Object Surface Research Articles

Neural skeleton: Implicit neural representation away from the surface

Implicit Neural Representations are powerful tools for representing 3D shapes. They encode an implicit field in the parameters of a Neural Network, leveraging the power of auto-differentiation for optimizing the implicit function and avoiding the need for a manually crafted function. So far, Implicit Neural Representations have been mainly designed to extract or render object surfaces and methods primarily focus on improving the implicit function near the surface. In this paper we argue that implicit fields are useful for other shape analysis tasks, in particular skeleton (medial axis) extraction. Indeed, a medial axis is defined through distances to the surface, which can be provided by an implicit neural representation, making it robust to noise and missing data. However this requires the implicit field to be reliable away from the surface, something most representations are not optimized for. To achieve this, inspired by variational image denoising techniques, we propose to add a Total Variation term, to regularize the implicit field. We further design a skeleton sampling method working directly on the GPU, and link the extracted points using a coverage formulation. We show that our resulting neural skeleton is more robust to sample defects such as noise or missing data compared to other medial axis extraction methods.

Relationship between cooling and optical properties of polyester fabric treated with TiO2 nanopigments

The absorption of solar radiations (especially near infrared region) is the reason for solar heating the surface of object and increasing its temperature. To avoid this undesirable heating, it is possible to use some special nanopigments that can reflect the solar radiation in the near-infrared spectrum. Consequently, the thermal regulation and cooling property of fabric as a result of the surface coating is really important. This work studies the possibility of using Visible, NIR and SWIR reflective coatings on polyester fabric to reduce surface temperature when irradiated with solar radiations. Polyester fabrics were dyed with disperse dye and coated with TiO2 nanopigment with different diameters and concentrations. Field Emission Scanning Electron Microscopy (FESEM), Energy-dispersive X-ray spectroscopy (EDS), and heat box were used to characterize the coating process. Obtained results indicate that, the lower surface temperature was recorded by applying the TiO2 nanopigment on polyester fabric and selected materials can considerably increase the thermal regulation and cooling property of the polyester fabric. The pristine (white) and colored polyester fabric coated with TiO2 (500 nm diameter, 3 gr/L) has the highest reflectance throughout the NIR and SWIR regions of solar radiation. The treated polyester fabric respect to pristine polyester fabric exhibits reasonable cooling power and about 4 °C thermal regulation and cooling. Based on the obtained results by heat box system, the thermal regulation and cooling property as the temperature difference between pristine and coated fabric were increased with increasing the diameter and concentration of TiO2 nanopigments. The statistical analysis of variance (ANOVA) was used to study the effect of physical characteristics of TiO2 nanopigments (size and concentration) on the optical parameters as color difference (ΔE), reflectance factor (R) and reflectance function (K/S) and thermal regulation and cooling property as the temperature difference (ΔT). The obtained results indicate that there are good relationship and correlation between the NIR and SWIR reflectance and cooling properties.

A comparison of learning-based approaches for the corrosion detection on barrels in industrial applications

Abstract Machine-learning-based (ML) segmentation in the image domain can be utilized for the detection of corrosion on the surface of industrial objects. This research provides a comparison of techniques using convolutional neural networks (CNNs) on the one hand, and random forest (RF) classifiers within RGB and HSV feature spaces on the other hand. CNN-based approaches usually need a large amount of data for training in order for the network to converge and generalize well on new data. Due to the low amount of data provided, we apply a set of methods to increase the generalization ability of the model. These methods can be categorized into data augmentation, selection of larger and smaller models and pretraining strategies like self supervised learning (SSL). The RF classifiers on the other hand are trained per pixel, so that the amount of data is determined by the image size. The object to be tested is a barrel made of metal, from which the image of the coat is used as the training data, and the image of the bottom as test data. We found that a RF classifier in the RGB feature space outperforms the CNNs by seven percentage points regarding the f 1-score of the corrosion class.

