishing the relationship between object points and image points, which imposes stringent requirements on the calibration target. To address this issue, a practical calibration method based on interpolation function is proposed in order to measure a specific 3D profile - namely, the diameter of a rotating workpiece during machining. Firstly, the light stripe projected onto an ordinary machined workpiece by a light plane is captured using a Charge-Coupled Device (CCD) camera and subsequently fitted to a quadratic elliptic curve after undergoing image processing. Subsequently, the cubic spline interpolation function is selected to directly establish a mapping relationship between geometric characteristics of the elliptic curve and the diameter measurement. Several experiments are conducted to evaluate the proposed methods. The experimental results demonstrate that compared with two comparison methods, our approach reduces the calibration error of linear structured light systems by 69 % and 51 %, respectively; furthermore, it enables restoration of smooth and detailed 3D models through parallel movement of the system. Moreover, our entire calibration process proves practicable in simplifying experimental procedures while remaining suitable for industrial inspection applications.

RCI-Seg: Robust click-based interactive segmentation framework with deep reinforcement learning for biomedical images

Recently, interactive segmentation models have achieved remarkable success in the field of biomedical images. However, these models rely on the accurate and high-quality interaction information provided by users, otherwise the segmentation performance will be seriously affected. This problem is more severe in multi-target biomedical images, which means that an image contains multiple targets of interest. It is extremely challenging for users to always maintain high-quality interactions. In this paper, we propose a novel two-stage segmentation model with robust interaction points for biomedical images. In the first stage, we implement robust interaction points based on user initial interaction points and the deep reinforcement learning (DRL) model. Specifically, we build a reinforcement learning environment to simulate the movement of interaction points with agents, and obtain improved interaction points (clue points) that are beneficial for segmentation. In the second stage, we use a convolutional neural network (CNN) model to achieve segmentation by combining clue points and biomedical image. We validate the performance of our approach on five public biomedical image datasets. The experimental results show that the proposed approach outperforms several SOTA methods in multiple metrics.

High-Speed Schlieren Imaging And PIV Of Unsteady Free Jet Injection With Plume Formation

This work aims to investigate Schlieren image correlation velocimetry for flow field quantification of gaseous jet injection in non-manipulable apparatus like flow chambers. Such flow cases often struggle using tracer-based methods: Seeding particles may locally cause technical problems, or the plant design prevents the principal opportunity of tracer adding, both of which lead to a reduced method repertory. This is specifically the case under flow conditions, where contamination of apparatus internals by seeding particles should be avoided completely. Therefore, in this paper, we explore a seedless, refraction-based method to quantify the velocity field of a jet plume. As an experimental flow case, we examine a transient jet release into a closed compartment under nearly atmospheric conditions. For simplification and greater clarity, the laboratory model geometry is kept essentially two-dimensional, thus we chose a flat-jet nozzle as injection device. The experimental investigations cover high-speed Schlieren imaging and laser sheet visualization, followed by digital image correlation. Combining the traditionally qualitative Schlieren approach for flow visualization with Fast Fourier Transform and cross-correlation algorithms, we calculated the velocity vector field of an unsteady jet plume formation. Furthermore, we determined the axial jet velocity profile at steady-state conditions. Our research findings highlight the applicability of Schlieren image correlation velocimetry to quantify gaseous jet formation at encased flow sections spatially. Concluding, the results coincide with data from particle image velocimetry measurements and point out the potential of expanding flow diagnostics to hitherto hardly explored (industrial) flow cases.

