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Purpose: To examine if there are differences in smile esthetic evaluation scores by orthodontists and prosthodontists and if there are changes in those scores when two evaluations are performed, when the time of observation is increased, and when the lips are present or absent from the images. Materials and Methods: In total, 12 individuals participated in this pilot study. Two digital images were taken from each individual. The first digital image was a smile view, and the second image was an intraoral view. Two presentation files were prepared, with two images for each individual. The smile and dental attractiveness ratings were obtained from 10 specialists. Results: Repeated measures ANOVA was applied including all four within-subject factors, the evaluator (E), the repetition (R), the time of observation (T), and the presence or not of lips (L). Factors E, T, and L each had a statistically significant main effect. E and R had a statistically significant combined effect. In particular, the esthetic score for the view with smile was overall higher than for the intraoral view. The same results were obtained when the analysis was repeated with the 10 evaluators grouped to prosthodontists and orthodontists, and the prosthodontists tended to score higher than the orthodontists. Conclusions: The evaluator and the presence of lips have a statistically significant effect. The present pilot study has found that three (E, L, R) out of four factors (T) are important for the evaluation of dental esthetics. [ABSTRACT FROM AUTHOR]

This study investigates bilayers of 0.94(Na1/2Bi1/2)TiO3–0.06BaTiO3 (NBT–6BT) and 0.90(Na1/2Bi1/2)TiO3–0.06BaTiO3–0.04(K0.5Na0.5)NbO3 (NBT–6BT–4KNN) using digital image correlation, enabling the separate analysis of strain response in each layer. The bilayers were electrically connected without mechanical connection (polarization coupled) as well as mechanically and electrically connected (polarization and strain coupled) to determine the role of interlayer mechanical interactions. The large signal longitudinal and transverse piezoelectric coefficients, d 33 ∗ and d 31 ∗ , were characterized for both cases. In the polarization coupled composite, d 33 ∗ decreased linearly from 410 to 260 pm/V with increasing vol. % NBT–6BT. In contrast, in the polarization and strain coupled case, d 33 ∗ and d 31 ∗ were maximum at 50 vol. % NBT–6BT with values of 440 and −130 pm/V, respectively, highlighting the critical role of strain interactions in ceramic–ceramic composites. The stress-induced phase transformation through strain coupling significantly impacted the electromechanical response, with improved strain values observed in the NBT–6BT–4KNN layer. Furthermore, this study highlights the variability in the significance of strain coupling within bilayer systems as a function of the altering volume fraction of their constituent components. [ABSTRACT FROM AUTHOR]

Digital Pathology or whole slide imaging (WSI) is a diagnostic evaluation technique that produces digital images of high quality from tissue fragments. These images are formed on glass slides and evaluated by pathologist with the aid of microscope. As the concept of digital pathology is introduced, these high quality images are digitized and produced on-screen whole slide images in the form of digital files. This has paved the way for pathologists to collaborate with other pathology professionals in case of any additional recommendations and also provides remote working opportunities. The application of digital pathology in clinical practice is glazed with several advantages and adopted by pathologists and researchers for clinical, educational and research purposes. Moreover, digital pathology system integration requires an intensive effort from multiple stakeholders. All pathology departments have different needs, case usage, and blueprints, even though the framework elements and variables for effective clinical integration can be applied to any institution aiming for digital transformation. This article reviews the background and developmental phases of digital pathology and its application in clinical services, educational and research activities. [ABSTRACT FROM AUTHOR]

Efficient multiple pseudo-random number sequences (PRNS) and substitution boxes (S-boxes) are two of the most consequential construction blocks jointly assumed commonly for secure data encryption. Multiple aspects pave the way to address large-scale multimedia data. However, the computational efforts on multiple constructions may limit the required ciphering. Therefore, reducing the computational cost of various patterns, such as PRNS and S-boxes, is the core requirement for an efficient cryptosystem. For this achievement, this article addresses the challenge of constructing secure S-boxes with enhanced nonlinearity (NL) and keyspace in image encryption. Our technique aims to bolster security in digital image encryption methods by prioritizing robustness over conventional complexity. We present a novel and efficient cryptosystem that utilizes Projective Coordinates (PCs) of Elliptic Curves (ECs) for encrypting digital images. Initially, we leverage the power of PCs of ECs over the set of integers modulo p r , where p is prime and r = 9 . Using ECs and applying trace mappings, we create optimal 8 × 8 S-boxes for pixel substitution in digital images. Moreover, the proposed scheme generates 2 108 S-boxes in a single case of the proposed scheme. In addition, Pseudo-Random Numbers (PRNs) are generated from ECs over modulo p r to enhance security. Further, computational experiments demonstrate that our proposed cryptosystem offers superior protection against linear, differential, and statistical attacks compared to existing cryptosystems. [ABSTRACT FROM AUTHOR]

It can be argued that contemporary colourisation techniques have in the last fifteen years created an artificial desire – which has now become normalised into an expectancy – for 'colour' in all domains related to images of the past, rhetorically considered today as some sort of benchmark to ensure emotional engagement, relatedness, familiarity, authenticity, humanity and realism. A genealogical or archaeological approach to this trend could consist in looking at the history of one of the most iconic film colour stocks ever manufactured, Kodachrome. A look into Kodachrome's history, cultural recognisability and role in capturing key moments of the XXth century, can help us better assess the history of colourisation and its ent stronghold on historical image fantasy. [ABSTRACT FROM AUTHOR]