Development of an Optical System with an Orientation Module to Detect Surface Damage to Potato Tubers

This method is a variant of non-destructive multiparametric surface analysis and includes the implementation of hyperspectral and RGB image processing approaches from different angles. This work is based on a fundamental hyperspectral survey system for obtaining data on scanned biological objects in many spectral ranges and with several possible variants of assembling a system with different types of surface illumination with point light and diffuse illumination. The implementation of the technology through the use of a diffused light source provides the diffuse illumination of a biological object with pronounced symptoms of rarefaction on the surface of a biological object—in this case, potato tubers, due to the presence of signs of disease on the potato peel, as well as their deformation. Using broadband lamps, a short-pass filter is located between the source and the object (λ ≤ 400 nm, λ may vary depending on the excitation length), and a long-pass filter (λ ≥ 400 nm) between the root or onion and the chamber. The use of a vision system with a created database containing models of real defects in potato tubers showed a high sorting efficiency, providing an accuracy of sorting by size of 95.4%, and an accuracy by the presence of defects of 93.1%.

Preparation and Characterization of Flexible Phase Change Material for Compact Electrical Applications

The development of advances in digital technologies, the compact electronic devices plays an important role but the space constraints and irregular surfaces in the device makes it hard to dissipate heat with the help of external medium. They overheat in most of the cases and become a common cause for failure of the device. Phase Change Materials (PCM) release or absorb heat at its melting temperature to its better thermal management. But the PCM have limitations as its brittleness could not occupy the irregular surfaces of compact electronic devices and undergo deformations during operation, a new class of material Flexible PCM (FPCM) developed to endure a certain amount of deformation and make compact contact with irregular surfaces of objects, making them ideal for many smart applications. In this work, the newer thin film FPCM are prepared using Olefin Block Copolymer and paraffin wax at different combinations on mass basis and field emission SEM and XRD characterization of samples are studied and reported. This paper presents selection of optimal PCM and copolymer, preparation of FPCM and detailed analysis of the sample report and electrical device applications. This report provides a basis for implementing and optimizing phase change thermal management techniques in electrical devices. The experimental results shows PCM 45 and 55 wt% co-polymer combination of FPCM having better thermal management in Electrical Applications. Thermal analysis will be studied in future. HIGHLIGHTS Flexible Phase Change Materials (FPCM) prepared and properties are studied Olefin Block Copolymer and paraffin wax at different combinations on mass basis developed and studied FPCM used for Compact Electronic Applications considered as LED Lamb GRAPHICAL ABSTRACT

Writing on the world beyond the page: medieval inscriptions as facta and ficta

From the twelfth to the fifteenth centuries texts proliferated upon the surfaces of objects, monuments and architecture, ensuring that medieval people lived with texts, even if they never turned a page. Examining a series of inscribed objects, monuments, and buildings, this article focuses upon three constitutive features of this large-scale dynamic: epigraphic prosopopoeia, the use and effects of deixis in medieval inscriptions, and the device of the banderole. It concludes by examining the twelfth-century Bridekirk baptismal font: an epigraphic monument in which all three devices are skillfully deployed, to remarkably self-reflexive ends. The article aims to show how the design of epigraphic texts added not only new textual-material facts to the world, but also textual-visual ficta or fictions.

Оценка наночастиц как перспективных альтернативных антимикробных препаратов

The search for new substances capable of inhibiting the growth of microorganisms is relevant. Therefore, an assessment was made of the effect of carbon nanoparticles on the growth and biofilm formation of microbial isolates from the surfaces of anthropogenic environmental objects. It has been established that carbon nanoparticles on the C60 + PVP + AC960 diamond platform are able to suppress the growth, reproduction and ability to biofilm formation of gram-positive and gram-negative prokaryotes, as well as fungi of the genus Candida and Aspergillus. Under the influence of C60+AC960 fullerenes, the biofilm-generation ability of Staphylococcus epidermidis, Bacillus cereus, Escherichia coli, and Candida albicans was significantly inhibited. These data provide a basis for further study of these compounds as a promising antimicrobial agent. Keywords: Antimicrobial action, carbon nanoparticles, biofilm formation, fullerenes, nanodiamonds.

Modified prism methods for measuring the refractive index of solid and liquid substances

Methods for measuring the refractive index of optically transparent dielectric materials are considered. Modified methods based on the methods of minimum deviation and constant deviation are proposed and allow determining the refractive index of triangular prisms with unknown apex angles. In the proposed methods, the angles of light deviation on three faces of the prism are measured, and the refractive index of the material and the prism angles are determined from the solution of a system of equations. To implement the proposed methods, a goniometric system is used. That system was designed to measure angles between the flat surfaces of objects in manual and automated modes. Reference prism samples made of N-SF 1 optical glass, and a hollow prism filled with distilled water are studied. The proposed methods are compared and the measurement error is estimated. It is shown that the modified methods can be used for high-precision measurements of the refractive index in cases where the angles of the prism are unknown, or their measurement is associated with technical difficulties.