Computation of Green’s Function in a Strongly Heterogeneous Medium Using the Lippmann–Schwinger Equation: A Generalized Successive Over-Relaxion plus Preconditioning Scheme

The computation of Green’s function is a basic and time-consuming task in realizing seismic imaging using integral operators because the function is the kernel of the integral operators and because every image point functions as the source point of Green’s function. If the perturbation theory is used, the problem of the computation of Green’s function can be transformed into one of solving the Lippmann–Schwinger (L–S) equation. However, if the velocity model under consideration has large scale and strong heterogeneity, solving the L–S equation may become difficult because only numerical or successive approximate (iterative) methods can be used in this case. In the literature, one of these methods is the generalized successive over-relaxation (GSOR) iterative method, which can effectively solve the L–S equation and obtain the desired convergent iterative series. However, the GSOR iterative method may encounter slow convergence when calculating the high-frequency Green’s function. In this paper, we propose a new scheme that utilizes the GSOR iterative with a precondition to solve the complex wavenumber L–S equation in a slightly attenuated medium. The complex wavenumber with imaginary components localizes the energy of the background Green’s function and reduces its singularity by enabling exponential decay. Introduction of the preconditioning operator can further improve the convergence speed of the GSOR iterative series. Then, we provide a preconditioned generalized successive over-relaxation (pre-GSOR) iterative format. Our numerical results show that if an appropriate damping factor and a proper preconditioning operator are selected, the method presented here outperforms the GSOR iterative for the real wavenumber L–S equation in terms of the convergence speed, accuracy, and adaptation to high frequencies.

Optical flow matching with automatically correcting the scale difference of tunnel parallel photogrammetry

AbstractUsing parallel photography to model tunnels is an efficient method for real scene modelling. Aiming at the problem that the accuracy of optical flow matching in tunnel parallel photography sequence photos is severely affected by the scale deformation of stereo images, a novel optical flow matching method with automatically correcting the scale difference of tunnel parallel photography stereo images is proposed from the perspective of imaging relationships. By analysing the distribution pattern of scale difference in stereo images, a model is obtained in which the scale difference of image points is symmetrically distributed radially on the image and follows a power function growth. Introduce it into traditional optical flow matching to correct image scale differences based on the model to improve matching accuracy. The mean square error of the optical flow matching after correcting scale difference in the experiment is less than 0.3 pixels, which is at least 34.3% higher than before correction and a maximum improvement of 45.5% in the experimental results. The research result indicates that the proposed optical flow matching method with automatically correcting the scale difference has a significant effect on improving the accuracy of tunnel parallel photography image matching and modelling.

Kalman Filter for a Particular Class of Dynamic Object Images

We discuss the problem of estimating the state of a dynamic object by using observed images generated by an optical system. The work aims to implement a novel approach that would ensure improved accuracy of dynamic object tracking using a sequence of images. We utilize a vector model that describes the object image as a limited number of vertexes (reference points). Upon imaging, the object of interest is assumed to be retained at the center of each frame, so that the motion parameters can be considered as projections onto the axes of a coordinate system matched with the camera's optical axis. The novelty of the approach is that the observed parameters (the distance along the optical axis and angular attitude) of the object are calculated using the coordinates of specified points in the object images. For estimating the object condition, a Kalman-Bucy filter is constructed on the assumption that the dynamic object motion is described by a set of equations for the translational motion of the center of mass along the optical axis and variations in the angular attitude relative to the image plane. The efficiency of the proposed method is illustrated by an example of estimating the object's angular attitude.

Point of care magnetic resonance neonatal neuroimaging applications and early imaging in infants under active therapeutic hypothermia: a perspective.

As care of the most vulnerable infants in the neonatal intensive care unit (NICU) evolves, improved and real-time understanding of brain health becomes key. The availability of an in-NICU magnetic resonance imaging (MRI) scanner provides unique options to bedside care providers and researchers. We present our perspective on the 1-Tesla MRI unit in our NICU and its utilities and applications both in the clinical and research fields. We also discuss our experience with early and serial MRI in a cohort of infants with hypoxic-ischemic encephalopathy while undergoing therapeutic hypothermia, using a compatible cooling blanket and monitoring apparatus with special insight into the planning and organization between providers, and parental perspectives around early, detailed imaging.

