Surface Imaging Research Articles

Long-term Doppler imaging of the star XX Trianguli indicates chaotic non-periodic dynamo

Sunspots are the most well-known manifestations of solar magnetic fields and exhibit a range of phenomena related to the interior dynamo. Starspots are the direct analogs of sunspots on other stars but with the big observational restriction that we usually cannot resolve other star’s surfaces. In this paper we employ an indirect surface imaging technique called Doppler imaging and present 99 independent Doppler images of the star XX Trianguli. The star was selected because it had shown a gigantic star spot in a previous study and was thus well suited for a long-term monitoring effort. We combine the Doppler images into a movie visualizing the star’s surface spot evolution for the past 16 years. Stellar-disk photocenter displacements of up to 24 μas, or about 10% of the stellar disk radius, are reconstructed, but do not show the typical solar-like periodic behavior that could be interpreted as an activity cycle. It suggests a mostly chaotic, likely unperiodic, dynamo. These rotation-induced stellar photocenter variations pose an intrinsic limitation for astrometric exoplanet catches.

Multi-scale evaluation of surfactant effects on asphalt desorption behavior from oil sand surfaces

This study created a hydrophobic oil sand model to investigate the impact of surfactants on the desorption behavior of asphalt from solid surface. Cationic surfactant cetyl trimethyl ammonium bromide (CTAB) and anionic surfactant sodium dodecyl benzene sulfonate (SDBS) were used to soak the model to observe the effect of surfactants on asphalt desorption. The effect of surfactant pretreatment on the wettability of the substrate was evaluated by measuring the contact angle of ultrapure water on the substrate and observing the morphological changes of the solid surface by atomic force microscopy. The results show that the contact angle of the model treated with SDBS decreases to 10° after 3 days, which significantly improves the wettability of the substrate surface and promotes the desorption of asphalt. However, CTAB has no obvious effect on asphalt desorption. The standard deviation of the gray value of the sand surface image is significantly reduced to 38.693 after the SDBS treatment, which can be seen by binarizing the image. This suggests that the asphalt and solution are evenly distributed and that there is hardly any discernible boundary. Nevertheless, neither of the two surfactants can improve the water-based extraction efficiency of oil sands after soaking treatment. The asphalt yield is less than 5% after 30 days of pretreatment with CTAB solution, while only 45% asphalt yield is obtained after 30 days of pretreatment with SDBS solution. Despite these variations, the quality of asphalt foam remains relatively the same. This may be related to the failure of surfactants to improve the surface wettability of hydrophobic sand. Therefore, the surface wettability of the sand is considered to be a key factor affecting the asphalt desorption and final yield. This study can provide a scientific basis for oil sand recovery to a certain extent.

Neural Basis of Perceptual Surface Qualities and Materials: Evidence from EEG Decoding

Abstract The human visual system can easily recognize object material categories and estimate surface properties such as glossiness and smoothness. Recent psychophysical and computational studies suggest that the material perception depends on global feature statistics. To elucidate dynamic neural representations underlying surface property and material perception in humans, we measured visual evoked potentials (VEPs) for 191 natural images consisting of 20 categories of materials and then classified material categories and surface properties from the VEPs. As a result, we found that material categories were correctly classified by the VEPs even at latencies of 150 msec or less. The apparent surface properties were also significantly classified within 175 msec (lightness, colorfulness, and smoothness) and after 200 msec (glossiness, hardness, and heaviness). The subsequent reverse-correlation analysis revealed that the VEPs at these latencies are highly correlated with low- and high-level global feature statistics of the surface images, indicating that neural activities about such global features are reflected in the VEPs. Moreover, by using deep generative models (multimodal variational autoencoder models) to reconstruct surface images from the VEPs via style information, we demonstrated that the reconstructed surface images are judged by observers to have very similar material categories and surface properties as the original natural surfaces. These results support the notion that neural representations of statistical features in the early cortical response play a crucial role in the perception and recognition of surface materials in humans.

