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The incidence of "acid attacks" (vitreolage) is a global concern, with those affected often receiving lifelong medical care due to physical and psychological damage. The purpose of this study was to evaluate the effectiveness of several emergency skin decontamination approaches against concentrated (>99 %) sulphuric acid and to identify the effective window of opportunity for decontamination. The effects of four decontamination methods (dry, wet, combined dry & wet and cotton cloth) were assessed using an in vitro diffusion cell system containing dermatomed porcine skin. Sulphuric acid (H 2 SO 4) was applied to the skin with decontamination protocols performed at 10 s, 30 s, 8 min, and 30 min post exposure. Skin damage was quantified by tritiated water (3H 2 O) penetration, receptor fluid pH and photometric stereo imaging (PSI), with quantification of residual sulphur (by SEM-EDS) to determine overall decontamination efficiency. Skin translucency (quantified by PSI) demonstrated a time-dependent loss of dermal tissue integrity from 10 s. Quantification of dermal sulphur content confirmed the rapid (exponential) decrease in decontamination efficiency with time. The pH of the water effluent indicated complete neutralisation of acid from the skin surface after 90 s of irrigation. Wet decontamination (either alone or immediately following dry decontamination) was the most effective intervention evaluated, although no decontamination technique was statistically effective after 30 s exposure to the acid. These data demonstrate the time-critical consequences of dermal exposure to concentrated sulphuric acid: we find no practical window of opportunity for acid decontamination, as physical damage is virtually instantaneous. • Response to an acid exposure requires rapid intervention. • Immediate flushing with water reduces skin damage, other methods exacerbate damage. • Effectiveness of washing deteriorates as soon as 30 s following exposure to acid. [ABSTRACT FROM AUTHOR]

Artificial skin has shown great potential in robot perception and human healthcare. It provides multifunctional tactile sensing, including 3D shape reconstruction, contact feedback, and temperature perception, where the 3D reconstruction function is indispensable for dexterous hands in tactile cognition and interaction. Vision‐based tactile sensor (VTS) is an innovative bionic tactile sensor and supports high‐resolution, high‐precision, and high‐density tactile reconstruction compared with electronic tactile sensors. Considering the unique contribution of visuo‐tactile sensing to artificial skin, this review focuses on the 3D reconstruction techniques of the VTS. 3D reconstruction methods are classified into five categories based on sensing modalities, hardware categories, and modeling approaches: 1) photometric stereo, 2) binocular depth calibration, 3) optical flow, 4) deep learning, and 5) ToF (time of flight). In addition, the association and difference of reconstruction methods are analyzed from the hardware perspective, and the development and technological details of 3D reconstruction are summarized. On this basis, the challenges and development direction are discussed. This review can be viewed as a technology guide to provide references for interested researchers. Furthermore, it is expected to promote the extensive application of the VTS in artificial skins. [ABSTRACT FROM AUTHOR]

This paper addresses image enhancement and 3D reconstruction techniques for dim scenes inside the vacuum chamber of a nuclear fusion reactor. First, an improved multi-scale Retinex low-light image enhancement algorithm with adaptive weights is designed. It can recover image detail information that is not visible in low-light environments, maintaining image clarity and contrast for easy observation. Second, according to the actual needs of target plate defect detection and 3D reconstruction inside the vacuum chamber, a defect reconstruction algorithm based on photometric stereo vision is proposed. To optimize the position of the light source, a light source illumination profile simulation system is designed in this paper to provide an optimized light array for crack detection inside vacuum chambers without the need for extensive experimental testing. Finally, a robotic platform mounted with a binocular stereo-vision camera is constructed and image enhancement and defect reconstruction experiments are performed separately. The results show that the above method can broaden the gray level of low-illumination images and improve the brightness value and contrast. The maximum depth error is less than 24.0% and the maximum width error is less than 15.3%, which achieves the goal of detecting and reconstructing the defects inside the vacuum chamber. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the process of scratch defect detection in textured materials, there are often problems of low efficiency in traditional manual detection, large errors in machine vision, and difficulty in distinguishing defective scratches from the background texture. In order to solve these problems, we developed an enhanced scratch defect detection system for textured materials based on optimized photometric stereo vision and FFT-Gabor filtering. We designed and optimized a novel hemispherical image acquisition device that allows for selective lighting angles. This device integrates images captured under multiple light sources to obtain richer surface gradient information for textured materials, overcoming issues caused by high reflections or dark shadows under a single light source angle. At the same time, for the textured material, scratches and a textured background are difficult to distinguish; therefore, we introduced a Gabor filter-based convolution kernel, leveraging the fast Fourier transform (FFT), to perform convolution operations and spatial domain phase subtraction. This process effectively enhances the defect information while suppressing the textured background. The effectiveness and superiority of the proposed method were validated through material applicability experiments and comparative method evaluations using a variety of textured material samples. The results demonstrated a stable scratch capture success rate of 100% and a recognition detection success rate of 98.43% ± 1.0%. [ABSTRACT FROM AUTHOR]