Uniform-sampling foveated Fourier single-pixel imaging

Inspired by the retina of human eye, foveated imaging is an effective way to achieve high imaging quality and high imaging efficiency. However, with an inherent property of nonuniformity, advantages of foveated imaging cannot be played well due to the confliction with many theories having uniform properties. Thus, an innovative concept “uniform-sampling foveated imaging” is proposed to break the barrier of foveated imaging and uniform imaging. In this work, the concept is used as a uniform-sampling foveated Fourier single-pixel imaging (UFFSI) to achieve high imaging quality and high imaging efficiency of single-pixel imaging (SPI). First, by flexibly using the proposed three kinds of foveated pattern structures of foveated SPI, namely, “circular structure” via log-polar transformation, “rectangular structure” via log-rectilinear transformation and “rotating-rectangle structure” via log-rectilinear-rotation transformation, the total data number is reduced radically by the data redundancy reduction only requiring high resolution (HR) on regions of interest (ROIs). Next, by a nonuniform weight distribution processing based on the innovative concept of “uniform-sampling foveated imaging”, nonuniform sampling is transformed into uniform sampling to obtain the same uniform optical power as the uniform HR FSI, thus, sparse spectrum property and fast Fourier transform of FSI are used accurately and directly to obtain high imaging quality with further reduced measurements and fast reconstruction. Experimentally, in a large-scale scene, at an ultralow sampling ratio of 0.78 % referring to uniform HR FSI with 1024 × 768 pixels, by using UFFSI with 89 % reduction of data redundancy, ROI has a significantly better imaging quality with around 3.3times reduction in reconstruction time. We hope this work can provide a breakthrough point for future foveated imaging and real-time SPI in real life.

Gastric Emptying Scintigraphy Protocol Optimization Using Machine Learning for the Detection of Delayed Gastric Emptying.

The purpose of this study is to examine the feasibility of a machine learning (ML) system for optimizing a gastric emptying scintigraphy (GES) protocol for the detection of delayed gastric emptying (GE), which is considered a primary indication for the diagnosis of gastroparesis. An ML model was developed using the JADBio AutoML artificial intelligence (AI) platform. This model employs the percent GE at various imaging time points following the ingestion of a standardized radiolabeled meal to predict normal versus delayed GE at the conclusion of the 4 h GES study. The model was trained and tested on a cohort of 1002 patients who underwent GES using a 70/30 stratified split ratio for training vs. testing. The ML software automated the generation of optimal predictive models by employing a combination of data preprocessing, appropriate feature selection, and predictive modeling analysis algorithms. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was employed to evaluate the predictive modeling performance. Several models were developed using different combinations of imaging time points as input features and methodologies to achieve optimal output. By using GE values at time points 0.5 h, 1 h, 1.5 h, 2 h, and 2.5 h as input predictors of the 4 h outcome, the analysis produced an AUC of 90.7% and a balanced accuracy (BA) of 80.0% on the test set. This performance was comparable to the training set results (AUC = 91.5%, BA = 84.7%) within the 95% confidence interval (CI), demonstrating a robust predictive capability. Through feature selection, it was discovered that the 2.5 h GE value alone was statistically significant enough to predict the 4 h outcome independently, with a slightly increased test set performance (AUC = 92.4%, BA = 83.3%), thus emphasizing its dominance as the primary predictor for delayed GE. ROC analysis was also performed for single time imaging points at 1 h and 2 h to assess their independent predictiveness of the 4 h outcome. Furthermore, the ML model was tested for its ability to predict "flipping" cases with normal GE at 1 h and 2 h that became abnormal with delayed GE at 4 h. An AI/ML model was designed and trained for predicting delayed GE using a limited number of imaging time points in a 4 h GES clinical protocol. This study demonstrates the feasibility of employing ML for GES optimization in the detection of delayed GE and potentially shortening the protocol's time length without compromising diagnostic power.