Determining the Level of Threat in Maritime Navigation Based on the Detection of Small Floating Objects with Deep Neural Networks

The article describes the use of deep neural networks to detect small floating objects located in a vessel’s path. The research aimed to evaluate the performance of deep neural networks by classifying sea surface images and assigning the level of threat resulting from the detection of objects floating on the water, such as fishing nets, plastic debris, or buoys. Such a solution could function as a decision support system capable of detecting and informing the watch officer or helmsman about possible threats and reducing the risk of overlooking them at a critical moment. Several neural network structures were compared to find the most efficient solution, taking into account the speed and efficiency of network training and its performance during testing. Additional time measurements have been made to test the real-time capabilities of the system. The research results confirm that it is possible to create a practical lightweight detection system with convolutional neural networks that calculates safety level in real time.

Utilizing pretrained convolutional neural networks for crack detection and geometric feature recognition in concrete surface images

Accurate detection of concrete cracks and identification of crack geometric characteristics are essential for ensuring structural safety. To address this, a concrete surface crack detection method was developed using pretrained convolutional neural networks (CNNs). Additionally, a comprehensive framework was proposed that integrates the pretrained CNN as a feature extractor with various regression algorithms to recognize concrete crack features, including the crack area, maximum width, and average width. Results demonstrate that the detection accuracy and training speed of modified CNN models based on pretrained networks, such as VGG16 and MobileNet, outperform those of CNN models trained from scratch. Moreover, the established CNNs achieve high accuracy in handling diverse images affected by environmental disturbances and noise. The developed comprehensive framework successfully recognized crack geometric features in concrete surface images. Evaluation of four regression algorithms revealed that support vector regression (SVR) achieved R2 values of 0.863 for predicting crack area and 0.764 for predicting the crack average width, while the XGBoost regression algorithm yielded an R2 of 0.782 for predicting the crack maximum width.

Measurement of Sea Surface Characteristics from Radar Images Using Gradient Methods

Introduction. Remote sensing and monitoring of the sea surface are of great importance in such fields, as operational oceanography, environmental monitoring, etc. The ability to quickly assess the state of the sea surface is particularly relevant in areas that pose a danger to shipping, where rapid and accurate response becomes critical. Modern radars represent information as digital image series largely reminiscent to a frame series in a video stream, thus enabling the use of gradient methods originally designed for and proven successful in video analysis.Aim. Determination of sea wave characteristics from radar images using gradient motion estimation methods. The use of gradient methods will allow implementing additional tools for processing radar image series obtained from sea backscatter.Materials and methods. To assess the characteristics of the sea surface from radar images, gradient methods were used. To train the methods, a series of synthetic images of the sea surface obtained by mathematical modeling were used. To evaluate the effectiveness of the gradient methods, two representative experimental radar image series provided by the Institute of Oceanography RAS were employed.Results. Using gradient methods, the direction and speed of waves were calculated from several consecutive radar observations. Regression models of the dependence of calculated values on the specified ones were constructed. The Farneback and TV-L1 methods proved to be effective especially for obtaining the direction of the sea waves.Conclusion. An algorithm for evaluating speed and direction of the sea surface displacement using gradient methods was pre-trained using simulated model data. The implemented methods and algorithms for assessing the speed and direction of sea waves were validated using two experimental image series obtained from shipborne navigational radars.

Quantifying whole human hair scalp fibres of varying curl: A micro-computed tomographic study.

Scalp hair is a key feature of humans and its variability has been the subject of a broad range of studies. A small subset of these studies has focused on geometric quantification of human scalp hair fibres, however the use of race- and ethnicity-based classification systems makes it challenging to draw objective conclusions about fibre variability. Furthermore, sample preparation techniques for micro-imaging studies often alter the original form of hair fibres. This study sought to determine which of the commonly reported descriptors could be resolved using micro-computed tomography (micro-CT) for fibres of varying curl. Images obtained from micro-CT were used to reconstruct three-dimensional images that were then analysed. The study also explored the capabilities and limitations of micro-CT as an imaging modality by comparing and cross-validating findings with those obtained from scanning electron microscopy (SEM) and laser micrometry. The former deals with surface imaging while the latter deals with cross-sectional measurements. Micro-CT was found to be highly effective at resolving cross-sectional ellipsoidal parameters, but performed more poorly than SEM in reconstructing surface level details at a 2 resolution. The technique was, however, able to reveal the presence of the medulla in type VI (high curl) hair fibres. When compared with high curl fibres, greater intra-fibre variability was observed for the low and medium curl fibres, highlighting the importance more objective classificationsystems.