Line structured light (LSL) measurement systems can obtain high accuracy profiles, but the overall clarity relies greatly on the sampling interval of the scanning process. Photometric stereo (PS), on the other hand, is sensitive to tiny features but has poor geometrical accuracy. Cooperative measurement with these two methods is an effective way to ensure precision and clarity results. In this paper, an LSL-PS cooperative measurement system is brought out. The calibration methods used in the LSL and PS measurement system are given. Then, a data fusion algorithm with adaptive weights is proposed, where an error function that contains the 3D point cloud matching error and normal vector error is established. The weights, which are based on the angles of adjacent normal vectors, are also added to the error function. Afterward, the fusion results can be obtained by solving linear equations. From the experimental results, it can be seen that the proposed method has the advantages of both the LSL and PS methods. The 3D reconstruction results have the merits of high accuracy and high clarity. [ABSTRACT FROM AUTHOR]

The use of visual rationality in planning and designing indoor 3D spaces can effectively enhance the quality of environmental art design. For the rational planning and design of 3D spaces, it is essential to assign attribute values to the rational evaluation index of 3D space planning, determine the weight vector of rational evaluation factors for indoor 3D space planning, and accomplish the visual rationality planning and design of indoor 3D spaces. 3D reconstruction is one of the primary objectives and focal points of machine vision research. The principle is to extract 3D information from the 2D image captured by the machine’s sensors in order to reconstruct a 3D scene model. The traditional method constructs the evaluation index system for indoor 3D space planning to determine the weight of the evaluation index for alternative points. However, it overlooks the weight vector of the evaluation factors, leading to low accuracy in rational planning. This paper optimizes the effect of structured light reconstruction using photometric stereo vision. Firstly, the text delves into the impact of light sources on the 3D reconstruction effect. Subsequently, a fusion algorithm combining 3D and structured light reconstruction methods is introduced, leveraging artificial intelligence (AI) technology. Experimental results show that this algorithm has a superior 3D reconstruction effect. [ABSTRACT FROM AUTHOR]

Wear topography is a significant indicator of tribological behavior for the inspection of machine health conditions. An intelligent in-suit wear assessment method for random topography is here proposed. Three-dimension (3D) topography is employed to address the uncertainties in wear evaluation. Initially, 3D topography reconstruction from a worn surface is accomplished with photometric stereo vision (PSV). Then, the wear features are identified by a contrastive learning-based extraction network (WSFE-Net) including the relative and temporal prior knowledge of wear mechanisms. Furthermore, the typical wear degrees including mild, moderate, and severe are evaluated by a wear severity assessment network (WSA-Net) for the probability and its associated uncertainty based on subjective logic. By integrating the evidence information from 2D and 3D-damage surfaces with Dempster–Shafer (D–S) evidence, the uncertainty of severity assessment results is further reduced. The proposed model could constrain the uncertainty below 0.066 in the wear degree evaluation of a continuous wear experiment, which reflects the high credibility of the evaluation result. [ABSTRACT FROM AUTHOR]

Along with the improvement of quality requirements industrial production, surface inspection of workpiece has gradually become an indispensable and important process in the production of the workpiece. Aiming at the traditional methods in textured paper inspection, there are problems of low efficiency and large error based on machine vision, we propose a "photometric stereo vision + fast Fourier enhancement + feature fusion" composite structure inspection method. First, as the traditional CCD camera produces obvious noise and scratches, which are difficult to distinguish from the background texture area, we propose combining the photometric stereo vision measurement algorithm to get the surface gradient information of the textured paper to obtain more gradient texture information; and then realize the secondary enhancement of the image through Fourier transform in spatial and frequency domains. Second, as the textured paper scratches are difficult to detect, the features are difficult to extract, and the threshold boundary is difficult to define, we propose dynamic threshold segmentation through multi-feature fusion to realize the surface scratch detection work of textured paper. We designed experiments using more than 300 different textured papers; and the results show that the composite structure detection method proposed in this paper is feasible and has advantages. [ABSTRACT FROM AUTHOR]