That digitally converting greyscale historical photographs and pieces of film into colour pixels can make their protagonists look more human has become something of a refrain among practitioners of this art and users and champions of their images. Such claims raise the question of whether colourisation can counter the experiments of what Griselda Pollock calls a 'laboratory for the systematic destruction of the humanity of the human' (2011. "Death in the Image: The Responsibility of Aesthetics in Night and Fog (1955) and Kapò (1959)." In Concentrationary Cinema: Aesthetics as Political Resistance in Alain Resnais's "Night and Fog", edited by Pollock and Max Silverman, 258–301. New York; Oxford: Berghahn, 271). Pollock is discussing a poem by Primo Levi, who worked as a slave at the Auschwitz plant of dye industry syndicate IG Farben. This article develops the first extended analysis of a documentary that experiments with colourisation in remembering the inhuman treatment of people who were imprisoned at the Auschwitz complex and other camps. My reading of Auschwitz Untold: In Colour (David Shulman, 2020) seeks to contribute to our understanding of this way of digitally altering still photographs and motion picture film by exploring its intersection with issues of de/humanisation. Drawing on scholarship on the interplay between stillness and movement, the construction of race in art and the alliance between the Nazi regime and the dye industry, I argue that Auschwitz Untold tests colourisation's capacity to make the past present and photographs cinematic. However, it overlooks the critical role of colour images in the development of violent ways of grouping and ranking humans and the entangled histories of dye and dehumanising forms of work and death. [ABSTRACT FROM AUTHOR]

The article analyses the phenomenon created by colouring black and white archival footage in Revolution in Colour (Martin Dwan, 2016) and Warsaw Uprising (Powstanie Warszawskie, Jan Komasa, 2014) and focuses on how the contemporary cultural and media environment affects our perceptual mechanisms and makes the colourisation of archival black and white footage attractive. At the beginning of the paper, I briefly review the ontology of the digital image and concentrate on the way in which the archival footage is changed by being converted from analogue to digital. In addition, I focus on the possible social causes of digital colourisation and the visual consequences of adding colour to archival footage, emphasising the problem of spatiality, floating layers and reduction of temporal distance. Subsequently, I analyse different visual effects generated by the added colours in more detail in the case of the Irish film, along with a typology specifically constructed for this purpose, and with the description of the operations of immediacy and hypermediacy for coloured material. In the final subsection, I examine the phenomenological consequences of 'over-restoration' in the case of the Polish film and how the creators erased the traces of time. In this comparative analysis of the two films, my aim is to explore the differences between the two processes of digitisation and colourisation of archival footage. [ABSTRACT FROM AUTHOR]

Visibility can be interpreted as the largest distance of an object that can be recognized or detected under a bright environment that can be used as an environmental indicator for weather conditions and air pollution. The accuracy of the classical approach of visibility calculation, in which meteorological laws and image feature extraction from digital images are used, depends on the quality and noise disturbances of the image. Therefore, artificial intelligence (AI) and digital image approaches have been proposed for visibility estimation in the past. Image features for the whole digital image are generated by pre-trained convolutional neural networks, and the Artificial Neural Network (ANN) is designed for correlation between image features and visibilities. Instead of using the information of the whole digital images, past research has been proposed to identify effective subregions from which image features are generated. A generalized regression neural network (GRNN) was designed to correlate the image features with the visibilities. Past research results showed that this method is more accurate than the classical approach of using handcrafted features. However, the selection of effective subregions of digital images is not fully automated and is based on manual selection by expert judgments. In this paper, we proposed an automatic effective subregion selection method using landmark object extraction techniques. Image features are generated from these LMO subregions, and the ANN is designed to approximate the mapping between LMO regions' feature values and visibility values. The experimental results show that this approach can minimize the reductant information for ANN training and improve the accuracy of visibility estimation as compared to the single image approach. [ABSTRACT FROM AUTHOR]

Traditional facial recognition is realized by facial recognition algorithms based on 2D or 3D digital images and has been well developed and has found wide applications in areas related to identification verification. In this work, we propose a novel live face detection (LFD) method by utilizing snapshot spectral imaging technology, which takes advantage of the distinctive reflected spectra from human faces. By employing a computational spectral reconstruction algorithm based on Tikhonov regularization, a rapid and precise spectral reconstruction with a fidelity of over 99% for the color checkers and various types of "face" samples has been achieved. The flat face areas were extracted exactly from the "face" images with Dlib face detection and Euclidean distance selection algorithms. A large quantity of spectra were rapidly reconstructed from the selected areas and compiled into an extensive database. The convolutional neural network model trained on this database demonstrates an excellent capability for predicting different types of "faces" with an accuracy exceeding 98%, and, according to a series of evaluations, the system's detection time consistently remained under one second, much faster than other spectral imaging LFD methods. Moreover, a pixel-level liveness detection test system is developed and a LFD experiment shows good agreement with theoretical results, which demonstrates the potential of our method to be applied in other recognition fields. The superior performance and compatibility of our method provide an alternative solution for accurate, highly integrated video LFD applications. [ABSTRACT FROM AUTHOR]

The "pivot" to digital that many arts and culture organisations faced during the Covid-19 pandemic, revealed a complex nexus of effects that includes significant accessibility improvements (for example, for D/deaf and disabled audiences) but also a replication of many pre-existing exclusions. We argue that understanding the experiences of online audiences can help inform arts and culture organisations' next steps in adapting to the current period of uncertainty, particularly with a cost-of-living crisis reducing leisure spending. Drawing on data from the Digital Experience survey carried out in the UK by Indigo Ltd. (2020–21), this article explores how diverse online audiences judged online theatre experiences and their potential impact on future behaviour. By analysing respondents' quality of experience in tandem with demographic information and how participants accessed the online experience, we provide evidence showing that online participation, particularly if the experience is high quality, has the potential to increase future in-person participation. [ABSTRACT FROM AUTHOR]

