Image Processing Applications Research Articles

A quantitative study of microstructure of Indian Gondwana shale: a fractal and algebraic topology approach

This paper covers a novel micro-level application of image processing in understanding the topological and petrophysical properties of Indian Gondwana Shale using an X-ray computed micro tomography images. The complexity and randomness in the pore system are explained through the concept of fractal dimension (FD). In this article, a quantitative analysis of 2D and 3D fractal dimension of pores, grains, and interface has been done for the Indian Gondwana Shale, using the Box counting method. A pore network is formed by the connection of many sub-pore clusters, each with a different volume. Hence, an image segmentation algorithm has been applied to label different sub-clusters, and subsequently, analysis of FD is done on such sub-clusters of pores and grains. We implemented a novel application of Betti numbers (B0, B1 and B2) and Euler characteristics on our sample and calculated the possible flow channels of the sample. The FD of grains was found to be greater than the FD of pore-grains interface, while the FD of pores was found to be the least. Consequently, we also observed how the FD of both pores and grains is majorly controlled by the largest sub-cluster, and during fluid intrusion, we see a significant decrease in FD of pores. Lastly, the pore network, with a larger B0 and larger difference of B1 can be proved best for the storage of hydrocarbon content and its fluid movement understanding due to more flow channels.

Image fusion-based low-dose CBCT enhancement method for visualizing miniscrew insertion in the infrazygomatic crest

Digital dental technology covers oral cone-beam computed tomography (CBCT) image processing and low-dose CBCT dental applications. A low-dose CBCT image enhancement method based on image fusion is proposed to address the need for subzygomatic small screw insertion. Specifically, firstly, a sharpening correction module is proposed, where the CBCT image is sharpened to compensate for the loss of details in the underexposed/over-exposed region. Secondly, a visibility restoration module based on type II fuzzy sets is designed, and a contrast enhancement module using curve transformation is designed. In addition to this, we propose a perceptual fusion module that fuses visibility and contrast of oral CBCT images. As a result, the problems of overexposure/underexposure, low visibility, and low contrast that occur in oral CBCT images can be effectively addressed with consistent interpretability. The proposed algorithm was analyzed in comparison experiments with a variety of algorithms, as well as ablation experiments. After analysis, compared with advanced enhancement algorithms, this algorithm achieved excellent results in low-dose CBCT enhancement and effective observation of subzygomatic small screw implantation. Compared with the best performing method, the evaluation metric is 0.07–2 higher on both datasets. The project can be found at: https://github.com/sunpeipei2024/low-dose-CBCT.

Salient object detection: a mini review

This paper presents a mini-review of recent works in Salient Object Detection (SOD). First, We introduce SOD and its application in image processing tasks and applications. Following this, we discuss the conventional methods for SOD and present several recent works in this category. With the start of deep learning AI algorithms, SOD has also benefited from deep learning. Here, we present and discuss Deep learning-based SOD according to its training mechanism, i.e., fully supervised and weakly supervised. For the benefit of the readers, we have also included some standard data sets assembled for SOD research.

Identification of Dragon Trees and Fruits in Ham Thuan Bac Growing Areas, Phan Thiet City, Binh Thuan Province, Vietnam

With the development of Computer Vision, we can effectively and accurately identify trees, fruit or object images. But to build a high-performance image dataset for tree identification problems in Agriculture is a challenge. Realizing that Vietnam is a country with strong agriculture with many tropical fruits grown widely such as Dragon fruit, Mangosteen, Mango, Orange, Lychee, Longan... We chose the Dragon Fruit tree for the data set. of my proposed images, all images will be collected using the close-up method, including tasks such as taking photos of Dragon Fruit trees from many angles and in different conditions (weather, temperature, light,...). In this article, we want to improve the data quality of the collected images so we have applied image processing techniques such as noise filtering (using Gaussian filter), image quality enhancement (image rotation), flip the image, zoom out, zoom in, etc.). From the collected Dragon Fruit tree data set, we will propose to use the Faster R-CNN model for this data set to build a tree and Dragon Fruit identification system.