Photometric stereo, which derives per-pixel surface normals from shading cues, faces challenges in capturing high-resolution (HR) images in linear response systems. We address the representation of HR surface normals from low-resolution (LR) photometric stereo images. To represent fine details of the surface normal in the HR domain, we propose a novel plug-in high-frequency representation module named the Complex Surface Structure (CSS) estimator. When combined with a conventional photometric stereo model, CSS is capable of representing intricate surface structures in 2D Fourier space. We show that photometric stereo super-resolution (SR) with our CSS estimator provides high-fidelity surface normal representations in higher resolution from the LR inputs. Experiments demonstrate that our results are quantitatively (MAE is less than half of the recent works) and qualitatively better than those of the existing deep learning-based SR work.

In the process of scratch defect detection in textured materials, there are often problems of low efficiency in traditional manual detection, large errors in machine vision, and difficulty in distinguishing defective scratches from the background texture. In order to solve these problems, we developed an enhanced scratch defect detection system for textured materials based on optimized photometric stereo vision and FFT-Gabor filtering. We designed and optimized a novel hemispherical image acquisition device that allows for selective lighting angles. This device integrates images captured under multiple light sources to obtain richer surface gradient information for textured materials, overcoming issues caused by high reflections or dark shadows under a single light source angle. At the same time, for the textured material, scratches and a textured background are difficult to distinguish; therefore, we introduced a Gabor filter-based convolution kernel, leveraging the fast Fourier transform (FFT), to perform convolution operations and spatial domain phase subtraction. This process effectively enhances the defect information while suppressing the textured background. The effectiveness and superiority of the proposed method were validated through material applicability experiments and comparative method evaluations using a variety of textured material samples. The results demonstrated a stable scratch capture success rate of 100% and a recognition detection success rate of 98.43% ± 1.0%.

Since fresh cherry tomatoes have a high likelihood of spoilage during post-harvest transport and storage, assessing the fruit grade based on its firmness or predicting its potential storage time becomes necessary. The study proposes a method for non-destructive fruit firmness detection using a photometric stereo vision-based tactile sensor. The tactile sensor can measure the three-dimensional deformation of cherry tomatoes by mapping between pixel color and surface normal using a gradient prediction network. The summation of the deformation heights measured by a tactile sensor pressing on the cherry tomatoes can accurately describe fruit firmness, which was also verified by a comparative experiment with the Texture Analyzer. To create a model of firmness variation over time, a group of tomatoes of the same variety and origin were measured for firmness at various storage dates following harvest in subsequent experiments. An experiment was conducted to measure and predict the variation in the firmness of cherry tomatoes using a tactile sensor. The results indicate that the firmness of cherry tomatoes decreases linearly over time under similar conditions. The relative prediction errors were found to be mostly within ± 15% and ± 25% during 1 and 2 weeks of storage, respectively. All findings indicate that the proposed method can effectively measure the firmness of cherry tomatoes and accurately predict their short-term variations. [ABSTRACT FROM AUTHOR]

This article explores the use of computer-vision technologies in the context of digitally editing and researching letters and literary papers by the British-American poet W. H. Auden. Two documents in the previously inaccessible 'Auden Musulin Papers' contain colourless indented typewriter impressions of poetry. These impressions result from the papers' original use as 'backing sheets', inserted into a typewriter below those sheets of paper on which Auden typed his poetry. Subsequently, these backing sheets were reused in the poet's 'working correspondence' to Welsh-Austrian writer Stella Musulin. While standard image-digitization technologies fail to capture these 3D indented impressions, they can successfully be represented by means of Photometric Stereo, which has been fruitfully employed in the research of 3D cultural-heritage objects. Following a detailed outline of this method, this article demonstrates how Photometric Stereo can help to reconstruct poetry that has survived only in the form of indented impressions. Thus, the case study illustrates how computer vision can contribute to our understanding both of 'poetic' practices of composition and revision as well as of 'material' writing practices. It also has wide-ranging implications for re-conceptualizing sheets of paper as 3D objects in the research of literary documents from the twentieth century. [ABSTRACT FROM AUTHOR]