Fault detection on the 3-D printed objective surface by using the SVM algorithm

The concept of 3D-printed items has gained traction in the age of Industry 4.0, proving helpful in terms of money and time spent. Assemble the products like this are physically in stages according to computerised CAD data. However, faults still exist in 3D printed objects owing to the diversity in characteristics and structure, which degrades printed quality. Must uncover these mistakes at each development stage of the product. This study Using an ensemble of machine learning algorithms and pre-trained models, details a procedure for detecting anomalies at each layer. For defect detection, the recommended combination is learned in advance offline and then applied in real-time online. This research compares several pre-trained models using SVM, machine learning methods for monitoring and defect detection in Fused Deposition Modeling in an experimental setting (FDM). According to the findings, the best accuracy was achieved by combining Alexnet with the SVM algorithm. As a result of its minimal experimental and computational costs, apply the suggested fault detection technique for real-time defect detection.

Reflection transformation calibration for mirror-assisted multi-view digital image correlation system using fluorescent speckle patterns

Mirror-assisted multi-view digital image correlation (MV-DIC) is a simple-to-implement DIC technique for panoramic kinematic field measurements of regular-sized specimens. In the practical implementation of mirror-assisted MV-DIC, accurate reflection transformation calibration of the planar mirrors is considered as a pivotal step. In this work, we propose to use fluorescent speckle patterns fabricated on the mirrors to calibrate their reflection transformation matrices, which can fully utilize the camera’s spatial resolution and simplify the calibration processes. The fluorescent speckle patterns on the mirrors do not interfere with the virtual object surfaces using a regular blue-light stereo-DIC system. However, under the illumination of ultraviolet light, the fluorescent speckle patterns can be captured and used to calibrate the position and orientation of the planar mirrors. The practicality and efficacy of the fluorescent speckle pattern-based calibration method are validated by profile measurement and translation tests, and an application in a four-point bending experiment is also demonstrated.

38‐2: Ubiquitous Display with High Transparency Phosphor Dot Projection Film

A see‐through projection system with phosphor dot film has been proposed and demonstrated to provide clear and sharp image on any kind of object surface. The attached projection film will not disturb the original feature of the surface and in turn make a display with no clearly defined display frame.

Assembly task execution using visual 3D surface reconstruction: An integrated approach to parts mating

This paper is focused on assembly tasks executed by an industrial robotic manipulator in the presence of uncertainties. The goal is to achieve higher levels of autonomy and flexibility of robotic systems in the execution of such tasks. In particular, as a well-established paradigm of assembly tasks, a Peg-in-Hole task has been considered, where the pose of the target object with respect to the robot is known with uncertainties far larger than the task tolerance, e.g., due to manual positioning of the object in the workcell. The proposed approach is based on the reconstruction of the object surface by means of a number of point clouds provided by a depth sensor. The reconstruction is then compared with a known CAD model of the surface, in order to localize the position and tilt of the holes. Finally, the peg insertion is performed in two steps: a search phase, in which the peg tip gently slides on the surface following a trajectory described by Lissajous functions, and a mechanical coupling phase, in which a compliant behavior is imposed to the peg. Experiments on a collaborative manipulator confirm that the proposed approach allows to achieve a better degree of autonomy and flexibility for a class of robotic tasks in partially structured environments.

Numerical investigation of the influence of surface wettability on water entry of spheres

The water-entry problem is a complex multiphase hydrodynamic problem that is directly related to many engineering applications and natural phenomena, such as torpedo airdrops, seaplane landings, and ship slamming. Therefore, studying the influence of the microscopic properties of the object surface on the macroscopic phenomenon during water entry is necessary. In this study, the volume of fluid model and continuum surface force models are coupled to establish a multiphase flow numerical method for the water entry of objects considering surface wettability. The effect of surface wettability on the evolution of the cavity, multiphase flow-field structure, and hydrodynamic force characteristics are analyzed in detail. The results show that the movement of liquid film formed on the surface of the sphere at the early stage is the key to the formation of the cavity. For hydrophobic spheres, the liquid film separates near the equator of the sphere, and air enters it to form a cavity. At the moment of pinch-off, the pressure in the lower cavity increases, which generates a force that pushes the sphere to accelerate the fall, and this force is higher for spheres with a smaller density ratio. The flow-field structure shows that both rotational and shear effects play a dominant role in the evolution of the flow field in the cavity. For hydrophilic spheres, the liquid film follows the contact line along the surface of the sphere and converges at the top to form an upward jet.