Mask-Space Optimized Transformer for Semantic Segmentation of Lithium Battery Surface Defect Images

The segmentation of surface defects in lithium batteries is crucial for enhancing the overall quality of the production process. However, the severe foreground–background imbalance in surface images of lithium batteries, along with the irregular shapes and random distribution of foreground regions, poses significant challenges for defect segmentation. Based on these observations, this paper focuses on the separation of foreground and background in surface defect images of lithium batteries and proposes a novel Mask Space Optimization Transformer (MSOFormer) for semantic segmentation of these images. Specifically, the Mask Boundary Loss (MBL) module in our model provides more efficient supervision during training to enhance the accuracy of the mask computation within the mask attention mechanism, thereby improving the model’s performance in separating foreground and background. Additionally, the Dynamic Spatial Query (DSQ) module allocates spatial information of the image to each query, enhancing the model’s sensitivity to the positions of small foreground targets in various scenes. The Efficient Pixel Decoder (EPD) ensures deformable receptive fields for irregularly shaped foregrounds while further improving the model’s performance and efficiency. Experimental results demonstrate that our method outperforms other state-of-the-art methods in terms of mean Intersection over Union (mIoU). Specifically, our approach achieves an mIoU of 84.18% on the lithium battery surface defect test set and 85.53% and 87.05% mIoUs on two publicly available defect test sets with similar defect characteristics to lithium batteries.

Rapid acquisition and surface defects recognition based on panoramic image of small-section hydraulic tunnel

Small-section hydraulic tunnels are characterized by small spaces and various section forms, under complex environments, which makes it difficult to carry out an inspection by the mobile acquisition equipment. To resolve these problems, an arbitrarily adjustable camera module deployment method and the corresponding automatic image acquisition equipment with multi-area array cameras are proposed and developed. Such method enables the acquisition of full-length surface images of the hydraulic tunnels with different cross-section forms and diameters by a one-way travel, and the overlap rate and accuracy of the acquired image sets meet the requirements of three-dimensional reconstruction and panoramic image generation. In addition, to improve the speed and accuracy of traditional algorithms for tunnel surface defects detection, this paper proposes an improved YOLOv5s-DECA model. The algorithm introduces DenseNet to optimize the backbone feature extraction network and incorporates an efficient channel attention ECA module to make a better extraction of features of defects. The experimental results show that mAP, and F1-Score of YOLOv5-DECA are 73.4% and 74.6%, respectively, which are better than the common model in terms of accuracy and robustness. The proposed YOLOv5-DECA has great detection performance for targets with variable shapes and can solve the problem of classification imbalance in surface defects. Then, by combining YOLOv5-DECA with the direction search algorithm, a “point-ring-section” method is established to allow rapid identification of common surface defects by detecting them layer by layer with the bottom image of the stitched panorama as the seed. The presented method in this paper effectively solves the problem that a single image fails to show the overall distribution of the defects and their accurate positioning in a whole large tunnel section and the effective features of defects in an excessively large panoramic image size are difficult to be captured by the neural network. Field applications demonstrated that the presented method is adequate for high-precision and intelligent surface defect detection and positioning for different small-section hydraulic tunnels such as circular, arch-wall, and box-shaped hydraulic tunnels.

Solar dryer with double pass flat plate solar collector and carbon nanodots-coated absorber surface.

The present study details the development of a solar dryer with double pass flat plate solar collector having carbon nanodots (CNDs) coated absorber surface. Among the various concentrations of CNDs (0.1, 0.2, 0.5, 1, and 2%), the 0.5% CNDs coated absorber surface recorded the highest absorptance and lowest reflectance with higher spectral selectivity of 0.933. Hence, the 0.5% coating was selected as the optimum concentration and coated over the absorber surface of flat plate solar air collector. SEM image of black painted surface is clear and smooth and CNDs coated absorber surface is having dispersed particle with rough surface. FTIR absorption peak values revealed that the presence of black paint and CNDs in the 0.5% CNDs coated aluminium sheet. The efficiency of collector coated with 0.5% CNDs was calculated at various air flow rate of 0.008, 0.016, 0.018, and 0.021kg/s and results revealed that the rise in air flow rate from 0.008 to 0.021kg/s increased the efficiency from 39.22 to 82.99%. The solar dryer connected with the developed collector was tested for the performance and the drying studies revealed that shrimp and false white sardine required 10h and 11h drying time during the experimental studies.