In the field of the Cultural Heritage (CH), image-based 2D and 3D digital acquisition is today the most common technique used to create digital replicas of existing artifacts. This is carried out for many reasons, such as the following: research, analysis, preservation, conservation, communication, and valorization. These activities usually require complementary specialized equipment, tailored to specific purposes in order to achieve the desired results. This equipment is not easy-to-find on the market, it is not always affordable for museums operators; it is sometimes expensive, and it usually needs tricky customizations. However, the development in recent years of more generalized, versatile, and affordable instruments and technologies has led to new approaches, leveraging a new generation of low-cost, adaptable equipment. This paper presents custom-made equipment following this new path, designed to provide optimized results through calibrated tools alongside the software to make it work. The essay focuses specifically on the self-production of instruments for the digital reproduction of ancient drawings, manuscripts, paintings, and other museum artifacts and their transformative impact on digitization techniques. The outcomes of self and custom-built equipment specifically produced for the contexts described in this paper highlight their potential to foster interdisciplinary collaboration, facilitate scholarly research, enhance conservation efforts, and promote cultural exchange. The final goal is to propose inexpensive equipment that is easy to use (even by not specifically trained operators) and that provides remarkable quality.

Line structured light (LSL) measurement systems can obtain high accuracy profiles, but the overall clarity relies greatly on the sampling interval of the scanning process. Photometric stereo (PS), on the other hand, is sensitive to tiny features but has poor geometrical accuracy. Cooperative measurement with these two methods is an effective way to ensure precision and clarity results. In this paper, an LSL-PS cooperative measurement system is brought out. The calibration methods used in the LSL and PS measurement system are given. Then, a data fusion algorithm with adaptive weights is proposed, where an error function that contains the 3D point cloud matching error and normal vector error is established. The weights, which are based on the angles of adjacent normal vectors, are also added to the error function. Afterward, the fusion results can be obtained by solving linear equations. From the experimental results, it can be seen that the proposed method has the advantages of both the LSL and PS methods. The 3D reconstruction results have the merits of high accuracy and high clarity.

Ductile cast iron pipes (DCIPs) are widely utilized in water and gas supply due to their exceptional mechanical and material properties. Quality control during production is paramount for optimal performance, with surface defect detection being of utmost importance. Currently, production sites primarily rely on manual visual inspection, which presents several challenges, including high labor intensity, low efficiency, and subjectivity in defect judgment. 2D detection algorithms based on single images struggle to effectively detect small defects such as pores, pinholes, and scratches, resulting in high missed detection rates and vulnerability to interference from oil and water stains. To address these issues, this paper proposes a symmetrical twin-light photometric stereo (TLPS) detection algorithm based on photometric stereo theory, and image correction algorithm for mitigating the effects of ambient light. A visual inspection prototype system was designed and successfully implemented at a production site. The system comprises eight visual inspection units, each equipped with a high-speed line scan camera and two bar light sources for image acquisition of moving DCIP. Under the Lambertian model and parallel light source assumption, the gradient expression in the texture direction of the DCIP surface is derived based on light source symmetry. Production experiment results demonstrate that the gradient map obtained through the proposed TLPS effectively enhances defect contrast and accurately detects and locates micro defects such as pores, pinholes, and scratches on cast pipe surfaces. This verifies the effectiveness of the TLPS algorithm, addresses the shortcomings of traditional 2D detection algorithms, and lays the foundation for subsequent online detection of DCIP surface defects.

The measurement of the density of woven fabrics based on the vision method has been widely developed. This study used a photometric stereo method to measure the warp and weft density of woven fabrics based on the 3D surface structure. Six 2D images of the fabric were recorded, each with a different lighting direction. The six images were then reconstructed using the unbiased photometric stereo algorithm to produce the three-dimensional surface structure. The reconstructed image was used to detect and correct the skew angle with the Hough transform. For each image, a depth profile was made toward the x-axis to get the weft curve and towards the y-axis to get the warped curve. The two depth curves were filtered using a locally weighted smoothing (LOESS) filter. This study successfully measured the density of woven fabric with an average error for warp and weft of 0.64% and 0.45%, respectively. [ABSTRACT FROM AUTHOR]