Robust image watermarking that can resist camera shooting has gained considerable attention in recent years due to the need to protect sensitive printed information from being captured and reproduced without authorization. Indeed, the evolution of smartphones has made identity watermarking a feasible and convenient process. However, this process also introduces challenges like perspective distortions, which can significantly impair the effectiveness of watermark detection on freehandedly digitized images. To meet this challenge, ResNet50-based ensemble of randomized neural networks (ReRNet), a recursive convolutional neural network-based correction method, is presented for the print-cam process, specifically applied to identity images. Therefore, this paper proposes an improved Fourier watermarking method based on ReRNet to rectify perspective distortions. Experimental results validate the robustness of the enhanced scheme and demonstrate its superiority over existing methods, especially in handling perspective distortions encountered in the print-cam process. [ABSTRACT FROM AUTHOR]

We propose an objective method to quantify the solid-particle removal rate from fabric. The method extracts the ratio (K/S) λ of light absorption K to scattering S at wavelength λ from fused digital images captured under a stereo photometric system in which illumination is from four directions. Three different white polyester fabrics were contaminated with iron oxide particles. Digital images of the fabrics were obtained before contamination, then before and after cleaning. The (K/S) λ ratios extracted from images were used in a fabric-detergency formula to determine the solid-particle removal rate. Digital image acquisition conditions were optimized to minimize the effects of fabric structural factors. Our method was faster, more accurate, and cheaper than existing methods. Moreover, it is nondestructive and does not require a tracer. The average accuracy of the proposed method was improved by 44.77% compared with the existing surface reflectance method and by 42.51% compared with the binary image method. Moreover, the accuracy was further increased by calculating (K/S) λ for a signal that corresponds to the color of the contaminant particles. This method can be used to quantify the effectiveness of self-cleaning textiles and garment-care machines. [ABSTRACT FROM AUTHOR]

The study investigates effect of particle shape on gas bypassing and mixing of gas‐fluidized Geldart A particles. A shallow fluidized bed (FB), configured at benchscale, was used with digital image analysis (DIA) for the investigation. The extent of scatter of tracer particles throughout the bed was assessed from DIA images of defluidized powder. A novel method employing Jupyter notebook software, was used to directly determine Mixing Index from digital images. Remarkably, platelet‐shaped China clay powder displayed the best mixing characteristics (Mixing Index: 0.79) with no significant bypassing. Angular shaped Quartz displayed moderate mixing (Mixing Index: 0.67), but high bypassing (Bypassing Index: 0.75). Contrary to conventional assumptions, spherical‐shaped diatomite exhibited poor mixing (Mixing Index: 0.61) with the highest bypassing (Bypassing Index: 0.82). Platelet particles performed well even with fines removal. Most likely, particle shape significantly influenced the number of available particle contact points, tracer migration, and traceronparticle binding. [ABSTRACT FROM AUTHOR]

Cloud Computing (CC) is a fast emerging field that enables consumers to access network resources on-demand. However, ensuring a high level of security in CC environments remains a significant challenge. Traditional encryption algorithms are often inadequate in protecting confidential data, especially digital images, from complex cyberattacks. The increasing reliance on cloud storage and transmission of digital images has made it essential to develop strong security measures to stop unauthorized access and guarantee the integrity of sensitive information. This paper presents a novel Crayfish Optimization based Pixel Selection using Block Scrambling Based Encryption Approach (CFOPS-BSBEA) technique that offers a unique solution to improve security in cloud environments. By integrating steganography and encryption, the CFOPS-BSBEA technique provides a robust approach to secure digital images. Our key contribution lies in the development of a three-stage process that optimally selects pixels for steganography, encodes secret images using Block Scrambling Based Encryption, and embeds them in cover images. The CFOPS-BSBEA technique leverages the strengths of both steganography and encryption to provide a secure and effective approach to digital image protection. The Crayfish Optimization algorithm is used to select the most suitable pixels for steganography, ensuring that the secret image is embedded in a way that minimizes detection. The Block Scrambling Based Encryption algorithm is then used to encode the secret image, providing an additional layer of security. Experimental results show that the CFOPS-BSBEA technique outperforms existing models in terms of security performance. The proposed approach has significant implications for the secure storage and transmission of digital images in cloud environments, and its originality and novelty make it an attractive contribution to the field. Furthermore, the CFOPS-BSBEA technique has the potential to inspire further research in secure cloud computing environments, making the way for the development of more robust and efficient security measures. [ABSTRACT FROM AUTHOR]

In digital communication, the protection of digital images containing user-sensitive information is an emerging area of research. In the literature, several image encryption schemes have been proposed but they might not be appropriate for use in real-world situations. Some of them are computationally expensive, and some do not offer high security. In this article, we introduce an innovative and robust solution to this challenge: a secure multi-index multi-step image encryption scheme specifically designed for greyscale images. The proposed encryption scheme uses a hybrid combination of substitution, permutation and hyperchaotic structure. The substitution operation is achieved through a substitution box that is defined over the finite field. The permutation operation is achieved through a multi-index permutation algorithm. A hyperchaotic sequence is generated from a four-dimensional hyperchaotic map that is used in bitwise XOR operation for providing masking of image pixels. A comprehensive evaluation of our proposed encryption scheme against established security benchmarks reveals its resilience. Our proposed scheme not only passes all well-known security tests but also attains test values closely aligned with optimal benchmarks. Notably, our encryption scheme exhibits noteworthy efficiency, making it well suited for deployment on low-memory devices. This work not only contributes to advancing the field of digital communication but also underscores the practicality and efficiency of the proposed scheme for real-world applications [ABSTRACT FROM AUTHOR]

