Digital Image Research Articles

Enhancing accessibility and discoverability of digital archive images through automated image recognition tool

This research paper presents a comprehensive evaluation of the effectiveness of Imagga and Google cloud vision application programming interface (API) as image recognition tools for generating metadata in digital archive images. The assessment encompasses a diverse range of archive images, including those without text, images with text, and both color and black-and-white images. Through the use of evaluation metrics such as cosine similarity, word overlap similarity, recall, precision, and F1 score, the performance of these tools is quantitatively measured. The findings highlight the strong individual performance of both Imagga and Google cloud vision API, with the combined metadata outputs achieving significantly higher scores across all metrics. This emphasizes the potential benefits of employing a combined approach, leveraging the strengths of multiple tools to enhance the reliability and robustness of the metadata extraction process. The findings contribute to the advancement of metadata management in digital archives and underscore the importance of utilizing multiple tools for improved performance in image metadata generation.

PSDCLS: Parallel simultaneous diffusion–confusion image cryptosystem based on Latin square

Today, due to the unparalleled growth of multimedia data sharing, especially digital images, between users over insecure channels in real-time applications, cryptography algorithms have gained increasing attention for the secure and efficient transmission. In classical chaos-based image cryptosystems, the confusion and diffusion operations are often applied as two separate and independent phases, which threatens the cryptosystem security. To address these problems, in this paper, a fast image cryptosystem based on parallel simultaneous diffusion–confusion strategy has been proposed using Latin squares, called PSDCLS. It consists of three main steps. First, the initial parameters of the Hénon-Sine chaotic map are produced from SHA256 of both the plain image content and the user’s secret key. Second, a chaos-based random Latin square is constructed by employing the chaotic sequence produced through the Hénon-Sine chaotic map. Third, a parallel simultaneous diffusion–confusion scheme is proposed by using Latin square and vectorization technique to overcome the problems of computational complexity and high risk of separable and iterative confusion–diffusion operations in the classical chaos-based image cryptosystems. To analyze and evaluate the security and performance of PSDCLS cryptosystem, we conducted extensive simulations and experiments on various benchmark images. Experimental results and analyses show that PSDCLS achieves excellent scores for information entropy (>7.99), correlation coefficients close to 0, key space (2512), NPCR (>99.60%), UACI (>33.46%). The encryption time for test images of size 512 × 512 and 512×512×3 was around 0.026 and 0.081 s, respectively. Therefore, PSDCLS is highly robust against common cryptographic attacks and serves as a swift cryptosystem for real-time encryption applications. The source code of PSDCLS is accessible at: https://github.com/EbrahimZarei64/PSDCLS.

Ultra-Efficient Low-Power Retinal Nano Electronic Circuit for Edge Enhancement and Detection Using 7 nm FinFET Technology

Ultra-Efficient Low-Power Retinal Nano Electronic Circuit for Edge Enhancement and Detection Using 7 nm FinFET Technology

Spatial domain noise removal filtering for low-resolution digital images

<p>n this research work, six different filters are applied on a low resolution 8 b/pixel gray-scale images, which operate on small sub-images (windows of 3×3 to 11×11 pixels). The enhanced images are used to compare the efficiency of the different six filters using the peak signal to noise ratio (PSNR) image quality measure. Noise peak elimination filter (PSNR)=36.63) outperforms others, such as median filter (PSNR=36.61), while corruption estimation (PSNR=36.03) significantly cuts processing time by only processing the corrupted pixels while maintaining image details. Mean filter (PSNR=34.05) is sensitive to outliers, which cause the image's sharpness and fine features to be lost. By avoiding averaging across edges, bimodal-averaging filter (PSNR=35.30), which improves on the mean filter, chooses the mean of the biggest population. The median-mean filtering (PSNR=36.32), which combines median and mean filters and determines the output pixel by averaging the median and some nearby pixels, is another improvement above averaging.</p>

Synergistic effects of Monel 400 filler wire in gas metal arc welding of CoCrFeMnNi high entropy alloy