Construction of non-stationary biorthogonal Wavelets based on cardinal Chebyshev B-spline

In this paper, we propose a novel method for designing non-stationary biorthogonal wavelets that leverages Bezout equations to compute wavelet filter coefficients. Our approach integrates cardinal Chebyshev B-splines of varying degrees (second, third, and fourth) as bases for constructing these wavelets, providing explicit formulations for both low-pass and high-pass filters at all levels. By employing this method, we streamline the design process, making it more efficient and accessible while accommodating diverse processing requirements. The utilization of Bezout equations offers a systematic framework for deriving wavelet filter coefficients, enhancing the reproducibility and reliability of the design process. This systematic approach ensures that the resulting wavelets possess desired properties such as vanishing moments, smoothness, and optimal localization. Moreover, by incorporating cardinal Chebyshev B-splines, we can effectively capture intricate signal and image features, addressing challenges encountered in traditional wavelet design methods. Our method not only simplifies the design process but also provides a comprehensive solution by explicitly formulating filters and dual filters across all levels. This comprehensive approach ensures that the resulting wavelets meet the specific demands of applications in signal and image processing, including compression, denoising, and pattern recognition. These advancements hold significant implications for various domains, including information technology, healthcare, and engineering, where efficient and reliable processing techniques are crucial. Moving forward, it is imperative to refine algorithms, assess parameter robustness, and conduct thorough empirical validations across diverse datasets and application scenarios. This will ensure the efficacy and scalability of our proposed method and pave the way for further advancements in non-stationary wavelet design and its applications. By continuously improving and validating our approach, we can foster innovation and enhance practical outcomes in a wide range of fields.

PlantGuard: Leaf Disease Detecting and Suggests Remedy using Machine Learning

Abstract: Plantguard is a leader in the fusion of artificial intelligence and agriculture, with a primary focus on transforming cropmanagement via sophisticated leaf disease detection. The initiative provides quick and reliable plant disease identification from leaf photos by utilizing cutting-edge computer vision and machine learning algorithms, giving farmers the ability to take prompt action. Plantguard is dedicated to creating user-friendly interfaces that will allow precision agriculture to be smoothly incorporatedinto conventional agricultural methods. This will promote sustainable production, boost farmer productivity, and raise their financial stability. This novel strategy represents a significant step toward a future for global agriculture that is both resilient and more technologically advanced. Convolution neural network (CNN), transfer learning, deep learning, image processing, data augmentation, and agricultural applications are some of the index terms

Enhanced Road Safety through Integrated Speed Breaker, Pothole, and Lane Detection System

Abstract: Amidst the rapid evolution of autonomous vehicle technologies, ensuring road safety remains a paramount challenge. Effective detection of lanes and potential hazards, including speed breakers and potholes, is critical for safe autonomous driving. In this study, we introduce an innovative Lane, Speed Breaker, and Pothole Detection System (LSPDS) utilizing YOLOv4 Tiny, a state-of-the-art object detection algorithm and computer vision techniques. Our system integrates computer vision and machine learning techniques for analysis of road conditions. By employing camera sensors, we capture the road scene and apply image processing algorithms to identify lanes, speed breakers, and potholes. Moreover, the system incorporates Firebase for user authentication and SMS services for real-time alerts. YOLOv4 Tiny is employed for accurate detection and classification of these features within the captured images, thereby enhancing the perception capabilities of autonomous vehicles.