Combined dual-channel fluorescence depth sensing of indocyanine green and protoporphyrin IX kinetics in subcutaneous murine tumors.

Fluorescence sensing within tissue is an effective tool for tissue characterization; however, the modality and geometry of the image acquisition can alter the observed signal. We introduce a novel optical fiber-based system capable of measuring two fluorescent contrast agents through 2cm of tissue with simple passive electronic switching between the excitation light, simultaneously acquiring fluorescence and excitation data. The goal was to quantify indocyanine green (ICG) and protoporphyrin IX (PpIX) within tissue, and the sampling method was compared with wide-field surface imaging to contrast the value of deep sensing versus surface imaging. This was achieved by choosing filters for specific wavelengths that were mutually exclusive between ICG and PpIX and coupling these filters to two separate detectors, which allows for direct swapping of the excitation and emission channels by switching the on-time of each excitation laser between 780- and 633-nm wavelengths. This system was compared with two non-contact surface imaging systems for both ICG and PpIX, which revealed that the fluorescence depth sensing system was superior in its ability to resolve kinetics differences in deeper tissues that would normally be dominated by strong signals from skin and other surface tissues. Specifically, the system was tested using pancreatic adenocarcinoma tumors injected into murine models, which were imaged at several time points throughout tumor growth to its diameter. This demonstrated the system's capability to track longitudinal changes in ICG and PpIX kinetics that result from tumor growth and development, with larger tumors showing sluggish uptake and clearance of ICG, which was not observable with surface imaging. Similarly, PpIX was quantified, which showed slower kinetics over different time points, and was further compared with the wide-filed imager. These results were further validated through depth measurements in tissue phantoms and model-based interpretation. This fluorescence depth sensing system can be used to sample the interior blood flow characteristics by ICG sensing of tissue as deep as 20mm into the tissue with sensitivity to kinetics that are superior to surface imaging and may be combined with other imaging modalities such as ultrasound to provide guided deep fluorescence measurements.

Peculiarities of formation and processing of surface interferograms according to Twyman-Green scheme in non-destructive testing

The goal of the work is to ensure the maximum accuracy of reconstruction of relief and de-formation displacement fields of structural element surface by the interferometric method. The authors successfully use novel algorithms for the processing of surface interferograms as well as the interferometers that follow the Twyman-Green scheme in order to implement such opti-cal testing of the surface. The maximum precision of obtained experimental data was achieved by the use of structural additions to the optical circuit and the selection of optical elements and their settings during adjustment of the interferometer, as well as new algorithms for the selec-tion and registration of a series of interferograms, speckle interferograms and speckle surface images. The authors proposed structural additions to the interferometer scheme used in the testing of both smooth and rough surfaces, appropriate methods for calculating the parameters of optical elements and their adjustments. Examples of obtained interferograms of the surface of metals under the static and fatigue cyclic loading, speckle interferograms of the surface of metals and polymer composites under the static and dynamic loading are given. The results of processing and interpreting these interferograms confirm that the Twyman-Green interfero-meter is an effective instrument for solving the problems of fracture mechanics and deformable solids. In particular, an assessment was made of: roughness and waviness of the surface of metals; sizes of the plastic zone around the stress concentrator; deformation displacement fields of metals and composites, as well as the detection of hidden defects in them.