Weldability plays a crucial role in the journey of high entropy alloys towards their engineering applications. In this study, gas metal arc welding was performed to join an as-rolled CoCrFeMnNi high entropy alloy using Monel 400 as the filler wire. The present research findings demonstrate a favorable metallurgical chemical reaction between the Monel 400 filler and the CoCrFeMnNi high entropy alloy, resulting in compositional mixing within the fusion zone that promotes a solid-solution strengthening effect, counteracting the typical low hardness associated to the fusion zone of these alloys. The weld thermal cycle induced multiple microstructure changes across the joint, including variations in the grain size, existing phases and local texture. The grain size was seen to increase from the base material toward the fusion zone. An FCC matrix and finely sparse Cr-Mn-based oxides existed in both base material and heat affected zone, while in the fusion zone new FCC phases and carbides were formed upon the mixing of the Monel 400 filler. The role of the filler material on the fusion zone microstructure evolution was rationalized using thermodynamic calculations. Texture shifted from a γ-fiber (in the base material) to a strong cubic texture in the fusion zone. Digital image correlation during tensile testing to fracture coupled with microhardness mapping revealed that, stemming from the process-induced microstructure changes, the micro and macromechanical response differed significantly from the original base material. This study successfully established a correlation between the impact of the process on the developed microstructural features and the resultant mechanical behavior, effectively assessing the processing-microstructure-properties relationships towards an improved understanding of the physical metallurgy associated to these advanced engineering alloys. In conclusion, this work provides an important theoretical framework and practical guidance for optimizing the engineering applications of high entropy alloys.

Vibration analysis of a partially submerged structure using the DIC technique

The continuous expansion of offshore wind turbine sites has been garnering increasing attention, particularly in countries where available land is limited. Nevertheless, efficiently allocating resources for the maintenance and repair of offshore wind turbines presents a formidable challenge. This challenge is compounded by the complex and often unpredictable weather conditions, as well as various business-related issues specific to the offshore environment. To tackle these challenges, one effective solution is to implement vibration analysis through the use of digital image correlation (DIC) methods. This approach has a well-established theoretic background dating back to approximately 2008 and has demonstrated its effectiveness in the realm of structural health monitoring for large structures like bridges and towers. Relevant information on the upper structure of the wind turbines has been obtained using numerical models in our previous work. Changes in the structural feature frequencies of wind turbines are the primary focus in vibration analysis. However, changes in feature frequencies can occur for various reasons, and relying solely on frequency shifts can make it challenging to pinpoint the precise areas of damage within wind turbine structures, especially without the benefit of long-term observations. In this study, the DIC method is employed on scaled cylindrical models to gain insights into the frequency variations stemming from the erosion of non-cohesive soils beneath offshore wind turbine foundations. Changes in modal curvature within the upper structure of the wind turbine will also be analyzed. This additional investigation will enable a clear differentiation between defects in the upper structure and frequency shifts resulting from erosion or problems with the underwater foundation.

Tensile failure of open-hole composite laminates with yarn gaps: Experimental and numerical study

The tensile mechanical behavior and failure mechanism of multidirectional carbon-fiber-fabric-reinforced open-hole composite laminates were investigated in this study. Using digital image correlation (DIC) technology, test specimens were subjected to quasi-static tensile tests to determine their macroscale mechanical properties. An analytical framework was developed to predict the tensile properties, damage evolution, and failure mechanisms of the open-hole composite laminates. Two types of finite element (FE) models: mesoscale model and macroscale-equivalent model, were established. The mesoscale model considers the coupling effect of the yarn gaps and central circular hole. An Abaqus subroutine was written to introduce the Hashin failure criterion, considering the stiffness degradation. The fracture morphology was examined by scanning electron microscopy (SEM) to determine the failure mechanism of the specimens. The results show that the average ultimate load of the specimens is 27.2 kN. The errors predicted by the macroscale-equivalent and mesoscale models are 5.5 % and 1.5 %, respectively. The mesoscale FE model accurately simulated the damage evolution process of each layer under the coupling effect of the yarn gaps and central circular hole. The simulation results and SEM images showed the different failure modes of the fiber layers at different angles.

Non-destructive Machine Vision System based Rice Classification using Ensemble Machine Learning Algorithms

Aims and Background: Agriculture plays a major role in the global economy, providing food, raw materials, and jobs to billions of people and driving economic growth and poverty reduction. Rice is the most widely consumed crop domestically, making it a particularly important crop for rural populations. The exact number of rice varieties worldwide is difficult to determine as new varieties are constantly being developed and marketed. Objectives: The most common method of rice variety identification is a comparison of its physical and chemical properties to a reference collection of known types. Methodology: This is a relatively quick and cost-effective approach that can be used to accurately differentiate between distinct varieties. In some cases, genetic testing may be used to confirm the identity of a variety, although this technique is more expensive and time-consuming. However, we can also utilize efficient, precise, and cost-effective digital image processing and machine vision techniques. Results: This study describes different types of ensemble methods, such as bagging (Decision Tree, Random Forest, Extra Tree), boosting (AdaBoost, Gradient Boost, and XGBoost), and voting classifiers to classify five different varieties of rice. Extreme Gradient Boosting (XGBoost) has achieved the highest average classification accuracy of 99.60% among all the algorithms. Conclusion: The findings of the performance measurement indicated that the proposed model was successful in classifying the various varieties of rice.