Photometric stereo methods typically rely on RGB cameras and are usually performed in a dark room to avoid ambient illumination. Ambient illumination poses a great challenge in photometric stereo due to the restricted dynamic range of the RGB cameras. To address this limitation, we present a novel method, namely Event Fusion Photometric Stereo Network (EFPS-Net), which estimates the surface normals of an object in an ambient light environment by utilizing a deep fusion of RGB and event cameras. The high dynamic range of event cameras provides a broader perspective of light representations that RGB cameras cannot provide. Specifically, we propose an event interpolation method to obtain ample light information, which enables precise estimation of the surface normals of an object. By using RGB-event fused observation maps, our EFPS-Net outperforms previous state-of-the-art methods that depend only on RGB frames, resulting in a 7.94% reduction in mean average error. In addition, we curate a novel photometric stereo dataset by capturing objects with RGB and event cameras under numerous ambient light environments. • We introduce a novel photometric stereo method with event camera. • We propose a method for the photometric stereo task under ambient illumination. • We curate the event camera photometric stereo dataset in the real world. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiaotong University (Science) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the field of the Cultural Heritage (CH), image-based 2D and 3D digital acquisition is today the most common technique used to create digital replicas of existing artifacts. This is carried out for many reasons, such as the following: research, analysis, preservation, conservation, communication, and valorization. These activities usually require complementary specialized equipment, tailored to specific purposes in order to achieve the desired results. This equipment is not easy-to-find on the market, it is not always affordable for museums operators; it is sometimes expensive, and it usually needs tricky customizations. However, the development in recent years of more generalized, versatile, and affordable instruments and technologies has led to new approaches, leveraging a new generation of low-cost, adaptable equipment. This paper presents custom-made equipment following this new path, designed to provide optimized results through calibrated tools alongside the software to make it work. The essay focuses specifically on the self-production of instruments for the digital reproduction of ancient drawings, manuscripts, paintings, and other museum artifacts and their transformative impact on digitization techniques. The outcomes of self and custom-built equipment specifically produced for the contexts described in this paper highlight their potential to foster interdisciplinary collaboration, facilitate scholarly research, enhance conservation efforts, and promote cultural exchange. The final goal is to propose inexpensive equipment that is easy to use (even by not specifically trained operators) and that provides remarkable quality. [ABSTRACT FROM AUTHOR]

The standard photometric stereo model makes several assumptions that are rarely verified in experimental datasets. In particular, the observed object should behave as a Lambertian reflector, and the light sources should be positioned at an infinite distance from it, along a known direction. Even when Lambert's law is approximately fulfilled, an accurate assessment of the relative position between the light source and the target is often unavailable in real situations. The Hayakawa procedure is a computational method for estimating such information directly from data images. It occasionally breaks down when some of the available images excessively deviate from ideality. This is generally due to observing a non-Lambertian surface, or illuminating it from a close distance, or both. Indeed, in narrow shooting scenarios, typical, e.g., of archaeological excavation sites, it is impossible to position a flashlight at a sufficient distance from the observed surface. It is then necessary to understand if a given dataset is reliable and which images should be selected to better reconstruct the target. In this paper, we propose some algorithms to perform this task and explore their effectiveness. [ABSTRACT FROM AUTHOR]

• Photometric Stereo and Elastomeric Sensor Imaging can be used to examine documents. • Ink line application sequence can be inferred from 3D imaging. • Ink line application direction can be inferred from 3D imaging. • Photometric stereo is a non-contact and non-destructive 3D imaging technique. • Elastomeric sensor imaging yields quantitative depth information. Photometric Stereo and Elastomeric Sensor Imaging were assessed for measuring the 3-dimensional (3D) morphology of questioned document samples. Photometric stereo is shown to be a powerful non-contact technique for revealing micron level detail of the samples examined. Elastomeric Sensor Imaging is shown to complement photometric stereo by yielding equivalent results. Additionally, this technique allows quantification of the morphological depth information. The techniques were applied to 2 different types of questioned document sample. Firstly, written signatures were examined. Both techniques were able to reveal characteristic features that could be used to infer stroke direction and ink line application sequence. Secondly toner/ink intersections were examined. Both techniques allowed visualisation of 3D features which were used to infer the sequence of application. [ABSTRACT FROM AUTHOR]