Studying the growth and morphology of metal microstructures in sodium metal batteries with ionic liquid electrolytes by operando 23Na NMR spectroscopy

Sodium metal batteries are a potentially promising energy storage solution due to the abundance and low cost of sodium and high energy density of metal anodes. However, the growth of microstructural metal during cycling remains a crucial safety and performance issue to be addressed. Herein we investigate this process in sodium metal symmetric cells using operando 23Na nuclear magnetic resonance spectroscopy. We analyse the NMR signal arising from the microstructural sodium metal to obtain quantitative and qualitative information on its growth and morphology, observing significant differences in the evolution of the microstructures for different electrolytes, including ionic liquids with different sodium salt concentrations, water as an additive, and comparing with a more traditional organic electrolyte. The results correlate well with the previously reported cycling performance of these electrolytes, as well as ex situ SEM images of the electrode surface after cycling.

Comparative assessment of breast volume using a smartphone device versus MRI.

Assessment of breast volume has a relevance for aesthetic surgery and for the prevention and prediction of breast diseases. This study investigated breast volume measurements using a three-dimensional (3D) body surface scanner integrated in a smartphone device in comparison with magnetic resonance imaging (MRI) scans. Breast volume was assessed for 22 women who underwent routine MRI imaging. 3D surface images were acquired using a smartphone's digital texture camera (iPhone 11 Pro Max, Apple, California, USA, 2019). Breast volumes were manually outlined and calculated by two independent investigators using a 3D software tool (Meshmixer 3.5, Autodesk, Inc., 2018). Volume assessments from MRI images were performed by a radiologist using Syngo.via (Siemens Healthcare, Erlangen, Germany, VB50). The agreement between both methods and the inter-observer agreement was calculated with the concordance correlation coefficients and analysed with Bland-Altman plots. The mean breast volume as determined by MRI volumetry was 771.0ml on the left side and 763.9ml on the right side. Utilizing the 3D body surface volume assessment method, the mean breast volume was measured as 660.3ml (observer A) and 616.8ml (observer B) on the left side, and 701.9ml (observer A) and 638.6ml (observer B) on the right side. Although a high correlation was observed, differences in volume measurements appeared more pronounced in cases of larger breast volume. Smartphone-based 3D assessment of breast volume sufficiently agreed with MRI-based breast volume. This new technique could be used for cosmetic breast assessments in a surgical context and possibly in breast cancer risk studies assessing breast volume as outcome parameters.

Relationship between high-level color features and temperature mapping of magnesium alloy surface images based on the K-nearest neighbor algorithm

Numerous deformation and processing application scenarios of magnesium alloy sheets require high precision and efficiency in temperature detection. Traditional contact temperature measurement and infrared thermal imaging temperature measurement methods suffer from low accuracy and susceptibility to environmental influences. To this end, a real-time sensing method for determining the average surface temperature of magnesium alloys using visible light images is proposed in this paper. Firstly, real-time surface images of 3 mm thick magnesium alloy sheets were captured as the temperature decreased from 450 °C to 300 °C under varying light intensities. Then, calculate the high-level color features based on the grayscale frequency of the Red Green Blue color space of the image, and select the high-level color features with a strong correlation with temperature by Fisher criterion. Finally, a mapping relationship between the high-level color features of magnesium alloy images and temperature was established using the K-nearest neighbor algorithm. The results show that images in RAW format have a wider grayscale range compared to JPG, which is helpful in determining the mapping relationship between image color features and temperature. After extracting and selecting the high-level color features that have a strong correlation with temperature, the dimensions of input features are reduced. Using the K-nearest neighbor algorithm can fit the complex nonlinear relationship between high-level color features and temperature more accurately.

Evaluation of Electrical Properties and Uniformity of Single Wall Carbon Nanotube Dip-Coated Conductive Fabrics Using Convolutional Neural Network-Based Image Analysis

This study proposes a convolutional neural network (CNN)-based image analysis method to evaluate the electrical properties and uniformity of conductive fabrics treated with single-walled carbon nanotube (SWCNT) dip-coating. The conductive fabric was produced by dip-coating cotton-blended spandex with SWCNT, and the surface images were scanned and preprocessed to obtain image data, while resistance measurements were conducted to obtain labels and build the dataset. SEM analysis revealed that as the number of dip-coating cycles increased, particle density and path formation improved. The CNN model learned the relationship between surface images and resistance values, achieving a high predictive performance, with an R-squared (R²) value of 0.9422. The model demonstrated prediction accuracies of 99.1792% for the coefficient of variation (CV) of uniformly coated fabrics and 96.8877% for non-uniformly coated fabrics. Additionally, p-value analysis of all fabric samples yielded a result of 0.96044, indicating no statistically significant difference between the predicted and actual values. The proposed CNN-based model can accurately evaluate the electrical uniformity of conductive fabrics, showing potential for contributing to quality control and process optimization in production.