Local Fourier features for handwriting digit images classification

Multiple choice questions (MCQ) are effective in normative assessment and offline testing is still relevant due to the lack of efficient mass infrastructures and maintenance. For the automatic correction of MCQ paper form and reporting of the grade, it is generally necessary to read and recognize a handwriting digit in a box. This paper focuses on local feature extraction in the frequency domain using Fourier transform. The pre-process begins with the extraction of the fields from the entity map, followed by the application of 2D fast Fourier transform (2DFFT) and the reduction of computed coefficients to obtain the corresponding final local characteristic in the representation. The experimental results of the Modified National Institute of Standards and Technology (MNIST) handwriting digits dataset show that the local characteristics extracted in the frequency domain used as input to a support vector machine (SVM) classifier are efficient in terms of 99.51% accuracy. The proposed system successfully helped in the reporting of all the marks for seven subjects in a class of 98 students during the automatic correction of the MCQ exam papers.

Image Steganography

Abstract: Steganography is the art of hiding the fact that communication is taking place, by hiding by information in other information. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. For hiding secret information in images, there exists a large variety of stenographic techniques some are more complex than others and all of them have respective strong and weak points. This project intends to give an overview of image steganography, its uses and techniques. It also supports steganography in Audio files. For a more secure approach, the project encrypts the message using secret key and then sends it to the receiver. The receiver then decrypts the message

Global Copyright Consortium: Empowering Decentralisation with Authority - Backed Blockchain

Abstract: Digital images are transferred with ease through the network. Many users are using the images without the knowledge of the owners. Zero watermarking does not alter the original information contained in vector map data and provides perfect imperceptibility. The use of zero watermarking for data copyright protection has become a significant trend in digital watermarking research. However, zero watermarking encounters tremendous obstacles to its development and application because of its requirement to store copyright information with a third party and its difficulty in confirming copyright ownership. However, traditional digital image generation methods have high operational requirements for designers due to difficulties in collecting data sets and simulating environmental scenes, which results in poor quality, lack of diversity, and long generation speed of generated images, making it difficult to meet the current needs of image generation.

Kidney Stone Detection and Stone Analysis Using Image Processing

Abstract: The primary goal of the project is to detect the kidney stone from the digital ultrasound image of the kidney by performing various image processing techniques. Due to the varied texture and existence of speckle noise, detecting regions of interest in ultrasound pictures is a difficult process. Ultrasound scanning is the most common method of examining a patient for the presence of kidney stones. We created an application that aids the medical practitioner in selecting the region to be evaluated can also referred to as renal calculi using the suggested technology. The feature extraction is done on cropped portions that may have stones in them. The KNN classifier is accustomed to categories images based on training data.

Adversarial Learning for Constrained Image Splicing Detection and Localization

Abstract: In the era of digital media, the manipulation of images has become a significant concern, particularly on social media platforms. Adversarial Learning for Constrained Image Splicing Detection and Localization aims to develop a robust system capable of recognizing and localizing spliced or tampered images. This research proposes a novel approach that leverages Convolutional Neural Networks (CNNs) and adversarial learningtechniques to enhance the detection and localization of image splicing. The system is designed to aid in distinguishing between authentic and manipulated images, thereby promoting transparency and trust in digital media. With the rapid advancement of digital media and image editingtools, the manipulation of visual content has become increasingly prevalent, posing significant challenges to the credibility and trustworthiness of images shared on social media platforms. One pervasive form of image manipulation is image splicing, where portions of two or more images are seamlessly combined to create a composite image, often with the intent to mislead or deceive viewers. Adversarial Learning for Constrained Image Splicing Detection and Localization aims to address this issue by developing a robust system capable of accurately detecting and localizing spliced regions within tampered images. This research proposes a novel approach that synergistically integrates Convolutional Neural Networks (CNNs) and adversarial learning techniques to enhance the performance of image splicing detection and localization. The proposed system employs a two-stage process: first, a CNN-based classifier is trained on a large dataset of authentic and spliced images to learn discriminative features for distinguishing between the two classes; subsequently, an adversarial learning model is employed to generate adversarial examples that can deceive the CNN classifier, while the classifier is iteratively updated to become more robust against these adversarial perturbations. By accurately identifying and localizing spliced regions within images, this research contributes to promoting transparency and trust in digital media shared on social media platforms, ultimately empowering users to make informed decisions about the authenticity of visual content. The proposed system has potential applications in various domains, including journalism, law enforcement, and content moderation, where verifying the integrity of visual evidence is of paramount importance.

