Advanced Image Processing Techniques Research Articles

Fake Indian Currency Detection

The proliferation of counterfeit currency poses a significant threat to both individuals and the national economy. While existing fake currency detection tools are primarily accessible to banks and large enterprises, everyday people and small businesses remain susceptible. Thus, this project aims to delve into the security features of Indian currency and develop a software solution leveraging advanced image processing and computer vision techniques to detect and neutralize counterfeit notes. Counterfeiting currency poses a genuine menace to both the populace's well-being and the nation's economic stability. Although counterfeit currency detection tools exist, their accessibility is typically confined to banking institutions and corporate entities, leaving ordinary citizens and small enterprises susceptible to fraud. Thus, this project endeavours to examine the diverse security attributes of Indian currency and subsequently craft a software-driven apparatus capable of discerning and nullifying counterfeit Indian currency through sophisticated image processing and computer vision methodologies. Notably, this currency authentication system will be meticulously crafted using the Python programming language within the Jupyter Notebook framework.

Color-based discrimination of color hues in rock paintings through Gaussian mixture models: a case study from Chomache site (Chile)

The article explores advanced image processing techniques for pigment discrimination in rock art paintings, emphasizing color separation using RGB (red, green, blue) and LHCUv (Luminance, Hue, Chroma) imagery. It highlights the use of dimensionality reduction methods such as Principal Components Analisys PCA and Independent Component Analysis (ICA), with a focus on Gaussian Mixture Models (GMM) for probabilistic classification of image elements. This approach, applied to the Chomache archaeological site on the northernmost coast of the Atacama Desert in Chile, reveals previously undetected motifs and details, offering a nuanced perspective in rock art documentation and analysis. This proposal reinforces the value of rock art panel not only as a finished product but as a process.

HealthSquare: A Survey of Deep Learning Based Healthcare Platform

The multi-health diagnosis web application HealthSquare represents a pioneering endeavor in healthcare technology by integrating advanced AI models for comprehensive health analysis. The system comprises multiple components, including two image processing models for fracture detection from X-ray reports and brain tumor identification from MRI scans. Additionally, GCP’s Vertex AI is leveraged for blood & thyroid report analysis to predict & provide descriptive diagnosis. Moreover, the platform enables users to create accounts for secure storage of medical reports, akin to a specialized digital locker tailored specifically for healthcare documents. Furthermore, an AI-driven chat bot offers real-time health- related guidance and recommendations to users based on their queries. The integration of these components aims to redefine healthcare diagnostics by providing users with accessible, accurate, and personalized health analysis tools. Utilizing advanced image processing techniques, AI-driven analyses, and secure user accounts, the project strives to revolutionize healthcare by offering a comprehensive and user-centric approach to medical diagnosis and health management. Keywords: AI Integration, Fracture Detection, Brain Tumor Identification, Thyroid Detection, Blood Analysis, Secure Medical Report Storage, AI Chat Bot.

Fundus- Based Glaucoma Detection- Machine Learning A Design Approach

Glaucoma is a leading cause of irreversible blindness worldwide, characterized by the progressive degeneration of the optic nerve, often associated with increased intraocular pressure. This paper proposes a machine learning-based design approach for the detection of glaucoma using fundus images. Our approach leverages advanced image processing techniques and machine learning algorithms to identify glaucomatous features in fundus images. The system comprises several key components: pre-processing of fundus images to enhance quality, feature extraction to identify relevant glaucomatous markers, and classification using machine learning models to differentiate between healthy and glaucomatous eyes. The proposed model achieved high accuracy, sensitivity, and specificity in detecting glaucoma, demonstrating its potential as a reliable and non-invasive diagnostic tool. Our findings highlight the efficacy of machine learning in medical imaging applications and underscore the importance of automated systems in assisting ophthalmologists in early glaucoma detection