E-Citrus: A Cloud-Based Citrus Pest and Disease Detection, Diagnostic and Prevention using Convolutional Neural Network

Citrus fruit yields in the Philippines have been fluctuating dramatically in recent years. Diseases, pests, and soil inadequacy have all contributed to the citrus industry's severe decline. More than 15 viruses and virus-like diseases have infected Citrus. Agricultural productivity must improve for a country to be progressive. Resources should be utilized to their full potential, diseases and pests should be controlled efficiently, and technological advancements must be adopted. This application will identify and map common pests and diseases of citrus fruits in Oriental Mindoro, apply image processing techniques to analyze diseases of citrus fruits with corresponding solutions caused by bacteria, and give information about diseases related to citrus fruits and how to cure them. The researchers used the Spiral Model as a Software Development Life Cycle (SDLC) model to develop this application. In this model, researchers can plan the flow of the application. If the application did not meet the desired result, the researchers could revise it again until it met the desired one. The researchers used the convolutional neural network to classify and process the captured images of the citrus fruits’ diseases and pests. The researchers asked the selected Citrus farmers in Oriental Mindoro to evaluate the project using the different ISO 25010 criteria and rated the application as very acceptable overall.

Analysis of the application of artificial intelligence image processing technologybased on citespace

Image processing technology has begun to be widely used in all major fields, and the update of artificial intelligence technology has also led to the progress and development of image recognition technology, which not only contributes to scientific research purposes, but also makes peoples lives more convenient. The article analyses the theoretical concepts of artificial intelligence-related technologies, the current status of the application of artificial intelligence image processing technology, and the characteristics of image processing technology, and combined with the actual situation, to explore the combination of artificial intelligence and image processing technology, so that the image processing technology can be sustained and applied to life, scientific research, industry and other fields.

Hand Joint Position Analysis System for the Care of Patients with Rheumatoid Arthritis

According to the studies carried out in advance, the medical area has analysed the patient's condition visually by applying a common methodology to all patients based on their knowledge of the disease, making a mistaken assessment due to the complexity of the disease, which varies according to the length of time the patient has been suffering from it. The problem posed by this disease rheumatoid arthritis is the bad evaluation by means of radiographs of the hands, relying on the criteria of the doctor when treating the disease with his experience, making serious mistakes when the quality of the image is not correct when interpreting a first stage disease with an advanced one, therefore, it is important to evaluate rheumatoid arthritis by means of an image treatment in a safe way for a better diagnosis of the patient. The aim of this research is to develop a hand joint position analysis system for the care of patients with rheumatoid arthritis to help doctors detect tuberculosis earlier and more accurately and to avoid prolonged infections that could be fatal for patients. The methodology used for this research is based on taking a computerized X-ray of both hands and applying image processing using a computer, the image processing is carried out using MATLAB by applying the various processing tools to detect these conditions. According to the tests performed, it was observed that the system detects the state of the patient with rheumatoid arthritis with an efficiency of 96.14% in its operation, standing out satisfactorily with respect to other systems for its high efficiency of image analysis of patients with the MATLAB tool. It concludes that this system can be used in different circumstances of the patient's condition, from the initial stage of arthritis to an advanced stage of the patient's disease.

Facial Recognition-based Attendance System

Facial recognition stands as one of the most efficient applications in image processing, playing a crucial role in the technical sphere. Identifying human faces is a pressing concern, particularly in verifying student attendance. Utilizing facial biostatistics, an attendance system employing face recognition relies on high-resolution monitoring and advanced computer technologies. The objective of developing this system is to digitize the traditional method of attendance-taking, which involves verbal calls and manual record- keeping.Current attendance procedures are laborious and time-consuming, prone to manipulation through manual recording. Both traditional attendance marking and existing biometric systems are susceptible to fraudulent proxies. This paper aims toaddressthesechallenges. The proposed system incorporates the Haar cascade algorithm, OpenCV, Dlib, Pandas, and MySQL. Following facial recognition, attendance reports are generated and saved in Excel format. The system undergoes testing under different conditions, such as variations in illumination, head movements, and changes in camera-to-student distance. Rigorous testing evaluates overall complexity and accuracy. The proposed system proves to be an efficient and robust solution for classroom attendance management, eliminating manual labour and time consumption. Additionally, the system's development is cost-effective and requires minimal installation.