The pilling grade is one of the important indexes to evaluate the quality of fabric; the traditional method for pilling evaluation is based on manual operation, which is subjective. In this paper, a self-developed system is established and a photometric stereo-based three-dimensional (3D) acquisition method for pilling evaluation has been demonstrated. Firstly, a 3D surface model is obtained by using photometric stereo, mapping a 3D model to a two-dimensional image; the gray value is used to represent the depth value of the fabric surface and the pilling segmentation is realized by the Otsu threshold method after filtering the fabric texture by the relative total variance algorithm. The pilling number and pilling area are used as eigenvectors for pilling classification using the support vector machine. Finally, the classification accuracy of the pilling grade was 95.9%. For pilling evaluation, the developed system and method can be considered reliable and effective according to the experimental results. [ABSTRACT FROM AUTHOR]

Following from our recent work, we present results of a detailed analysis of a representative sample of nearby galaxies. The photometric parameters of the morphological components are obtained from bulge-disk decompositions, using GALFIT software. The previously obtained method and library of numerical corrections for dust, decomposition and projection effects, are used to correct the measured (observed) parameters to intrinsic values. Observed and intrinsic galaxy dust and star-formation related scaling relations are presented, to emphasize the scale of the biases introduced by these effects. To understand the extent to which star-formation is distributed in the young stellar disks of galaxies, star-formation connected relations which rely on measurements of scale-lengths and fluxes/luminosities of Hα images, are shown. The mean dust opacity, dust-to-stellar mass and dust-to-gas ratios of the sample, together with the main characteristics of the intrinsic relations are found to be consistent with values found in the literature. [ABSTRACT FROM AUTHOR]

Fatigue detection has many applications, one of them is intelligent transportation for accident prevention. It is challenging to detect driver fatigue adopting photometric stereo and 3D fatigue-related facial action unit identification. During 3D reconstruction shadows are encountered, causing reconstruction error, therefore, for accurate fatigue detection shadow removal is crucial. This study presents a novel snake based approach for shadow handling in photometric stereo. Seven light sources were employed and region visibility of the light source for each region of interest (ROI) was deduced. Surface normal estimation enhancement was achieved via snake model based shadow labeling. Discrepancies measured among shadowed regions and ROIs were recorded. Light sources having high intersection with the shadow map were excluded from the reconstruction process at pixel level. This results in an automatic driver fatigue related action unit detection employing uncalibrated Lambertian surface under shadow conditions. The developed method was tested and compared with other established driver fatigue detection methods and higher performance in terms of accuracy (i.e. 97.85%) was achieved. The developed method proves to be well suited for driver fatigue detection under uncalibrated conditions.

Predicting accurate normal maps of objects from two-dimensional images in regions of complex structure and spatial material variations is challenging using photometric stereo methods due to the influence of surface reflection properties caused by variations in object geometry and surface materials. To address this issue, we propose a photometric stereo network called a RMAFF-PSN that uses residual multiscale attentional feature fusion to handle the "difficult" regions of the object. Unlike previous approaches that only use stacked convolutional layers to extract deep features from the input image, our method integrates feature information from different resolution stages and scales of the image. This approach preserves more physical information, such as texture and geometry of the object in complex regions, through shallow-deep stage feature extraction, double branching enhancement, and attention optimization. To test the network structure under real-world conditions, we propose a new real dataset called Simple PS data, which contains multiple objects with varying structures and materials. Experimental results on a publicly available benchmark dataset demonstrate that our method outperforms most existing calibrated photometric stereo methods for the same number of input images, especially in the case of highly non-convex object structures. Our method also obtains good results under sparse lighting conditions. [ABSTRACT FROM AUTHOR]

The Annunciation by Guido di Pietro from Mugello, known as Beato Angelico, is a wide tempera painting with some fine gold foil placed on a wooden support, today hosted at the Museum of the Basilica of Santa Maria delle Grazie, in San Giovanni Valdarno. On the occasion of the exhibition "Masaccio e Angelico. Dialogo sulla verità nella pittura", the museum asked to the Department of Architecture at the University of Bologna to develop a digital high-resolution surrogate to favour deep investigations, to plan restoration and to simply tell the stories behind the artwork. Two tasks were accomplished: to let visitors discover the secrets in the painting and to let scholars study the artwork, to better understand the masterpiece. This paper introduces the outcomes of the research developed to digitize the Annunciation, following a dedicated pipeline developed to improve the fruition of its digital replica, originated from different input sources, and surrogating the user experience on the real object. This work presents a method for the 3D reconstruction of the surfaces based on different techniques for elements with different depth resolutions (i.e., the painting and the wooden frame) which combine photogrammetry and photometric stereo exploiting both procedures and pushing forward the boundaries of Gigapixel Imaging and photogrammetric-based 3D model representation. [ABSTRACT FROM AUTHOR]