Chip defect detection is a crucial aspect of the semiconductor production industry, given its significant impact on chip performance. This paper proposes a lightweight neural network with dual decoding paths for LED chip segmentation, named LDDP-Net. Within the LDDP-Net framework, the receptive field of the MobileNetv3 backbone is modified to mitigate information loss. In addition, dual decoding paths consisting of a coarse decoding path and a fine-grained decoding path in parallel are developed. Specifically, the former employs a straightforward upsampling approach, emphasizing macro information. The latter is more detail-oriented, using multiple pooling and convolution techniques to focus on fine-grained information after deconvolution. Moreover, the integration of intermediate-layer features into the upsampling operation enhances boundary segmentation. Experimental results demonstrate that LDDP-Net achieves an mIoU (mean Intersection over Union) of 90.29% on the chip dataset, with parameter numbers and FLOPs (Floating Point Operations) of 2.98 M and 2.24 G, respectively. Comparative analyses with advanced methods reveal varying degrees of improvement, affirming the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The modern society generates vast amounts of digital content, whose credibility plays a pivotal role in shaping public opinion and decision-making processes. The rapid development of social networks and generative technologies, such as deepfakes, significantly increases the risk of disinformation through image manipulation. This article aims to review methods for verifying images' integrity, particularly through deep learning techniques, addressing both passive and active approaches. Their effectiveness in various scenarios has been analyzed, highlighting their advantages and limitations. This study reviews the scientific literature and research findings, focusing on techniques that detect image manipulations and localize areas of tampering, utilizing both statistical properties of images and embedded hidden watermarks. Passive methods, based on analyzing the image itself, are versatile and can be applied across a broad range of cases; however, their effectiveness depends on the complexity of the modifications and the characteristics of the image. Active methods, which involve embedding additional information into the image, offer precise detection and localization of changes but require complete control over creating and distributing visual materials. Both approaches have their applications depending on the context and available resources. In the future, a key challenge remains the development of methods resistant to advanced manipulations generated by diffusion models and further leveraging innovations in deep learning to protect the integrity of visual content. [ABSTRACT FROM AUTHOR]

In recent years, the internet has developed rapidly. With the popularity of social media, uploading and backing up digital images has become the norm. A huge number of digital images are circulating on the internet daily, and issues related to information security follow. To protect intellectual property rights, digital watermarking is an indispensable technology. However, the common lossy compression technology in the network transmission process is a big problem for watermarking. This paper describes an innovative semi-blind watermarking method with the use of deep convolutional generative adversarial networks (DCGANs) for hiding and extracting watermarks from JPEG-compressed images. The proposed method achieves an average peak signal-to-noise ratio (PSNR) of 49.99 dB, a structural similarity index (SSIM) of 0.95, and a bit error rate (BER) of 0.008 across varying JPEG quality factors. The process is based on an embedder, decoder, generator, and discriminator. It allows watermarking, decoding, or reconstruction to be symmetric such that there is less distortion and durability is improved. It constructs a specific generator for each image and watermark that is supposed to be protected. Experimental results show that, with the variety of JPEG quality factors, the restored watermark achieves a remarkably low corrupted rate, outstripping recent deep learning-based watermarking methods. [ABSTRACT FROM AUTHOR]

Background/Objectives: The damage assessment of dental instruments, such as endodontic files, is crucial to ensure patient safety and treatment quality. Conventional scanning electron microscopy (SEM) has been the gold standard for this purpose; however, its limited accessibility and complex sample preparation protocols hinder its routine use in clinical settings. This study proposes a novel system that leverages digital photography and advanced image processing techniques as a viable alternative to SEM. Methods: Our system accurately detects early instrument damage by capitalizing on the high resolution of digital images. Its exceptionally user-friendly interface, portability, and key features make it highly suitable for daily clinical practice. Results: Our findings suggest that the proposed system provides image quality comparable to SEM. Conclusions: Image stacking provides a practical, efficient, and objective method for assessing endodontic instruments' morphology. By detecting early damage, this system significantly improves the safety and quality of endodontic procedures, especially for reusable NiTi files, instilling confidence and security in its use. It offers a cost-effective and user-friendly alternative to traditional methods such as visual inspection and SEM, making it a comfortable and confident choice for both research and clinical settings. [ABSTRACT FROM AUTHOR]

Background Size-specific dose estimates is an important metric for personalizing dose measurements during abdominal computed tomography (CT) examination. This study aimed to establish patient size-specific dose data as a guide for dose monitoring of abdominal computed tomography examinations among Nigerians. Methods Abdominal CT images of adult subjects obtained from two CT scanners - a light speed VCT --ZTe; (GE Healthcare) 16 -- Slice and a Brivo CT 385 series; (GC Healthcare) 16-slice scanners were used in the study. The estimated computed tomography dose index volume (CTDIvol) and dose length product (DLP) were extracted from the CT dose report on the patients' electronic Image folders. The effective size of the abdomen was obtained by using electronic caliper on the scanner console to measure the anterior-posterior and lateral dimensions at the level of the widest diameter on the image. With Table1A from the AAPM report 220, conversion factors were determined for a total of 264 abdominal CT images. The corresponding conversion factor was multiplied by the CTDIvol to obtain the size specific dose estimates (SSDE). The relationships between effective diameter (ED), CTDIVOL and age on SSDE were analyzed using minitab statistical software version 17. Results The mean CTDlvol was 6.94+ 1.63mGy, while SSDE was 9.76 + 2.56mGy. The SSDE decreased significantly with effective diameter, and increased significantly with the CTDI vol. The effective diameter measured between 8.72.90 and 37.70cm. Conclusion The study concludes that the CTDvol and patient's abdominal size are determinant factors in the development of a size-specific radiation protection protocol and optimization of patient dose during abdominal CT examinations based on scanner output. [ABSTRACT FROM AUTHOR]