Verinote - Fake Currency Detection Using Convolutional Neural Network

The proliferation of counterfeit money poses a significant threat to financial systems and economies worldwide. To solve this problem, advanced technological solutions have emerged, such as the counterfeit money detection system “FCDS”. The system leverages advanced image processing, machine learning and data analysis techniques to identify counterfeit bills accurately and effectively [1]. FCDS works by analyzing various security features found on legal tender, including watermarks, security chains, holograms and microprinting. Using image recognition and pattern analysis, the system distinguishes between real and fake money. Machine learning algorithms play a central role in training systems to recognize the subtle nuances that counterfeiters try to reproduce. FCDS can be deployed in a variety of contexts, from banks and financial institutions to retail businesses, providing a robust and scalable solution to combat counterfeiting [1]. By quickly identifying fraudulent notes, it helps prevent economic loss and maintain the integrity of financial transactions. This summary describes the nature of counterfeit currency detection systems and its importance in maintaining financial security and confidence. Its integration into modern banking and commerce systems represents an important step towards a counterfeit-proof financial ecosystem.

COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier

This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images using a lean transfer learning-based model. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the uppermost and lowermost slices were removed, preserving sixty percent of each patient’s slices. Secondly, all slices underwent manual cropping to emphasize the lung areas. Subsequently, resized CT scans (224×224) were input into an Xception transfer learning model with a modified output. Both Xception’s architecture and pretrained weights were leveraged in the method. A big and rigorously annotated database of CT images was used to verify the method. The number of patients/subjects in the dataset is more than 5000, and the number and shape of the slices in each CT scan varies greatly. Verification was made both on the validation partition and on the test partition of unseen images. Results on the COV19-CT database showcased not only improvement from our previous solution and the baseline but also comparable performance to the highest-achieving methods on the same dataset. Further validation studies could explore the scalability and adaptability of the developed methodologies across diverse healthcare settings and patient populations. Additionally, investigating the integration of advanced image processing techniques, such as automated region of interest detection and segmentation algorithms, could enhance the efficiency and accuracy of COVID-19 diagnosis.

Enhancing Quality Control: Image-Based Quantification of Carbides and Defect Remediation in Binder Jetting Additive Manufacturing.

While binder jetting (BJ) additive manufacturing (AM) holds considerable promise for industrial applications, defects often compromise part quality. This study addresses these challenges by investigating binding mechanisms and analyzing common defects, proposing tailored solutions to mitigate them. Emphasizing defect identification for effective quality control in BJ-AM, this research offers strategies for in-process rectification and post-process evaluation to elevate part quality. It shows how to successfully process metallic parts with complex geometries while maintaining consistent material properties. Furthermore, the paper explores the microstructure of AISI M2 tool steel, utilizing advanced image processing techniques like digital image analysis and SEM images to evaluate carbide distribution. The results show that M2 tool steel has a high proportion of M6C carbides, with furnace-cooled samples ranging from ~2.4% to 7.1% and MC carbides from ~0.4% to 9.4%. M6C carbides ranged from ~2.6% to 3.8% in air-cooled samples, while water-cooled samples peaked at ~8.52%. Sintering conditions also affected shrinkage, with furnace-cooled samples showing the lowest rates (1.7 ± 0.4% to 5 ± 0.4%) and water-cooled samples showing the highest (2 ± 0.4% to 14.1 ± 0.4%). The study recommends real-time defect detection systems with autonomous corrective capabilities to improve the quality and performance of BJ-AM components.

Meso-mechanical behavior of crystalline rocks: An image-based discrete element method

This research examines the meso-mechanical behavior of rock materials under various loading conditions, with a focus on the influence of mesoscale heterogeneity in crystalline rock specimens. Traditional approaches often rely on a limited set of statistical parameters for constructing numerical models, failing to capture the complex mesoscale heterogeneities integral to rock mechanics. To overcome this limitation, the study utilizes an advanced image-processing technique to accurately identify mineralogical features within rock microstructures. The extracted mineralogical data were then integrated into a discrete element method (DEM) model to explore the mesoscale mechanical responses of crystalline rocks. The results of the simulations indicated that mesoscale heterogeneities play a critical role in stress concentration localization, which in turn dictates the direction of fracture propagation and leads to a sophisticated interaction among various fracture pathways.