Optimizing support vector machine (SVM) by social spider optimization (SSO) for edge detection in colored images

Edge detection in images is a vital application of image processing in fields such as object detection and identification of lesion regions in medical images. This problem is more complex in the domain of color images due to the combination of color layer information and the need to achieve a unified edge boundary across these layers, which increases the complexity of the problem. In this paper, a simple and effective method for edge detection in color images is proposed using a combination of support vector machine (SVM) and the social spider optimization (SSO) algorithm. In the proposed method, the input color image is first converted to a grayscale image, and an initial estimation of the image edges is performed based on it. To this end, the proposed method utilizes an SVM with a Radial Basis Function (RBF) kernel, in which the model's hyperparameters are tuned using the SSO algorithm. After the formation of initial image edges, the resulting edges are compared with pairwise combinations of color layers, and an attempt is made to improve the edge localization using the SSO algorithm. In this step, the optimization algorithm's task is to refine the image edges in a way that maximizes the compatibility with pairwise combinations of color layers. This process leads to the formation of prominent image edges and reduces the adverse effects of noise on the final result. The performance of the proposed method in edge detection of various color images has been evaluated and compared with similar previous strategies. According to the obtained results, the proposed method can successfully identify image edges more accurately, as the edges identified by the proposed method have an average accuracy of 93.11% for the BSDS500 database, which is an increase of at least 0.74% compared to other methods.

A lightweight feature activation guided multi-receptive field attention network for light compensation

Image enhancement techniques are commonly used to improve problems such as lack of brightness, high noise, and low contrast in low-light images. Notably, deep learning-based approaches have recently achieved substantial advancements in this domain. However, learning-based methods often require many parameters and multi-layer network structures to achieve high-quality enhancement effects, which limits their application in real-time image processing. To solve this problem, a lightweight Feature Activation Guided Multi-Receptive Field Attention Network (FAMANet) is designed in this paper. The Wavelet Feature Activation Block (WFAB) introduced in the network utilizes the discrete wavelet transform and residual connection to achieve selective activation of image features, thus reducing the redundant information in the feature map and improving the computational efficiency. In addition, the Multi-Receptive Field Attention (MRFA) introduced in this paper addresses the issue of inadequate pixel information and feature map loss stemming from a single input image by concentrating on the image structure, spanning from intricate details to the overall composition. By better-utilizing image information and distinguishing between global and local features, MRFA can improve the speed and efficiency of real-time image processing. After sufficient experimental validation, FAMANet significantly outperforms state-of-the-art methods in low-light image enhancement and exposure correction tasks.

Development of a sustainable procedure for smartphone-based colorimetric determination of benzalkonium chloride in pharmaceutical preparations

A sustainable procedure offering green, simple, and rapid analysis was developed to determine benzalkonium chloride (BKC) in pharmaceutical preparations. The determination using smartphones was based on the ion pair colorimetric reaction with bromothymol blue (BTB), which produces a yellow color. The intensity of the product color, which is proportional to the concentration of BKC, was detected and evaluated using a smartphone camera and an image processing application. The procedure was performed in a microliter and was rapidly detected within 1 min after incubation. This offered high throughput at 28 samples per well plate in duplicate. Linear calibration, which was a plot of BKC concentrations and relative red intensities, was in the range of 2.0–24.0 μg/mL with an R2 of 0.997. The limits of detection (LOD) and quantitation (LOQ) were 1.0 and 3.2 μg/mL, respectively. This work was successful in applying it to pharmaceutical materials, disinfectant products, and pharmaceutical products containing BKC. It was discovered that the concentrations of BKC as an active ingredient in pharmaceutical materials were 82% w/v, whereas those in disinfectant products ranged from 0.4 to 2.1% w/v. In pharmaceutical products, ophthalmic drops and nasal sprays contain BKC as preservatives in the 0.01–0.02, and the 0.02% w/v, respectively. The results obtained by the proposed procedure compared with a reference titration method showed no significant differences at a 95% confidence level with 1.2–3.4% RSDs. This promotes the efficiency of pharmaceutical preparations regarding infection prevention and control by ensuring that available disinfectants contain a sufficient concentration of BKC. Additionally, this improves the efficiency of pharmaceutical preparations for quality control of pharmaceutical products by ensuring that the available preservatives maintain a sufficient concentration throughout the lifespan of the products.