The recognition and dating of corrective stratigraphies, in the study of manuscript writings, is fundamental in the preparation of critical editions, for author philology and variant criticism, and in cases of authorship. The analysis of the different "corrective campaigns" of a text can now be carried out with some imaging techniques, from RTI, to interferometers, to photometric stereo, which allow to reconstruct the third dimension: "z", that of time: a technology widely used in cultural heritage, but still not very widespread in the study of texts. The contribution illustrates some cases of 3D reconstruction of manuscripts, from Dante to Leopardi, and proposes a protocol for sharing digital reproduction standards.

Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagation and attenuation, perspective viewing geometry and specular light reflection are considered. Many of works tackling Photometric Stereo (PS) problems often relax most of the aforementioned assumptions. Especially they ignore specular reflection and global illumination effects. In this work, we propose a CNN-based approach capable of handling these realistic assumptions by leveraging recent improvements of deep neural networks for far-field Photometric Stereo and adapt them to the point light setup. We achieve this by employing an iterative procedure of point-light PS for shape estimation which has two main steps. Firstly we train a per-pixel CNN to predict surface normals from reflectance samples. Secondly, we compute the depth by integrating the normal field in order to iteratively estimate light directions and attenuation which is used to compensate the input images to compute reflectance samples for the next iteration. Our approach sigificantly outperforms the state-of-the-art on the DiLiGenT real world dataset. Furthermore, in order to measure the performance of our approach for near-field point-light source PS data, we introduce LUCES the first real-world 'dataset for near-fieLd point light soUrCe photomEtric Stereo' of 14 objects of different materials were the effects of point light sources and perspective viewing are a lot more significant. Our approach also outperforms the competition on this dataset as well. Data and test code are available at the project page. [ABSTRACT FROM AUTHOR]

Copyright of Drawing Ideas Images / Disegnare Idee Immagini is the property of Gangemi Editore S.P.A. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The accumulative freeze-thaw cycles in cold regions could cause deteriorations to concrete pavements. The measurement of air-void system inside hardened concrete is important for estimating the freeze-thawing performance of a concrete pavement. The segmentation of air voids from the concrete surface image is one of the most essential and challenging steps for automated air-void measurement. Conventional automated air-voids segmentation methods rely on a color enhancement step to manually create a contrast between air voids and non-air-voids. This study aims to investigate the use of a photometric stereo method for automated segmentation of air voids in hardened concrete surface without a need for contrast enhancement. In the study, the photometric stereo system with a Charge-Coupled Device (CCD) camera and six illumination lights was designed to collect images of polished concrete specimen surfaces under various lighting directions. Two air-void segmentation approaches were developed in the study. One approach interpreted the air voids by the surface depth. The other approach interpreted the air voids by the surface normal. Consequently, the segmentation results were refined by a set of image processing procedures. The results showed that the proposed automated air-void segmentation procedures could detect air voids in concrete surface with a satisfactory accuracy. [ABSTRACT FROM AUTHOR]

Photometric stereo aims to recover the surface normals of a 3D object from various shading cues, establishing the relationship between two-dimensional images and the object geometry. Traditional methods usually adopt simplified reflectance models to approximate the non-Lambertian surface properties, while recently, photometric stereo based on deep learning has been widely used to deal with non-Lambertian surfaces. However, previous studies are limited in dealing with high-frequency surface regions, i.e., regions with rapid shape variations, such as crinkles, edges, etc., resulted in blurry reconstructions. To alleviate this problem, we present a normalized attention-weighted photometric stereo network, namely NormAttention-PSN, to improve surface orientation prediction, especially for those complicated structures. In order to address these challenges, in this paper, we (1) present an attention-weighted loss to produce better surface reconstructions, which applies a higher weight to the detail-preserving gradient loss in high-frequency areas, (2) adopt a double-gate normalization method for non-Lambertian surfaces, to explicitly distinguish whether the high-frequency representation is stimulated by surface structure or spatially varying reflectance, and (3) adopt a parallel high-resolution structure to generate deep features that can maintain the high-resolution details of surface normals. Extensive experiments on public benchmark data sets show that the proposed NormAttention-PSN significantly outperforms traditional calibrated photometric stereo algorithms and state-of-the-art deep learning-based methods. [ABSTRACT FROM AUTHOR]