Automated Detection and Classification of Rice Crop Diseases Using Advanced Image Processing and Machine Learning Techniques

Automated Detection and Classification of Rice Crop Diseases Using Advanced Image Processing and Machine Learning Techniques

Preparation and High-Temperature Resistance Properties of Phenolic Resin/Phosphate Hybrid Coatings.

In this study, a novel fabrication method was used to synthesize phenolic resin/phosphate hybrid coatings using aluminum dihydrogen phosphate (Al(H2PO4)3, hereafter denoted as Al), SC101 silica sol (Si) as the primary film-forming agent, and phenolic resin (PF) as the organic matrix. This approach culminated in the formation of Al+Si+PF organo-inorganic hybrid coatings. Fourier-transform infrared spectroscopy (FT-IR) and X-ray photoelectron spectroscopy (XPS) results confirmed the successful integration of hybrid structures within these coatings. The crystalline structure of the coatings post-cured at various temperatures was elucidated using X-ray diffraction (XRD). Additionally, the surface and cross-sectional morphologies were meticulously analyzed using scanning electron microscopy (SEM), offering insights into the microstructural properties of the coatings. The coatings' porosities under diverse thermal and temporal regimes were quantitatively evaluated using advanced image processing techniques, revealing a significant reduction in porosity to a minimum of 5.88% following a thermal oxidation process at 600 °C for 10 h. The antioxidant efficacy of the phosphate coatings was rigorously assessed through cyclic oxidation tests, which revealed their outstanding performance. Specifically, at 300 °C across 300 h of cyclic oxidation, the weight losses recorded for phosphate varnish and the phenolic resin-infused phosphate coatings were 0.15 mg·cm-2 and 0.09 mg·cm-2, respectively. Furthermore, at 600 °C and over an identical period, the weight reduction was noted as 0.21 mg·cm-2 for phosphate varnish and 0.085 mg·cm-2 for the hybrid coatings, thereby substantiating the superior antioxidation capabilities of the phenolic resin hybrid coatings in comparison to the pure phosphate varnish.

Integrated multispectral remote sensing approach for high-resolution spectral characterization and automated mapping of carbonate lithofacies

Field-based high-resolution carbonate facies mapping is often challenging due to the limited accessible exposure, high-degree of heterogeneity, and lack of distinct natural characteristics between different lithofacies. To mitigate this issue, we proposed a novel approach by integrating multispectral remote sensing, advanced image processing techniques, and supervised classification to perform high-resolution carbonate lithofacies mapping and utilized the extensive Mesozoic carbonate in Saudi Arabia as an example. For this study, the Tuwaiq Mountain Formation (TMF) was selected not only because of its wide aerial distribution but also its importance as conventional and unconventional hydrocarbon reservoirs in the subsurface. Our proposed method was able to map and delineate different members (T1, T2, T3) and key lithofacies in the TMF. In addition, based on the spectral characteristics, the middle member of TMF (T2) can be further subdivided into two subunits (T2-a of higher reflectance & T2-b of lower reflectance). These findings are further corroborated by detailed microfacies analysis, which validates the presence of two sub-members of T2 (T2-a: Spiculitic foraminiferal wackestone and T2-b: Coralline floatstone facies). This resulted in a revised and accurate lithofacies map that made significant modifications over older maps. The overall accuracy of TMF lithofacies is 93.4 % with a kappa coefficient of 0.88. This study demonstrates that multispectral remote sensing approach are effective at distinguishing different carbonate units and providing high-resolution carbonate facies maps. The proposed approach should be applicable to other carbonate outcrops globally and could help in improving carbonate lithofacies mapping where the outcrops are not accessible.