Understanding the long-term behavior of brittle rocks requires fundamental consideration of time-dependent strain evolution and brittle creep processes. Previous studies have evaluated sub-critical crack growth during time-dependent deformation and damage evolution in brittle rocks; however, there is an incomplete knowledge of how damage evolves spatially and temporally within the body of intact rocks, where distributed regions of damage interact and coalesce during creep. This paper presents laboratory research focusing on evaluating brittle creep damage processes in Stanstead granite (SG) using 2-dimensional digital image correlation (2D-DIC). In the laboratory, the prismatic SG specimens were loaded beyond an estimated Crack Damage stress threshold (CD) level and then maintained a constant stress to initiate the creep process. DIC was used to characterize full-field spatiotemporal strain evolution, which was then interpreted in the context of local regions of "damage", determined according to a strain-based criterion. A method was proposed for identifying "existing" and "new" damage regions over specified intervals during the test, followed by spatial clustering of these regions to assess their spatiotemporal evolution. The clustering analysis results demonstrated the extension of existing damage regions was the main damage process during brittle creep, which is consistent with existing models of sub-critical crack growth. In addition, temporal analysis of tensile and shear strains on a point-by-point basis revealed both new damage formation and the strain concentration within existing damaged regions significantly contribute to overall specimen strain during primary creep. In contrast, during secondary creep, increases in specimen deformation are influenced by the accumulation of strains within already damaged regions. Highlights: Strain-based evolution of damage is characterized during brittle creep in intact rocks. The temporal evolution of tensile and shear strains during primary and secondary creep is quantified. Relative influences of newly damaged regions are investigated in evaluating the damage process during creep. [ABSTRACT FROM AUTHOR]

All existing digital image-based numerical mean texture depth (MTD) calculation methods are unable to achieve a consistent level of acceptable accuracy for MTD values calculated at different degrees of pavement polishing. To solve this problem, a novel procedure is proposed to determine MTD as a function of degree of polishing represented by a texture statistic 'directional wear index' DWI. First, a relationship is established between DWI and the elevation Heq that defines the equivalent top surface of MTD. Heq is derived from 3-D digital image based on the concept of planation surface. Through studying laboratory specimens and field pavements polished to different degrees, a well-defined relationship between DWI and Heq was established. Knowing Heq from the Heq–DWI relationship, MTD is calculated as the mean depth of the voids between Heq and the test surface. The digital image-based procedure is able to concurrently determine MTD and the surface's degree of polishing. [ABSTRACT FROM AUTHOR]

Purpose: As an ancient art form, embroidery has strong practicality and artistic value. However, current embroidery style migration models produce images with unclear textures and a lack of stitch detail. So, in this paper, we propose a cyclic consistent embroidery style migration network with texture constraints, which is called Texture Cycle GAN (TCGAN). Design/methodology/approach: The model is based on the existing Cycle GAN network with an additional texture module. This texture module is implemented using a pre-trained Markovian adversarial network to synthesize embroidery texture features. The overall algorithm consists of two generative adversarial networks (for style migration) and the Markovian adversarial network (for texture synthesis). Findings: Qualitative and quantitative experiments show that, compared with the existing convolutional neural network style transfer algorithm, the introduction of the texture-constrained embroidery style transfer model TCGAN can effectively learn the characteristics of style images, generate digital embroidery works with clear texture and natural stitches and achieve more realistic embroidery simulation effects. Originality/value: By improving the algorithm for image style migration and designing a reasonable loss function, the generated embroidery patterns are made more detailed, which shows that the model can improve the realism of embroidery style simulation and help to improve the standard of embroidery craftsmanship, thus promoting the development of the embroidery industry. [ABSTRACT FROM AUTHOR]

Road damage often includes potholes, cracks, lane degradation, and surface shading. Potholes are a common problem in pavements. Detecting them is crucial for maintaining infrastructure and ensuring public safety. A thorough assessment of pavement conditions is required before planning any preventive repairs. Herein, we report the use of transfer learning and deep learning (DL) models to preprocess digital images of pavements for better pothole detection. Fourteen models were evaluated, including MobileNet, MobileNetV2, NASNetMobile, DenseNet121, DenseNet169, InceptionV3, DenseNet201, ResNet152V2, EfficientNetB0, InceptionResNetV2, Xception, and EfficientNetV2M. The study introduces a modified MobileNetV2 (MMNV2) model designed for fast and efficient feature extraction. The MMNV2 model exhibits improved classification, detection, and prediction accuracy by adding a five-layer pre-trained network to the MobileNetV2 framework. It combines deep learning, deep neural networks (DNN), and transfer learning, which resulted in better performance compared to other models. The MMNV2 model was tested using a dataset of 5,000 pavement images. A learning rate of 0.001 was used to optimize the model. It classified images into 'normal' or 'pothole' categories with 99.95% accuracy. The model also achieved 100% recall, 99.90% precision, 99.95% F1-score, and a 0.05% error rate. The MMNV2 model uses fewer parameters while delivering better results. It offers a promising solution for real-world applications in pothole detection and pavement assessment. [ABSTRACT FROM AUTHOR]

The dynamic identification of the deformation of a noise barrier column is of great significance to the monitoring of its health. At the same time, the maximum stress of the column is an important indicator for the evaluation of its health status. Traditional contact displacement monitoring installs sensors on the structure, requires a lot of wiring and data acquisition equipment, and establishes a relatively independent and stable displacement reference system. Affected by the environment, wear, and material aging, the efficiency and reliability of data acquisition are reduced. A monitoring method based on digital image has the advantages of non-contact monitoring, high precision, and strong reliability. The existing DIC detection methods are limited by processor performance and image resolution, which are difficult to apply to engineering detection. In this paper, a structural displacement identification method based on convolutional neural networks (CNNs) and DIC technology is proposed. In this method, the data set is formed according to the column displacement cloud image obtained by DIC analysis, and the data set is enhanced by data normalization and region division. Through the analysis of the number of network layers and learning rate, the model design of the deep learning network is carried out. The high-speed camera image results of the test are introduced and identified by the static loading test of the equal-scale sound barrier. The results show that the structural displacement identification method based on CNN and DIC technology can accurately identify the displacement change in the structure, which greatly improves the efficiency of image displacement calculation using DIC technology. [ABSTRACT FROM AUTHOR]