Analysis of Polarization Detector Performance Parameters on Polarization 3D Imaging Accuracy.

Three-dimensional (3D) reconstruction of objects using the polarization properties of diffuse light on the object surface has become a crucial technique. Due to the unique mapping relation between the degree of polarization of diffuse light and the zenith angle of the surface normal vector, polarization 3D reconstruction based on diffuse reflection theoretically has high accuracy. However, in practice, the accuracy of polarization 3D reconstruction is limited by the performance parameters of the polarization detector. Improper selection of performance parameters can result in large errors in the normal vector. In this paper, the mathematical models that relate the polarization 3D reconstruction errors to the detector performance parameters including polarizer extinction ratio, polarizer installation error, full well capacity and analog-to-digital (A2D) bit depth are established. At the same time, polarization detector parameters suitable for polarization 3D reconstruction are provided by the simulation. The performance parameters we recommend include an extinction ratio ≥ 200, an installation error ∈ [-1°, 1°], a full-well capacity ≥ 100 Ke-, and an A2D bit depth ≥ 12 bits. The models provided in this paper are of great significance for improving the accuracy of polarization 3D reconstruction.

Self-Potential as a Tool to Monitor Redox Reactions at an Ore Body: A Sandbox Experiment

Ore bodies generate natural electrical fields that are measurable at the ground surface. The ground surface signature of this electrical field is called a self-potential anomaly. We developed a sandbox experiment to monitor the evolution of a self-potential anomaly associated with redox processes mediated by bacterial activity at the surface of a buried metallic object crossing the water table. A Bio-Electrochemical Cell (BEC) is formed by a metal bar connecting the upper, oxygen-rich, part of the tank and an aquifer containing an electron donor in the form of acetate. The self-potential response was observed during a period of 327 days. The tomography of the self-potential signature confirms that self-potential tomography is able to locate the metallic target acting as a BEC. In addition, we performed redox potential, pH, and electrical potential measurements over a vertical cross-section of the tank at several time steps to obtain an idea of where the redox front is located. The distributions of the redox potential and pH further demonstrated the development of the oxidation-reduction chemical processes facilitated by the BEC as bacterial communities developed around the metallic bar. The electrical potential anomaly shows that the bacterial communities followed a short period of exponential growth, then a longer period of a sustained population. These results demonstrate the usefulness of the self-potential method in monitoring redox processes at the surface of a buried ore body. Further works will need to combine such self-potential anomalies with induced polarization anomalies through joint inversion.

Spectral reflectance estimation from non‐raw color images with nonlinearity correction

AbstractThe spectral reflectance is recognized as the fingerprint of an object surface and has been used to achieve accurate color measurement in textile and other fields. Spectral reflectance can be recovered from color images to preserve high spectral and spatial resolutions simultaneously. However, a color camera commonly supplies a non‐raw color image, which is non‐linear with respect to the scene radiance, and is inappropriate for quantitative analysis. In this study, for non‐raw color images, different nonlinearity correction models are designed and evaluated with respect to different spectral estimation algorithms. The colorimetric and spectral accuracy of spectral estimation after the nonlinearity correction is assessed through both simulation and practical experiments. In the simulation, a large number of spectral images from several datasets are employed to directly verify the effectiveness of the nonlinearity correction. In the practical experiments, the spectral estimation accuracy following the nonlinearity correction is verified directly and indirectly based on actual color images. The resulting linear color image data after the nonlinearity correction can provide better spectral estimation accuracy especially for the PI algorithm with one power‐function based model. Besides, the combination of the simple PI algorithm with the power‐function based model can exceed other combinations comprising complex algorithms and models in both accuracy and efficiency. For the linear color image data, the PI algorithm even surpasses the deep learning‐based methods in certain metric, thus indicating a shallow relationship exists between the linear color image data and the spectral reflectance.

Minimally Invasive Liquid Microjunction Sampling to Identify Dye on a 17th Century Spanish Illuminated Manuscript.