Helicity-Sensitive Terahertz Detection in Monolayer 1T'-WTe2.

Valleytronics, identified as electronic properties of the energy band extrema in momentum space, has been intensively revived following the emergence of two-dimensional transition metal dichalcogenides (TMDCs) as their valley information can be controlled and probed through the spin angular momentum of light. Previous optical investigations of valleytronics have been limited to the visible/near-infrared spectral regime through which the carriers of most TMDCs can be excited. Monolayer 1T'-WTe2 with broken time-reversal symmetry provides a fertile platform to study the long-wavelength photonic properties in different valleys. Here, we employed a circularly polarized terahertz (THz) laser to selectively excite the valley of monolayer 1T'-WTe2 and demonstrate that the helicity-dependent photoresponse is generated via the photogalvanic effect (PGE). We also observed that the photocurrent is controlled by circular polarization and the external electric field. Because of the tunable Berry curvature dipole derived from the nontrivial wave functions near the inverted gap edge in monolayer WTe2, the bandgap can be tuned efficiently. Our results provide a versatile venue for controlling, detecting, and processing valleytronics and applications in on-chip THz imaging and quantum information processing.

Code-Free Machine Learning Solutions for Microscopy Image Processing: Deep Learning.

In recent years, there has been a significant expansion in the realm of processing microscopy images, thanks to the advent of machine learning techniques. These techniques offer diverse applications for image processing. Currently, numerous methods are used for processing microscopy images in the field of biology, ranging from conventional machine learning algorithms to sophisticated deep learning artificial neural networks with millions of parameters. However, a comprehensive grasp of the intricacies of these methods usually necessitates proficiency in programming and advanced mathematics. In our comprehensive review, we explore various widely used deep learning approaches tailored for the processing of microscopy images. Our emphasis is on algorithms that have gained popularity in the field of biology and have been adapted to cater to users lacking programming expertise. In essence, our target audience comprises biologists interested in exploring the potential of deep learning algorithms, even without programming skills. Throughout the review, we elucidate each algorithm's fundamental concepts and capabilities without delving into mathematical and programming complexities. Crucially, all the highlighted algorithms are accessible on open platforms without requiring code, and we provide detailed descriptions and links within our review. It's essential to recognize that addressing each specific problem demands an individualized approach. Consequently, our focus is not on comparing algorithms but on delineating the problems they are adept at solving. In practical scenarios, researchers typically select multiple algorithms suited to their tasks and experimentally determine the most effective one. It is worth noting that microscopy extends beyond the realm of biology; its applications span diverse fields such as geology and material science. Although our review predominantly centers on biomedical applications, the algorithms and principles outlined here are equally applicable to other scientific domains. Furthermore, a number of the proposed solutions can be modified for use in entirely distinct computer vision cases.

On n-Dimensional Maxwell and Dirac Equations in Curved Space-Time and Its Applications in SO(P,Q) Group Theoretic Image Processing

Maxwell equations in p time and q spatial dimensions are formulated. Properties of the Green’s function for the associated (p,q)-wave operator are derived based on contour integration. The notions of electric and magnetic fields in (p,q)-dimensions is explored by analogy with four dimensional physics and the problem of far field computation of the radiation field generated by charges and currents in \((1,n-1)\) space-time is analyzed. SO(p,q) invariance properties of the Maxwell equations are deduced and used to formulate SO(p,q)-group theoretic image processing problems in (p,q)-dimensions. Dirac’s equation in (p,q)-dimensional space-time is derived using the Clifford algebra of the Dirac gamma matrices. SO(p,q)-invariance of the Dirac equation based on the spinor representation of \(SO(p,q)\) is mentioned. The Maxwell’s equations in (p,q)-dimensional curved space-time is analyzed using perturbation theory in the context of maximally symmetric spaces which play a fundamental role in \((p,q)\)-dimensional cosmological models for homogeneous and isotropic spaces. The Einstein-Maxwell equations for (p,q)-dimensional gravity and electromagnetism is studied and used to derive the equations of motion of point charges carrying mass moving under mutual gravitational and electromagnetic interactions in general relativity. Finally, Dirac’s equation in (p,q)-dimensional curved space-time interacting with the (p,q)-dimensional electromagnetic field is looked at. \(U(1)\)-Gauge, local SO(p,q) Lorentz and diffeomorphism invariance of this equation is analyzed. Local \(SO(p,q)\) invariance of the curved space-time Dirac equation is deduced based on transformation properties of the Dirac matrices under the spinor representation. The appendix presents some applications of group representation theoretic statistical image processing on a manifold to the situation when the image field is described by the six component electromagnetic field tensor on which the Lorentz group acts.