Understanding rock hardness on extraterrestrial planets offers valuable insights into planetary geological evolution. Rock hardness correlates with morphological parameters, which can be extracted from navigation images, bypassing the time and cost of rock sampling and return. This research proposes a machine-learning approach to predict extraterrestrial rock hardness using morphological features. A custom dataset of 1496 rock images, including granite, limestone, basalt, and sandstone, was created. Ten features, such as roundness, elongation, convexity, and Lab color values, were extracted for prediction. A foundational model combining Random Forest (RF) and Support Vector Regression (SVR) was trained through cross-validation. The output of this model was used as the input for a meta-model, undergoing linear fitting to predict Mohs hardness, forming the Meta-Random Forest and Support Vector Regression (MRFSVR) model. The model achieved an R2 of 0.8219, an MSE of 0.2514, and a mean absolute error of 0.2431 during validation. Meteorite samples were used to validate the MRFSVR model's predictions. The model is used to predict the hardness distribution of extraterrestrial rocks using images from the Tianwen-1 Mars Rover Navigation and Terrain Camera (NaTeCam) and a simulated lunar rock dataset from an open-source website. The results demonstrate the method's potential for enhancing extraterrestrial exploration. [ABSTRACT FROM AUTHOR]

With the tremendous technological progress in many fields, especially in communications, and to protect the information transmitted through communication channels, especially digital images, researchers in this field try to find new methods that provide a high level of security. Combining chaotic maps and DNA encryption provides a high level of security because of their high randomness, complexity, and sensitivity to initial conditions. This study presents a novel technique for protecting images by encrypting their data at three levels. The first level involves redistributing the image points. In contrast, the second level combines a 1D chaotic map (PWLCM) and DNA sequences. In the third level, three keys are produced using three 1D chaotic maps (Logistic maps, Tent maps, and Sine maps). Each key encrypts data of a specific color. The results of the statistical tests showed that the suggested technique provides a good security level compared to the methods, achieving an average Number of Pixels Change Rate (NPCR) of 99.62, a Unified Average Changing Intensity (UACI) of 32.83, and an entropy of 7.9972. [ABSTRACT FROM AUTHOR]

Digital Image Forgery (DIF) detection involves identifying instances where a portion of image is copied and placed in different areas within same image to create a seemingly authentic but altered version. However, the detection of small duplicated regions is challenging, especially when noise is present in the image. This issue becomes more significant when the model is trained on noisy data as it negatively affects its accuracy. This research proposes an Elite Opposition-based Learning with Black Widow Spider Optimization (EBWSO) for feature selection and Cyclic Symmetry Convolutional Neural Network (CSCNN) for detection to enhance accuracy in image forgery detection. Pre-processing techniques such as Single Image Super-Resolution (SISR) and Histogram Equalization (HE) are used for image enhancement. The VGG16 and ResNet50 are used for feature extraction in digital images, done through identifying the key features such as edges and shapes. The EBWSO technique is utilized for feature selection by updating the relevant features and balancing exploration and exploitation. Detection is carried out using the CSCNN technique, which is designed to be rotation-invariant and aims to enhance detection accuracy. The EBWSO-CSCNN model accurately classifies image forgery when compared to the existing techniques such as Stacked Sparse Denoising Autoencoder (SSDAE) and Simple Linear Iterative Cluster (SLIC) algorithm. The proposed method achieves a better accuracy of 99.15% on MICC-F220, 98.10% on MICC-F600, 99.25% on MICC-F2000, and 98.95% on the CASIA 2.0 dataset. [ABSTRACT FROM AUTHOR]

Digital image source identification primarily focuses on analyzing and detecting the machine imprints or camera fingerprints left by imaging devices during the imaging process to trace the origin of digital images. The development of a swift search algorithm is crucial for the effective implementation of camera source identification. Despite its importance, this domain has witnessed limited research, with existing studies predominantly focusing on search efficiency while neglecting robustness, which is essential. In practical scenarios, query images often suffer from poor signal quality due to noise, and the variability in fingerprint quality across different sources presents a significant challenge. Conventional brute-force search algorithms (BFSAs) prove largely ineffective under these conditions because they lack the necessary robustness. This paper addresses the issues in digital image source identification by proposing a rapid fingerprint search algorithm based on global information. The algorithm innovatively introduces a search priority queue (SPQ), which analyzes the global correlation between the query fingerprint and all reference fingerprints in the database to construct a comprehensive priority ranking, thereby achieving the efficient retrieval of matching fingerprints. Compared to the traditional brute-force search algorithm (BFSA), our method significantly reduces computational complexity in large-scale databases, optimizing from O (n N) to O (n log N) , where n is the length of the fingerprint, and N is the number of fingerprints in the database. Additionally, the algorithm demonstrates strong robustness to noise, maintaining a high matching accuracy rate even when image quality is poor and noise interference is significant. Experimental results show that in a database containing fingerprints from 70 cameras, our algorithm is 50% faster in average search time than BFSA, and its matching accuracy rate exceeds 90% under various noise levels. This method not only improves the efficiency and accuracy of digital image source identification but also provides strong technical support for handling large-scale image data, with broad application prospects in fields such as copyright protection and forensic evidence. [ABSTRACT FROM AUTHOR]