Comparative Analysis of Traditional Methods and Building Information Modeling (BIM) in Construction Project Cost Estimation

Abstract: Building Information Modeling (BIM) has revolutionized the construction industry by providing digital images of the physical and functional aspects of the site. This article explores the use of BIM in project management and compares it to traditional methods. The study focused on parameters such as efficiency, cost, accuracy, and coordination. Research shows that BIM increases efficiency, accuracy, and coordination while reducing costs and risks. However, challenges remain, such as developing regulations and reaching consensus on requirements among stakeholders. Despite these challenges, the article concludes that BIM has the potential to transform project management.

FashionMorph: Contextually Adaptive Clothing Replacement with CLIP, Segmentation, and Stable Diffusion

Abstract: FashionMorph represents a groundbreaking advancement in digital image manipulation, seamlessly blending advanced segmentation techniques with the language comprehension capabilities of CLIP. This innovative approach simplifies the process of precise garment replacements while ensuring the preservation of realistic textures and boundaries. By incorporating stable diffusion, FashionMorph eliminates the need for extensive training on specific clothing styles, offering users a streamlined experience in expressing their preferences through natural language prompts. This empowers users in photo editing, granting them unparalleled creative freedom. The system comprises a comprehensive pipeline designed for the identification and inpainting of clothing elements. Leveraging CLIP and CLIPSeg models for robust image analysis, FashionMorph accurately identifies clothing elements and generates corresponding masks. The inpainting process utilizes a stable diffusion pipeline, resulting in impressive image quality that surpasses the performance of notable models like DCTransformer, StyleGAN, and ADM. Evaluations based on FID and SFID scores, along with Inception Score assessments, further validate FashionMorph's effectiveness in context-aware garment editing. Visual representations of original images, inpainted results, and masks vividly demonstrate the successful outcomes achieved by FashionMorph, establishing it as an efficient and user-friendly solution for professionals and enthusiasts alike. Among the evaluated models, Stable Diffusion emerges as the standout performer, boasting remarkable FID and SFID scores of 0.35 and 0.20, respectively, indicative of its outstanding image fidelity and diversity. Conversely, DCTransformer exhibits the highest FID and SFID scores, while StyleGAN excels in diversity but falls short in FID. ADM (dropout) strikes a balance between fidelity and diversity. These results underscore the exceptional performance of Stable Diffusion, solidifying its position as the leading approach with the lowest FID and SFID values

Botanic Shield: A Deep Learning Solution for Early Detection of Plant Disease

Abstract: Plant diseases threaten both food security and the botanical system of the environment which canlead to low- grade food quality and life style. Early detection of plants diseases is the key feature in plantproduction which increase the better growth of plant, results in best yield and better food quality andquantity. The plant condition can be determined byusing the photos of the plant which will be in the formof jpg using digital image processing and the current emerging technology Deep Learning. DeepLearning has made breakthroughs in the field of digital image processing, far superior to traditional methods. The Digital image processing process happens through convolution neural network (CNN) and computer vision techniques, the image recognition technology based on deep learning does not need to extractspecificfeatures, and only through iterative learning can find appropriate features, which can acquire global andcontextual features of images, and has strong robustness and higher recognition accuracy. By inputting a test image into the classification network, the network analyses the input image and returns a label that classifiesthe image. The added feature is thatit also suggeststhe required pesticides after the detection

Transparent, flexible, and eco-friendly starch-based films for reversible optoelectronic noses for food spoilage monitoring in smart packaging.

Areversible optoelectronic nose is presentedconsisting of ten acid-base indicators incorporated into a starch-based film, covering a wide pHrange. The starch substrate is odorless, biocompatible, flexible, and exhibits high tensile resistance. This optical artificial olfaction system was used to detect the early stages of food decomposition by exposing it to the volatile compounds produced during the spoialge process of three food products (beef, chicken, and pork).A smartphone was used to capture the color changes caused by intermolecular interactions between each dye and the emitted volatiles over time. Digital images were processed to generate a differential color map, which uses the observed color shifts to create a unique signature for each food product. To effectively discriminate among different samples and exposure times, we employed chemometric tools, including hierarchical cluster analysis (HCA) and principal component analysis (PCA). This approach detects food deterioration in a practical, cost-effective, and user-friendly manner, making it suitable for smart packaging. Additionally, the use of starch-based films in the food industry is preferable due to their biocompatibility and biodegradability characteristics.