Advanced Image Processing Techniques for Medical Image Retrieval Using Visual Features and Distance Measures

Recently, there has been a rapid increase in the volume of medical image repositories because of the maximized utilization of digitized image data in hospitals. As a result, there is a complexity in querying and handling such large databases, which has led to the development of a novel Content-Based Medical Image Retrieval (CBMIR) technique. In this work, the CBMIR technique is developed using three types of visual features (i.e., color, texture and shape) along with 12 distance measures, and is optimized with the Crossover-Gravitational Search Algorithm (CRO-GSA). For ease, we named this technique Content-Based Medical Image Retrieval using Visual features with CRO-GSA (CBMIRVC). The initial step of our CBMIRVC technique is to extract three types of visual features from the images. Then, each type of feature is employed with a suitable distance measure, which is used for the computation of image similarities between the medical images in the database and a provided query image. The optimization of our proposed CBMIRVC technique is done by the CRO-GSA algorithm. This algorithm optimizes the CBMIRVC technique by identifying the optimal combinations of visual features and similarity measures. Additionally, optimal weights are computed in accordance with the three types of features for the three similarities. Experimental validations were done using retrieved cancer-related and endoscopic color images from databases holding numerous image categories. The experimental outcome shows that our proposed CBMIRVC technique outperforms other conventional image retrieval techniques by effectively retrieving similar images from large databases.

Investigation of fluid flow during flow boiling inside a horizontal rectangular channel with single-sided heating using particle image velocimetry

Subcooled flow boiling is a highly efficient cooling systems for thermal management systems. This study explores the intricate dynamics of subcooled flow boiling within a horizontal channel, investigating the impact of vapor generation on liquid-phase velocity using Particle Image Velocimetry (PIV) and advanced image processing techniques. Four mass flow rates ranging from 5–20 g/s with subcooled inlet conditions are investigated in a rectangular channel with single-sided heating. Three regions of interest along the heated channel are investigated for instantaneous PIV analysis. The PIV system captures detailed velocity profiles, illustrating the impact of varying mass flow rates and heat flux levels on flow behavior. Vapor masking techniques are introduced to enhance the precision of PIV data by mitigating interference from the vapor phase. Results demonstrate the influence of vapor bubbles on flow resistance, revealing non-uniform velocity distributions and turbulence near the liquid–vapor interface. The study emphasizes the critical role of inertia and buoyancy forces in shaping the velocity profiles. Moreover, the investigation sheds light on the effects of flow rates on the interfacial behaviors, hinting at a transition point between 10 and 15 g/s. In summary, this research contributes valuable insights into the nuanced dynamics of flow boiling, laying the foundation for future studies on turbulence, heat transfer, and phase-change phenomena in two-phase thermal management systems.

An Innovative Runway Landing Path Detection using UAV Implementation of the K-Means Clustering Algorithm

Objective: To provide a novel approach for automatic Unmanned Aerial Vehicle (UAV) runway detection, leveraging remote sensing data and advanced image processing techniques. Methods: The methodology encompasses Gaussian filter-based despeckling and histogram equalization for preprocessing, followed by Independent Component Analysis (ICA) for feature extraction and segmentation using the K-means clustering algorithm. Findings: The research demonstrates successful UAV runway detection, even with unlabeled datasets, underscoring the efficacy of the proposed methods. Notably, the study contributes to automatic target recognition, specifically in Synthetic Aperture Radar (SAR) data analysis, where K-means clustering outperforms Korn B and morphological algorithms. Novelty : The K-means algorithms works by clustering the datasets obtained by integrating all the data collected from various sensors that are placed at specific positions in the runway. This work holds significance in facilitating immediate runway identification during emergencies and finds applications in military operations, surveillance, and remote sensing domains. Keywords: Runway detection, Unmanned Aerial Vehicle, Histogram Equalization, Gaussian filtering, Independent Component Analysis, K-means clustering based segmentation

Review on Automatic Detection & Counting of RBC’s by Using Image Processing Technique

Accurate counting of red blood cells (RBCs) in microscopic images is critical for various medical diagnoses and research purposes. Manual counting methods are labor-intensive, time-consuming, and prone to human error. This project aims to automate the RBC counting process using advanced image processing techniques. The primary objective is to improve the efficiency of the counting process, minimize subjectivity, and increase productivity in medical laboratories. The proposed method uses computer vision algorithms to analyze digital images of blood smears captured under a microscope. A combination of image segmentation and morphological operations is then applied to isolate individual RBCs, overcoming challenges posed by overlapping cells and variations in staining. Keywords: Image processing; Segmentation; Red Blood cells; Hough Transformation; RBC count.