In the era of widespread digital image sharing on social media platforms, deep-learning-based watermarking has shown great potential in copyright protection. To address the fundamental trade-off between the visual quality of the watermarked image and the robustness of watermark extraction, we explore the role of structural features and propose a novel edge-aware watermarking framework. Our primary innovation lies in the edge-aware secret hiding module (EASHM), which achieves adaptive watermark embedding by aligning watermarks with image structural features. To realize this, the EASHM leverages knowledge distillation from an edge detection teacher and employs a dual-task encoder that simultaneously performs edge detection and watermark embedding through maximal parameter sharing. The framework is further equipped with a social network noise simulator (SNNS) and a secret recovery module (SRM) to enhance robustness against common image noise attacks. Extensive experiments on three public datasets demonstrate that our framework achieves superior watermark imperceptibility, with PSNR and SSIM values exceeding 40.82 dB and 0.9867, respectively, while maintaining an over 99% decoding accuracy under various noise attacks, outperforming existing methods by significant margins. [ABSTRACT FROM AUTHOR]

The document discusses a legal case involving copyright infringement, performers' rights infringement, and unfair competition between two companies, Sihai Company and Mofa Company, regarding virtual digital human content. The court ruled that virtual digital humans are not entitled to copyright or neighbouring rights, but content related to them may be protected. Sihai Company's unauthorized use of Mofa Company's virtual digital human content was deemed infringing, constituting false advertising and unfair competition. The court upheld the original judgment, awarding Mofa Company compensation for economic losses and reasonable expenses for rights protection. [Extracted from the article]

Image based screening is now routinely available for early detection of cancer and other diseases. Quantitative analysis for effects of risk factors on digital images is important to extract biological insights for modifiable factors in prevention studies and understand pathways for targets in preventive drugs. However, current approaches are restricted to summary measures within the image with the assumption that all relevant features needed to characterize an image can be identified and appropriately quantified. Motivated by data challenges in breast cancer, we propose a nonparametric statistical framework for risk factor screening that uses the whole mammogram image as outcome. The proposed permutation test allows assessment of whether a set of scalar risk factors is associated with the whole image in the presence of correlated residuals across the spatial domain. We provide extensive simulation studies and illustrate an application to the Joanne Knight Breast Health Cohort using the mammogram imaging data. [ABSTRACT FROM AUTHOR]

ABSTRACT Color palette is a critical component of art, design, and lots of applications, providing the basis for organizing and utilizing colors to achieve specific objectives. However, generating color palettes from digital images presents unique challenges due to the complexity of colors in images. This review comprehensively investigates various techniques for generating color palettes from digital images and provides a thorough classification and discussion of these techniques from multiple perspectives. A color space must be selected to generate a color palette, and a generation method must be employed. This paper offers a concise overview of color spaces, an introduction to current palette generation methods, and an analysis of the metrics used to evaluate color differences between palettes. The review encompasses traditional manual methods and computer‐aided automation methods, further categorized as histogram‐based, clustering‐based, and neural network‐based methods. Discussion on the strengths, weaknesses, and applicability of existing methods are presented, and also opportunities for future research to enhance color palette generation from digital images are identified. [ABSTRACT FROM AUTHOR]

Digital pathology using whole-slide imaging (WSI) scanners aids pathologists challenged by diagnostic volume and novel diagnostic methods. Colour reproducibility of WSI scanners is crucial for accurate digital diagnoses. We propose an objective and quantitative method for evaluating colour reproduction in digital images of pathological specimens from two perspectives: true colour value reproduction and colour discrimination. We define 57 critical features for evaluation, extract tissue structures, and establish diagnostic criteria. Our method is unique and useful for clinical use. We tested a prototype scanner, and it satisfied all criteria for true colour value reproduction (all the colour samples corresponding to the selected tissue evaluation points were experimentally within the acceptable range) and colour discrimination (the colour difference ΔE2000 calculated from the corresponding colour sample combinations exceeded the lower permissible limit for the selected discrimination points). This evaluation method and WSI scanner can contribute to accurate colour reproduction in digital pathology. [ABSTRACT FROM AUTHOR]

Background and Objectives: The present study aims to assess and compare the accuracy of post-space impressions captured by three different intraoral scanners (IOS) using various canal diameters. Methods: Three extracted natural maxillary central incisors were selected and prepared for a 1 mm wide margin and a 3 mm ferrule. All steps required for the endodontic procedure were performed, and the post space was prepared using post drills. The post length was kept constant at 12 mm, whereas the width was varied (Group 1: 1.4 mm, Group 2: 1.6 mm, and Group 3: 1.8 mm). Three IOSs (Trios3, iTero2, and Medit i700) were used to acquire a digital impression of the prepared post space. Each tooth was scanned 10 times by each scanner. So, in the end, 90 digital images were recorded, and the STL files were stored. GC Pattern resin was used to fabricate resin post and core patterns, which were scanned using an extraoral scanner (EOS). The STL file obtained was used as the reference file. To evaluate the trueness of the tested IOSs, each three-dimensional scan from an IOS was superimposed on the reference scan with the help of the Medit Design software 2.1.4. The software generates color plots and gives numerical values as deviations in the Root mean square (RMS) for the variance between the two superimposed scans. The data collected was tabulated for statistical analysis. One Way ANOVA was used to test the significance difference between three different IOSs, followed by Bonferroni Post-hoc test pairwise test to identify the differences between every two different IOS. Statistical significance was set at p < 0.05. Results: The mean deviation for trueness in post space impression values recorded by the Medit i700 was highest among groups 1, 2, and 3 [0.825 (±0.071), 0.673 (±0.042) and 0.516 (±0.039), respectively], followed by iTero2 [0.738 (±0.081), 0.569 (±0.043) and 0.470 (±0.037), respectively] and Trios3 [0.714 (±0.062), 0.530 (±0.040) and 0.418 (±0.024), respectively]. Significant differences were found between the groups for all three IOSs (Trios3: p-value < 0.0001; iTero2: p-value < 0.0001; Medit i700: p-value < 0.0001). Conclusions: Within the limitations of this study, it can be concluded that Trios3 IOS has higher accuracy (as it exhibited minimal deviation for trueness) in recording post space, followed by iTero2 and Mediti700 IOS. As the diameter of the post space is increased, the accuracy of recording by IOS increases. For all the tested IOSs (except for Trios3 and iTero2, when used to record post space with 1.8 mm canal diameter), the deviations in trueness were higher than the clinically acceptable limits. Thus, IOSs should be used cautiously when recording impressions of post spaces. [ABSTRACT FROM AUTHOR]