The Legacy of Sycamore Gap: The Potential of Photogrammetric AI for Reverse Engineering Lost Heritage with Crowdsourced Data

Abstract. The orientation of crowdsourced and multi-temporal image datasets presents a challenging task for traditional photogrammetry. Indeed, traditional image matching approaches often struggle to find accurate and reliable tie points in images that appear significantly different from one another. In this paper, in order to preserve the memory of the Sycamore Gap tree, a symbol of Hadrian's Wall that was felled in an act of vandalism in September 2023, deep-learning-based features trained specifically on challenging image datasets were employed to overcome limitations of traditional matching approaches. We demonstrate how unordered crowdsourced images and UAV videos can be oriented and used for 3D reconstruction purposes, together with a recently acquired terrestrial laser scanner point cloud for scaling and referencing. This allows the memory of the Sycamore Gap tree to live on and exhibits the potential of photogrammetric AI (Artificial Intelligence) for reverse engineering lost heritage.

Double-clad optical fiber as a single-point sensor of imaging quality for scanning laser system

We have developed a single-point optical sensor of imaging quality based on a double-clad optical fiber for laser beam scanning imaging systems. The optimal imaging quality, primarily affected by the accurate focusing, is determined by maximizing the ratio of the optical power coupled to the single-mode core and the inner multi-mode cladding of the double-clad fiber collecting the light scattered from the sample. We present simulations of the sensor’s performance at different levels of defocus and validate it experimentally by optimizing focus when imaging a scattering phantom and a living human eye with a scanning laser ophthalmoscope. In contrast to the existing image-based optimization procedures, our sensor provides feedback independent of illumination intensity or sample reflectivity that can be obtained for each image point, thus exceeding the imaging framerate. Importantly, our sensor can seamlessly be integrated with most laser scanning imaging systems, providing online feedback for correction optics.

Three-Dimensional X-Ray Computed Tomography Image Segmentation and Point Cloud Reconstruction for Internal Defect Identification in Laser Powder Bed Fused Parts

Abstract Defects shape, volume, and orientation all have a direct impact on the mechanical properties of Laser Powder Bed Fused (L-PBF-ed) parts. Therefore, it is necessary to evaluate and analyze the three-dimensional (3D) geometrical characteristics of these defects. X-ray Computed Tomography (XCT) can reveal an object's internal structure by volumetric scanning through its building direction. Point clouds are 3D data that can be extracted from the stack of XCT images taken from a part to perform further analysis. This study presents a novel approach for 3D segmentation and geometrical analysis of L-PBF defect structures from XCT images. The proposed method integrates Voronoi labeling and 3D point cloud reconstruction to reveal individual defect characteristics from the XCT image stack of a part. A case study showed the proposed methodology's effectiveness in identifying and characterizing defect regions in L-PBF-ed Cobalt-Chrome (CoCr) parts.

P‐120: Sub‐Pixel Marking Method for the Elimination of Voxel Drifting in Integral Imaging Display System

Integral imaging is a type of true three‐dimensional (3D) display technology that uses a lens array to reconstruct vivid 3D images with full parallax and true color. In order to present a high‐quality 3D image, it's vital to correct the axial position error caused by the misalignment and deformation of the lens array which makes the reconstructed lights deviate from the correct directions, resulting in severe voxel drifting and image blurring. We proposed a sub‐pixel marking method to measure the axial position error of the lenses with great accuracy by addressing the sub‐pixels under each lens and forming a homologous sub‐pixel pair. The proposed measurement method relies on the geometric center alignment of image points, which makes the measurement accuracy high. Additionally, a depth‐based sub‐pixel correction method was proposed to eliminate the voxel drifting. The proposed correction method takes the voxel depth into consideration. The experimental results well confirmed that the proposed measuring and correction methods can greatly suppress the voxel drifting caused by the axial position error of the lenses, and greatly improve the 3D image quality.

Quantitative measurement of urban spatial vitality by integrating physical built environment and subjective perception dimensions

Urban space vitality is a critical indicator for supporting rational urban spatial planning and updating and formulating sustainable development strategies. However, in many areas (e.g., aging urban areas), there is often a mismatch between the conditions of the physical built environment and its spatial attractiveness. Traditional methods based on physical space design theory often fail to accurately measure the spatial vitality of these areas. Street view images directly reflect the actual construction situation and effectively compensate for the lack of visual, subjective, perception dimension information. This study proposes a novel method that integrates objective and subjective dimensions to measure urban vitality, which is captured by incorporating spatial data of points of interest, building outlines, road networks, and street view images. Then, taking mobile phone signaling data as a source of ground truth validation, we choose Nanjing as a case study to demonstrate that our multidimensional fusion method exhibits higher explanatory power and better alignment with actual conditions by comparing it against single-dimensional methods. The results underscore the importance of integrating subjective and perceptual dimensions in measurements of urban vitality. We believe that the localized samples of the subjective perception survey will further enhance the accuracy and generalizability of this method in the future.