The Role of X-ray Images for Early Detection of Covid-19 using Machine Learning

The principal aim of the Covid Detection Using X-ray project is to develop a robust platform for accurate and efficient detection of COVID-19 through the analysis of X-ray scans. The proposed system aims to enhance the diagnostic process by leveraging artificial intelligence (AI) algorithms to identify specific patterns associated with COVID-19 in X-ray images. The platform will facilitate timely and precise diagnosis, enabling healthcare professionals to make informed decisions and optimize patient care. This project integrates advanced image processing techniques and machine learning algorithms to discern subtle patterns indicative of COVID-19 in X-ray scans. The system will provide a user-friendly interface for healthcare professionals, allowing seamless uploading and analysis of X-ray images. Additionally, the platform incorporates a comprehensive database of COVID-19-related patterns for accurate comparison and classification. The primary objective of the project is to contribute to the ongoing efforts in combating the COVID-19 pandemic by providing a reliable and efficient tool for early detection. The utilization of cutting-edge technologies, including deep learning and image analysis, ensures a high level of accuracy in identifying potential cases. The project aims to be a valuable resource for healthcare practitioners, aiding in the prompt and accurate diagnosis of COVID-19 through the analysis of X-ray imagery.

Artificial Intelligence in Medical Imaging: Analyzing the Performance of ChatGPT and Microsoft Bing in Scoliosis Detection and Cobb Angle Assessment.

Open-source artificial intelligence models (OSAIM) find free applications in various industries, including information technology and medicine. Their clinical potential, especially in supporting diagnosis and therapy, is the subject of increasingly intensive research. Due to the growing interest in artificial intelligence (AI) for diagnostic purposes, we conducted a study evaluating the capabilities of AI models, including ChatGPT and Microsoft Bing, in the diagnosis of single-curve scoliosis based on posturographic radiological images. Two independent neurosurgeons assessed the degree of spinal deformation, selecting 23 cases of severe single-curve scoliosis. Each posturographic image was separately implemented onto each of the mentioned platforms using a set of formulated questions, starting from 'What do you see in the image?' and ending with a request to determine the Cobb angle. In the responses, we focused on how these AI models identify and interpret spinal deformations and how accurately they recognize the direction and type of scoliosis as well as vertebral rotation. The Intraclass Correlation Coefficient (ICC) with a 'two-way' model was used to assess the consistency of Cobb angle measurements, and its confidence intervals were determined using the F test. Differences in Cobb angle measurements between human assessments and the AI ChatGPT model were analyzed using metrics such as RMSEA, MSE, MPE, MAE, RMSLE, and MAPE, allowing for a comprehensive assessment of AI model performance from various statistical perspectives. The ChatGPT model achieved 100% effectiveness in detecting scoliosis in X-ray images, while the Bing model did not detect any scoliosis. However, ChatGPT had limited effectiveness (43.5%) in assessing Cobb angles, showing significant inaccuracy and discrepancy compared to human assessments. This model also had limited accuracy in determining the direction of spinal curvature, classifying the type of scoliosis, and detecting vertebral rotation. Overall, although ChatGPT demonstrated potential in detecting scoliosis, its abilities in assessing Cobb angles and other parameters were limited and inconsistent with expert assessments. These results underscore the need for comprehensive improvement of AI algorithms, including broader training with diverse X-ray images and advanced image processing techniques, before they can be considered as auxiliary in diagnosing scoliosis by specialists.