Analysis on the Application of Image Processing in UAV and Autopilot

The purpose of this paper is to analyze the application of image processing in UAV and autopilot. UAV and autonomous driving technology are important development directions in the field of intelligent transportation and agricultural production, and image processing, as one of the core technologies supporting its perception and decision-making, plays a vital role. For UAV, in agricultural plant protection and natural disaster monitoring, the rapid evaluation and identification of farmland growth status and disaster situation can be realized through image processing technology, which provides important support for crop production and rescue work. For the automatic driving system, image processing technology can detect and track the road environment, traffic signs and pedestrians, and improve the driving safety and comfort of vehicles. However, image processing technology also faces some challenges in application, such as complex environmental conditions and real-time requirements. In the future, through the improvement of sensor technology, algorithm optimization and data sharing, the application of image processing technology in UAV and autonomous driving will be continuously improved, bringing more innovation and development opportunities for intelligent transportation and agricultural production.

Research On Real Time Emotion Recognition Using Digital Image Processing Using ML

Face recognition technology has garnered significant attention in recent years due to its wide range of applications in various fields such as security, surveillance, biometrics, and human-computer interaction. This abstract provides a comprehensive overview of the advancements in face recognition image processing techniques, methodologies, and applications. The abstract begins by elucidating the fundamental concepts underlying face recognition, including feature extraction, dimensionality reduction, and classification algorithms. Various approaches such as Eigenfaces, Fisher faces, and Local Binary Patterns (LBP) are discussed, highlighting their strengths and limitations. Moreover, recent developments in deep learning techniques, particularly convolutional neural networks (CNNs) and Siamese networks, have revolutionized face recognition by achieving remarkable accuracy and robustness. The abstract delves into the architecture and training procedures of these deep learning models, emphasizing their ability to learn discriminative features directly from raw pixel data. Furthermore, the abstract explores the challenges faced by face recognition systems, such as variations in pose, illumination, expression, and occlusion. Techniques for addressing these challenges, including data augmentation, normalization, and adversarial training, are examined.

Digital Image and Video Processing: Algorithms and Applications

Many of the techniques that are used in digital image and video processing were developed in the 1960s at Bell Laboratories. These techniques have applications in a variety of fields, including medical imaging, videophone, character recognition, satellite imagery, and wire-photo standards conversion. Additional applications include enhancement of photographs or vidoes. The early stages of image and video processing were developed with the intention of enhancing the overall quality of the image or video. For the purpose of enhancing the visual effect of humans, it is intended for human beings. When it comes to image and video processing, the input is an image of poor quality, and the output is an image and video of higher quality. Research on algorithms and applications of digital image and video processing is the primary purpose of this study, which aims to investigate these topics extensively. The methodology employed in this study is qualitative research technique. In accordance with the findings of this research, "Image Processing" refers to the process of analyzing images with the objective of determining the significance of objects and identifying them. Image analysts analyze data that has been remotely sensed and attempt to detect, identify, classify, measure, and evaluate the significance of physical and cultural objects, as well as their patterns and spatial relationships. One subcategory of signal processing is known as video processing, and it is distinguished by the fact that the signals that are input and output are video files or video streams. Technology such as television sets, videocassette recorders (VCRs), DVD players, and other devices all make use of video processing algorithms. The processing of images and videos is extremely useful in a variety of contexts.