Fabric pilling evaluation has been considered as an essential element for textile quality inspection. Traditional manual method is still based on human eyes and brain, which is subjective with low efficiency. This paper proposes an objective evaluation method based on semi-calibrated near-light Photometric Stereo (PS). Fabric images are digitalized by self-developed image acquisition system. The 3D depth information of each point could be obtained by PS algorithm and then mapped to 2D grayscale image. After that, the non-textured image could be filtered by using the Gaussian low-pass filter. The pilling segmentation is conducted by using global iterative threshold segmentation method, and then K-Nearest Neighbor (KNN) is finally selected as a tool for the grade classification of fabric pilling. Our experimental results show that the proposed evaluation system could achieve excellent judging performance for the objective pilling evaluation. [ABSTRACT FROM AUTHOR]

Image-based 3D reconstruction has been successfully employed for micro-measurements and industrial quality control purposes. However, obtaining a highly-detailed and reliable 3D reconstruction and inspection of non-collaborative surfaces is still an open issue. Photometric stereo (PS) offers the high spatial frequencies of the surface, but the low frequency is erroneous due to the mathematical model's assumptions and simplifications on how light interacts with the object surface. Photogrammetry, on the other hand, gives precise low-frequency information but fails to utilize high frequencies. As a result, in this research, we present a fusion strategy in Fourier domain to replace the low spatial frequencies of PS with the corresponding photogrammetric frequencies in order to have correct low frequencies while maintaining high frequencies from PS. The proposed method was tested on three different objects. Different cloud-to-cloud comparisons were provided between reference data and the 3D points derived from the proposed method to evaluate high and low frequency information. The obtained 3D findings demonstrated how the proposed methodology generates a high-detail 3D reconstruction of the surface topography (below 20 µm) while maintaining low-frequency information (0.09 µm on average for three different testing objects) by fusing photogrammetric and PS depth data with the proposed FFT-based method. [ABSTRACT FROM AUTHOR]

Photometric Stereo (PS) addresses the challenge of reconstructing a three-dimensional (3D) representation of an object by estimating the 3D normals at all points on the object's surface. This is achieved through the analysis of at least three photographs, all taken from the same viewpoint but with distinct lighting conditions. This paper introduces a novel approach for Universal PS, i.e., when both the active lighting conditions and the ambient illumination are unknown. Our method employs a multi-scale encoder–decoder architecture based on Transformers that allows to accommodates images of any resolutions as well as varying number of input images. We are able to scale up to very high resolution images like 6000 pixels by 8000 pixels without losing performance and maintaining a decent memory footprint. Moreover, experiments on publicly available datasets establish that our proposed architecture improves the accuracy of the estimated normal field by a significant factor compared to state-of-the-art methods. Code and dataset available at: https://clement-hardy.github.io/Uni-MS-PS/index.html. • Universal photometric stereo for all lighting and environments. • 3D reconstruction method that scales up to very high-resolution images. • New diverse training synthetic dataset for photometric stereo. [ABSTRACT FROM AUTHOR]

Current concrete spalling detection and measurement methods are sparse; despite recent research and commercial offerings using laser scanners, manual measurement is still the industry standard. This paper presents a spalling 3D reconstruction and measurement method. The method uses images illuminated with angled and directional lighting and three neural networks for photometric stereo 3D mesh generation and spalling volume measurement. The proposed method was benchmarked on a laboratory dataset of spalled concrete slabs against a high-resolution laser scanner, yielding an average height error of 0.0 mm and a standard deviation of 1.3 mm. Volume comparisons showed that with manual input, the method achieved a mean absolute percentage error of 22%. Finally, the proposed technique was compared to manual measurements and benchmarked on a spalled concrete structure against a Trimble X12 laser scanner. This research can provide inspectors with increased data interpretability and reduced imaging time for concrete defect mapping. • We use photometric stereo to produce 3D meshes of concrete surfaces. • Data acquisition and processing is fast and human-interpretable. • The 3D meshes are automatically analysed to locate spalling and measure volume. • Our method outperformed the results of research alternatives. • We compared our method to manual and commercial alternatives used in industry. [ABSTRACT FROM AUTHOR]