Enhanced Diagnostics Based on Deep Learning for Varicose Vein Detection

Varicose veins, a prevalent vascular condition affecting a significant portion of the population, necessitate early detection for optimal intervention and improved patient outcomes. This study introduces a novel diagnostic approach for varicose vein detection, leveraging the capabilities of deep learning. Varicose veins represent a prevalent vascular condition that can lead to discomfort and complications if not identified and treated promptly. This research introduces an advanced diagnostic methodology for varicose vein detection, utilizing deep learning techniques. Our approach harnesses the capabilities of deep neural networks, specifically employing a convolution neural network (CNN) architecture. Trained on a diverse dataset of vascular images, the model demonstrates a high proficiency in identifying varicose veins by capturing nuanced patterns and subtle features. Fine-tuning through transfer learning ensures adaptability to variations in patient demographics and imaging conditions. To enhance diagnostic precision, our system incorporates advanced image processing techniques and feature extraction methods. The integration of image segmentation algorithms and anatomical feature analysis provides detailed insights into the extent and severity of varicose veins. Furthermore, the model is designed to accommodate multi-modal imaging data, incorporating information from various imaging modalities to enhance diagnostic accuracy. Validation of our approach is conducted on a comprehensive dataset encompassing a spectrum of varicose vein cases, including varying degrees of severity. The results underscore the superior performance of our deep learning-based diagnostic system, achieving notable sensitivity and specificity in varicose vein detection. This proposed enhanced diagnostic tool holds promise for healthcare professionals, streamlining the diagnostic process and facilitating early and accurate identification of varicose veins. The potential implications include improved patient outcomes through timely intervention and the development of personalized treatment strategies. As we continue refining and expanding our model, its integration into clinical practice could contribute to a more efficient and effective management of varicose vein disorders.

Enhanced Driver Drowsiness Detection System using Deep Learning and Image Processing Techniques

Driver drowsiness remains a critical factor contributing to road accidents worldwide. To address this issue, we present an enhanced driver drowsiness detection system leveraging advanced deep learning and image processing techniques. Our research focuses on developing a robust and accurate model capable of detecting drowsiness indicators in real-time from driver facial images. We conducted a comprehensive review of existing literature on driver drowsiness detection systems and identified key challenges and opportunities in the field. Leveraging this knowledge, we propose a novel methodology that integrates convolutional neural networks (CNNs) for feature extraction and classification, coupled with sophisticated image processing algorithms for facial recognition and eye state analysis. We describe the experimental setup, data collection process, and model training procedures, followed by a detailed presentation of results and performance evaluation metrics. Our findings demonstrate significant improvements in drowsiness detection accuracy, with the proposed system achieving promising results in both laboratory and real-world driving scenarios. The implications of our research extend to the development of more effective driver assistance systems and the enhancement of road safety measures. We conclude by discussing future research directions and potential applications for advancing driver drowsiness detection technology. Keywords— Driver Drowsiness Detection, Deep Learning, Image Processing, Convolutional Neural Networks (CNNs), Facial Recognition, Road Safety, Real-Time Monitoring, Driver Assistance Systems, Accident Prevention

Creation Assistance of Cultural Confidence Aesthetic Works Based on Image Processing Technology

Image processing, combined with an aesthetic work culture, represents a powerful tool for enhancing creativity and productivity in various industries. By integrating image processing techniques with a workplace culture that values aesthetics and design, organizations can elevate the visual appeal of their products, services, and communications. Whether refining product designs, optimizing marketing materials, or creating visually stunning presentations, image processing enables teams to achieve higher levels of precision, consistency, and innovation. Moreover, fostering an aesthetic work culture encourages employees to prioritize creativity. This paper introduces a novel approach to the creation assistance of cultural confidence aesthetic works, leveraging image processing technology with Synergy Segmentation Global Entropy Classification (SS-GEC). By combining advanced image processing techniques with SS-GEC, this framework facilitates the generation of aesthetically pleasing artworks that instill cultural confidence and pride. Through simulated experiments and empirical validations, the effectiveness of the proposed approach is evaluated, demonstrating significant improvements in creative output and cultural resonance. For instance, the SS-GEC model achieved an average accuracy rate of 85% in identifying culturally significant motifs and themes, leading to more authentic and resonant artistic expressions. Additionally, the framework enabled a 40% reduction in design iteration time, streamlining the creative process and enhancing productivity. These results underscore the potential of image processing technology with SS-GEC in empowering artists and creators to produce culturally confident aesthetic works that celebrate heritage and identity.