A quantitative, image-based analysis of the spreading performance of PolyArylEtherKetone polymer powders during Powder Bed Fusion Additive Manufacture

Powder spreadability in the powder bed fusion process is normally visually assessed by the machine operator through repeat trials at room temperature or elevated temperatures. Some studies used powder rheology results as an indicator of the powder spreadability. This study presents a novel method of image-based analysis for the assessment of polymer spreading quality for powder bed fusion and demonstrates that the subjective assessment of the machine operator can be replaced by quantitative and measurable data. Twenty-four developmental grade PolyArylEtherKetone powders were tested. Images of the powder bed surface were analysed in MATLAB and the relationships with the powder rheology established through statistical analysis. The two methods for calculating surface deviations from variations in the greyscale images showed to be sensitive to the recoater travel and presented a strong correlation with the Normalised Aeration Sensitivity (NAS), a powder rheology parameter identified in a previous study as the most significant parameter able to categorise powder flow and spreadability based on a yes/no response.

Managing Residual Heat Effects in Femtosecond Laser Material Processing by Pulse-on-Demand Operation

Femtosecond laser processing combines highly accurate structuring with low residual heating of materials, low thermal damage, and nonlinear absorption processes, making it suitable for the machining of transparent brittle materials. However, with high average powers and laser pulse repetition rates, residual heating becomes relevant. Here, we present a study of the femtosecond laser pulse-on-demand operation regime, combined with regular scanners, aiming to improve throughput and quality of processing regardless of the scanner’s capabilities. We developed two methods to define the needed pulse-on-demand trigger sequences that compensate for the initial accelerating scanner movements. The effects of pulse-on-demand operation were studied in detail using direct process monitoring with a fast thermal camera and indirect process monitoring with optical and topographical surface imaging of final structures, both showing clear advantages of pulse-on-demand operation in precision, thermal effects, and structure shape control. The ability to compensate for irregular scanner movement is the basis for simplified, cheaper, and faster femtosecond laser processing of brittle and heat-susceptible materials.

Multimodal sensor-to-machined surface image diffusion for defect detection in industrial processes

Generative models, particularly diffusion-based approaches, have gained significant attention recently due to their ability to create realistic outputs. Despite their potential, the application of these models in manufacturing remains largely unexplored. This work presents a framework that addresses this gap by generating machined surface images guided by multiple sensor inputs in manufacturing. The proposed model integrates information from multiple sensors with varying sampling rates using multimodal embedding and employs a latent diffusion model to translate the fused sensor embedding into an image embedding, which is then converted into a machined surface image. The effectiveness of the framework is validated using real-world time-series data, including force, torque, acceleration, and sound, collected from various industrial processes, such as a carbon-fiber-reinforced plastic drilling process. The results demonstrate the model’s ability to predict defects from the generated machined surface images. The proposed approach can potentially revolutionize prognostics and health management (PHM) in smart manufacturing by enabling sensor-guided visual inspection, defect detection, process monitoring, and predictive maintenance.

On the Implications of Ground‐Based High‐Definition Imaging of Io's Surface

AbstractThe capability to observe temporal changes on Io's surface at optical wavelengths has recently been demonstrated by Conrad et al. (2024, https://doi.org/10.1029/2024GL108609) using new instrumentation at the Large Binocular Telescope. Monitoring of Io's surface morphology would be impactful since preexisting metrics of the degree, location and composition of Io's plume activities are severely limited. Relationships with external data sets appear especially fruitful. Comparisons with spatially resolved data on gas distributions and thermal hots spots would better characterize composition and chemistry in Io's volcanic plumes. Comparisons with measurements remote from Io are posed to advance our understanding of atmospheric escape and how eruptions connect to transient enhancements at disparate size and time scales in the extended neutral clouds and plasma torus.