Development of an Uneven Terrain Decision-Aid Landing System for Fixed-Wing Aircraft Based on Computer Vision

This paper presents a computer vision-based standalone decision-aid landing system for light fixed-wing aircraft, aiming to enhance safety during emergency landings. Current landing assistance systems in airports, such as Instrument Landing Systems (ILSs) and Precision Approach Path Indicators (PAPIs), often rely on costly and location-specific ground equipment, limiting their utility for low-payload light aircraft. Especially in emergency conditions, the pilot may be forced to land on an arbitrary runway where the road flatness and glide angle cannot be ensured. To address these issues, a stereo vision-based auxiliary landing system is proposed, which is capable of estimating an appropriate glide slope based on the terrain, to assist pilots in safe landing decision-making. Moreover, in real-world scenarios, challenges with visual-based methods arise when attempting emergency landings on complex terrains with diverse objects, such as roads and buildings. This study solves this problem by employing the Gaussian Mixture Model (GMM) to segment the color image and extract ground points, while the iterative weighted plane fitting (IWPF) algorithm is introduced to mitigate the interference of outlier feature points, reaching a highly robust plane normal estimation. With the aid of the proposed system, the pilot is able to evaluate the landing glide angle/speed with respect to the uneven terrain. Simulation results demonstrate that the proposed system can successfully achieve landing guidance in unknown environments by providing glide angle estimations with an average error of less than 1 degree.

An assessment of 2-D and 3-D interest point detectors in volumetric images

Many interest point detectors have been designed so far to work in two dimensional (2-D) images. However, expansion of these detectors into the third dimension for three dimensional (3-D) images can refine their representational power. This paper presents how the Harris corner, LoG filtering-based blob, and salient regions detectors can be expanded to find interest points in volumetric images handling multiple slices collectively. Performances of 2-D and 3-D detector implementations were assessed both qualitatively and quantitatively with value combinations of different parameters using metrics such as F1-score, localization error, and repeatability in binary images of twenty 3-D object models from the Princeton Shape Benchmark (PSB). Computation of F1-score and localization error depended on some manually marked ground truth points, while repeatability measurement was according to the proximity of the detected point sets. The 3-D detectors were evaluated as more successful in capturing distinctive and sparse interest points on 3-D object surfaces in qualitative analyses. Despite having greater computational complexities, most of the 3-D detectors yielded better average F1-score, localization accuracy, and repeatability given uniqueness constraint on the matched points in quantitative analyses. Therefore, the 3-D detectors appear preferable when longer working durations or sparser representations would not constitute any disadvantage.

Highly accurate three-dimensional measurement of large structures using multiple stereo vision with improved two-step calibration algorithm

A multiple stereo-based three-dimensional (3D) measurement method of large-scale structures is presented, which replaces time-consuming and laborious 3D measurement procedure of laser-based conventional equipment. To have 3D reconstruction accuracy with millimeter-precision, two-step calibration algorithms are improved. Specifically, we use 2D epipolar constraint, using fundamental matrix determined by matched points, to new combination of objective function for intrinsic parameter calibration. We additionally propose an onsite structure parameter calibration algorithm using different epipolar constraint of measurement points in images of large ship block as well as reprojection error of checkerboard, to bypass the limitation of the coverage of checkerboard in large-scale measurement. The actual 3D measurement of the three ship blocks is conducted, and the 3D reconstruction accuracy is validated by comparing the 3D coordinates measured by total station. The experimental results showed that the mean 3D reconstruction error ranged from 6 to 14 mm.