Common sampling methods for mass spectrometry such as sectioning are undesirably damaging to cultural heritage objects. A liquid microjunction sampling technique is developed that uses minimal solvent volume for analysis. Painted illustrations on a 17th century parchment manuscript from Spain were analyzed to identify the organic red pigment throughout its pages. By extracting with 0.1 μL solvent, the pigment was supplied for direct infusion electrospray MS, and the resulting disruption to the object surface was practically invisible to the naked eye.

Design of magnetostrictive tactile sensor for depth detection

PurposeThis paper aims to design a magnetostrictive tactile sensor for surface depth detection. Unlike the human finger, although most tactile sensors have high sensitivity to pressure, they cannot detect millimeter-level depth information on the surface of objects precisely. To enhance the ability to detect surface depth information, a piezomagnetic sensor combining inverse magnetostrictive effect and bionic structure is developed in this paper.Design/methodology/approachA magnetostrictive tactile sensor based on Galfenol [(Fe83Ga17)99.4B0.6] is designed and studied for surface depth measurement. The optimal structure of the sensor is determined by experiment and theory. The test platforms for static and dynamic characteristics are set up. The static and the dynamic sensing performance of the sensor are studied experimentally.FindingsThe sensor can detect 0–2 mm depth change with a sensitivity of 91.5 mV/mm. A resolution of 50 µm can be achieved in the depth direction. In 50 cycles of loading and unloading tests, the maximum error of the sensor output voltage amplitude is only 2.23%.Originality/valueThe sensor can measure the depth information of object surface precisely with good repeatability through sliding motion and provide reference for object surface topography detection.

Robust near-infrared-based plastic classification with relative spectral similarity pattern

Sensor-based material flow characterization techniques, particularly hyperspectral imaging in the near-infrared (NIR) range, can recognize materials quickly, accurately, and economically. When identifying materials using NIR hyperspectral imaging, extracting influential features from high-dimensional wavelength information is essential for effective recognition. However, spectral noise from the rough and contaminated surfaces of objects (especially un-shredded waste) degrades the feature-extraction performance, which in turn deteriorates the material classification performance. In this study, we propose a real-time feature-extraction method, named relative spectral similarity pattern color mapping (RSSPCM), to robustly classify materials in noisy environments, such as plastic waste sorting facilities. RSSPCM compares relative intra- and inter-class spectral similarity patterns, instead of individual similarity, to class-representative spectra alone. Recognition targets have similar chemical makeups that are applied to feature extraction as an intra-class similarity ratio. The proposed model is robust owing to the remaining relative similarity trends found in a contaminated spectrum. We evaluated the effectiveness of the proposed method using noisy samples obtained from a waste-management facility. The results were compared with two spectral groups obtained at different noise levels. Both results showed high accuracy as there was an increased number of true positives for low-reflectance regions. The average F1-score values were 0.99 and 0.96 for low- and high-noise sets, respectively. Furthermore, the proposed method showed minimal F1-score variations between classes (standard deviation of 0.026 for the high-noise set).

Synthetic aperture imaging using multi-view super-resolution

The occlusion problem is a major challenge in the field of computer vision. Synthetic aperture imaging (SAI) is often used for surface reconstruction of occluded objects. However, SAI usually relies on high-speed information transmission devices. In addition, a large amount of information in the scene is lost, when handling low-resolution input images. This limitation results in unclear reconstructed regions in the synthetic aperture image and thus hinders the application of SAI in downstream tasks. We propose a multi-view super-resolution SAI method. It aims to generate high-resolution synthetic aperture images using images acquired by few of low-resolution acquisition devices. The main contributions of this paper are: (1) a multi-view super-resolution algorithm is proposed. It can generate clear synthetic aperture images in an array with a limited number of cameras. (2) By exploiting the correlation between views, the proposed algorithm can generate super-resolution synthetic aperture images with more accurate image structure and sharper image edges. (3) A feature extraction module is proposed. It can effectively extract the complementary relationship between pictures from different perspectives. The experimental results show that the proposed method can generate a reconstructed image of the occluded object surface with clear edges and accurate structure. Compared to conventional SAI, our method improves 5.7 % / 21.1 % on peak signal-to-noise ratio (PSNR)/structure similarity index measure (SSIM) and 4.4 % / 9.2 % on PSNR/SSIM respectively on two datasets compared to other state-of-the-art super-resolution methods.