Deeper Insights into Mixed Crowding through Enzyme Activity, Dynamics, and Crowder Diffusion.

Given the fact that the cellular interior is crowded by many different kinds of macromolecules, it is important that in vitro studies be carried out in the presence of mixed crowder systems. In this regard, we have used binary crowders formed by the combination of some of the commonly used crowding agents, namely, Ficoll 70, Dextran 70, Dextran 40, and PEG 8000 (PEG 8), to study how these affect enzyme activity, dynamics, and crowder diffusion. The enzyme chosen is AK3L1, an isoform of adenylate kinase. To investigate its dynamics, we have carried out three single point mutations (A74C, A132C, and A209C) with the cysteine residues being labeled with a coumarin-based solvatochromic probe [CPM: (7-diethylamino-3-(4-maleimido-phenyl)-4-methylcoumarin)]. Both enzyme activity and dynamics decreased in the binary mixtures as compared with the sum of the individual crowders, suggesting a reduction in excluded volume (in the mixture). To gain deeper insights into the binary mixtures, fluorescence correlation spectroscopy studies were carried out using fluorescein isothiocyanate-labeled Dextran 70 and tetramethylrhodamine-labeled AK3L1 as the diffusion probes. Diffusion in binary mixtures was observed to be much more constrained (relative to the sum of the individual crowders) for the labeled enzyme as compared to the labeled crowder showing different environments being faced by the two species. This was further confirmed during imaging of the phase-separated droplets formed in the binary mixtures having PEG as one of the crowding agents. The interior of these droplets was found to be rich in crowders and densely packed, as shown by confocal and digital holographic microscopy images, with the enzymes predominantly residing outside these droplets, that is, in the relatively less crowded regions. Taken together, our data provide important insights into various aspects of the simplest form of mixed crowding, that is, composed of just two components, and also hint at the enhanced complexity that the cellular interior presents toward having a detailed and comprehensive understanding of the same.

Eccentrically compressed behavior of UHPC columns considering the P-delta effect

Eccentric compression is a critical behavior observed in arches and columns, particularly in structures constructed with ultra-high performance concrete (UHPC). Compared to normal concrete structures, those made with UHPC exhibit distinct behaviors that need to be clearly investigated. This study comprehensively investigates UHPC columns under eccentric compression load through experimental study and theoretical analysis. Various experimental parameters, including slenderness ratio, eccentricity distance, fiber content, and stirrup ratio, were examined across 26 UHPC columns. Failure modes were evaluated using digital image correlation (DIC) methods. The load-displacement responses indicated that slender columns subjected to significant eccentricity exhibited load decreases during yielding stages, whereas short columns experienced load increases. This discrepancy in yielding stages was attributed to P-delta effects. In addition, theoretical models were developed to determine the ultimate bearing capacity of columns experiencing compression-controlled and tension-controlled failures, considering the P-delta effect caused by the different slenderness ratios. The moment magnifying coefficients for UHPC were calibrated based on theoretical analyses of the limit curvature of eccentric compression members. The proposed theoretical models agreed well with the experimental results, with an average ratio of 1.02. The second-order effect for the case of UHPC short column with a slenderness ratio of 5 can be ignored as per Eurocode 2, which is less than 10 % of the corresponding first-order effect.

All-optical complex field imaging using diffractive processors.

Complex field imaging, which captures both the amplitude and phase information of input optical fields or objects, can offer rich structural insights into samples, such as their absorption and refractive index distributions. However, conventional image sensors are intensity-based and inherently lack the capability to directly measure the phase distribution of a field. This limitation can be overcome using interferometric or holographic methods, often supplemented by iterative phase retrieval algorithms, leading to a considerable increase in hardware complexity and computational demand. Here, we present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields using an intensity-based sensor array without any digital processing. Our design utilizes successive deep learning-optimized diffractive surfaces that are structured to collectively modulate the input complex field, forming two independent imaging channels that perform amplitude-to-amplitude and phase-to-intensity transformations between the input and output planes within a compact optical design, axially spanning ~100 wavelengths. The intensity distributions of the output fields at these two channels on the sensor plane directly correspond to the amplitude and quantitative phase profiles of the input complex field, eliminating the need for any digital image reconstruction algorithms. We experimentally validated the efficacy of our complex field diffractive imager designs through 3D-printed prototypes operating at the terahertz spectrum, with the output amplitude and phase channel images closely aligning with our numerical simulations. We envision that this complex field imager will have various applications in security, biomedical imaging, sensing and material science, among others.