Chaos-based encryption is promising for safeguarding digital images. Nonetheless, existing chaos-based encryption algorithms still exhibit certain shortcomings. Given this, we propose a novel multi-channel image encryption algorithm that leverages pixel reorganization and hyperchaotic maps (MIEA-PRHM). Our MIEA-PRHM algorithm employs two hyperchaotic maps to jointly generate chaotic sequences, ensuring a larger key space and better randomness. During the encryption process, we first convert input images into two fused matrices through pixel reorganization. Then, we apply two rounds of scrambling and diffusion operations, coupled with one round of substitution operations, to the high 4-bit matrix. For the low 4-bit matrix, we conduct one round of substitution and diffusion operations. Extensive experiments and comparisons demonstrate that MIEA-PRHM outperforms many recent encryption algorithms in various aspects, especially in encryption efficiency. [ABSTRACT FROM AUTHOR]

Image segmentation is the process of splitting a digital image into distinct segments or categories based on shared characteristics like texture, color, and intensity. Its primary aim is to simplify the image for easier analysis while preserving its important features. Each pixel in the image is assigned a label, grouped together by pixels with similar traits together. Segmentation helps to delineate boundaries and identify objects such as curves or lines within the image. The process generates a series of segmented images that cover the entire original image. This article reviews emerging applications of image segmentation in medical diagnostics, specifically employing nature-inspired optimization algorithms (NIOAs). It begins by outlining different segmentation methods and NIOAs types, then by examining relevant databases and medical imaging technologies. The study draws on a diverse range of research sources. Finally, this paper briefly discusses the challenges and future trends of medical image segmentation using NIOAs to detect different diseases. [ABSTRACT FROM AUTHOR]

Physical unclonable function labels have emerged as a promising candidate for achieving unbreakable anticounterfeiting. Despite their significant progress, two challenges for developing practical physical unclonable function systems remain, namely 1) fairly few high-dimensional encoded labels with excellent material properties, and 2) existing authentication methods with poor noise tolerance or inapplicability to unseen labels. Herein, we employ the linear polarization modulation of randomly distributed fluorescent nanodiamonds to demonstrate, for the first time, three-dimensional encoding for diamond-based labels. Briefly, our three-dimensional encoding scheme provides digitized images with an encoding capacity of 109771 and high distinguishability under a short readout time of 7.5 s. The high photostability and inertness of fluorescent nanodiamonds endow our labels with high reproducibility and long-term stability. To address the second challenge, we employ a deep metric learning algorithm to develop an authentication methodology that computes the similarity of deep features of digitized images, exhibiting a better noise tolerance than the classical point-by-point comparison method. Meanwhile, it overcomes the key limitation of existing artificial intelligence-driven classification-based methods, i.e., inapplicability to unseen labels. Considering the high performance of both fluorescent nanodiamonds labels and deep metric learning authentication, our work provides the basis for developing practical physical unclonable function anticounterfeiting systems. The authors use polarized fluorescent nanodiamonds to achieve high-dimensional encoding for physical unclonable functions, and propose a deep metric learning based approach for robust authentication of unseen labels. [ABSTRACT FROM AUTHOR]

OBJECTIVE To review data on the advances in equlne ophthalmic imaging that have been made during the past 5 years and highlight advantages of using multiple imaging modalities to improve clinical observational skills and improve diagnostic accuracy. METHODS A literature review from 2019 through 2024 of equine ophthalmic digital photography, fundus photography, ocular and orbital ultrasonography (US), ultrasound biomicroscopy (UBM), confocal microscopy (CM), spectral domain optical coherence tomography (SD-OCT), radiography, CT, and MRI. RESULTS Digital photography remains the cornerstone of equirle ophthalmic imaging for documenting examination findings, sharing information with colleagues, and consulting with specialists. Digital images also allow for in-depth postexamination review and evaluation, often revealing subtleties that may have otherwise gone undetected during the ophthalmic examination. Advanced imaging modalities are being used more frequently in equine ophthalmology, especially those that can be used with the horses standing under sedation, including US, UBM, SD-OCT, CM, and CT. DISCUSSION Advances in equine ophthalmic imaging have led to many new clinical discoveries and to an increase in our knowledge of ocular anatomy and diseases in the horse. Many of these advanced diagnostic imaging modalities, such as MRI, CT, SD-OCT, and CM, are cost prohibitive and require substantial operator training to ensure proficiency. However, their availability in tertiary referral centers, such as veterinary teaching hospitals and large equine clinics/practices, is becoming more widespread. CONCLUSIONS Advanced equine ophthalmic imaging data contributes substantially to our general understanding of clinical and applied anatomy and improves our understanding of the underlying pathogenesis associated with specific diseases. [ABSTRACT FROM AUTHOR]