Digital Image Processing Techniques Research Articles

Drapability of 3D-Printed Auxetic Structure Textiles for Wearable Products Through the Digital Image Processing Technique

This study aimed to explore drapability of 3D-printed auxetic structure textiles with different geometries through the digital image processing technique in order to showcase their potential applications in 3D-printed wearable product development. A 13 (textile samples) × 3 (repetition) experimental research design consisting of 10 3D-printed auxetic structure textiles and three traditional lace fabrics were utilized in this study. The findings indicate that 3D-printed multi-angular auxetic structures exhibited the highest level of drapability followed by sinusoidal and triangular auxetic structures; these multi-angular structure textiles with draping coefficient ranging from 26.82% to 31.43% have a great potential to simulate traditional lace-like fabrics. A statistically significant correlation also was found between drapability and weight of 3D-printed auxetic structure textiles. This study demonstrates a true potential of 3D-printed auxetic structure textiles as alternatives of traditional lace textiles and their application in the wearable product development.

Face Recognition-Based Automatic Attendance System in a Smart Classroom

The smart classroom is a fully automated classroom where repetitive tasks, including attendance registration, are automatically performed. Due to recent advances in artificial intelligence, traditional attendance registration methods have become challenging. These methods require significant time and effort to complete the process. Therefore, researchers have sought alternative ways to accomplish attendance registration. These methods include identification cards, radio frequency, or biometric systems. However, all of these methods have faced challenges in safety, accuracy, effort, time, and cost. The development of digital image processing techniques, specifically face recognition technology, has enabled automated attendance registration. Face recognition technology is considered the most suitable for this process due to its ability to recognize multiple faces simultaneously. This study developed an integrated attendance registration system based on the YOLOv7 algorithm, which extracts features and recognizes students’ faces using a specially collected database of 31 students from Mustansiriyah University. A comparative study was conducted by applying the YOLOv7 algorithm, a machine learning algorithm, and a combined machine learning and deep learning algorithm. The proposed method achieved an accuracy of up to 100%. A comparison with previous studies demonstrated that the proposed method is promising and reliable for automating attendance registration.

Feature information extraction method for narrow gap U-type groove based on laser vision

The precise measurement of weld groove position and geometric dimensions serves as the foundation for predicting and planning welding strategies and process parameters. The steep sidewalls of narrow gap U-type grooves pose challenges for sensing and detection during the welding process. This paper presents a feature information extraction method for narrow gap U-type grooves based on laser vision technology. Firstly, the image acquisition system is calibrated using the incident plane of the laser beam. Subsequently, a digital image processing technique is employed to enhance the collected images, combining a 3D spatial domain enhancement algorithm with a laser stripe image refinement method based on Euclidean distance transformation. Finally, the transverse and vertical positions of the groove, as well as its geometric features such as width, depth, assembly gap, and misalignment, are computed and measured using feature point positioning. The proposed algorithm is experimentally verified for its accuracy, robustness, and real-time performance. The results indicate that the method effectively suppresses unevenness in laser stripe brightness and mitigates random noise in the captured images. The average measurement errors for transverse position, vertical position, width, depth, assembly gap, and misalignment of the groove are 0.0857 mm, 0.0987 mm, 0.1664 mm, 0.2216 mm, 0.0935 mm, and 0.0997 mm, respectively. Furthermore, the average image processing time per frame is 58 ms, meeting the requirements of pre-weld detection in arc welding.

Image Fusion Algorithm using Grey Wolf optimization with Shuffled Frog Leaping Algorithm

Data fusion is a “formal framework in which are expressed the means and tools for the alliance of data originating from different sources.” It aims at obtaining information of greater quality; the exact definition of 'greater quality will depend upon the application. It is a famous technique in digital image processing and is very important in medical image representation for clinical diagnosis. Previously many researchers used many meta-heuristic optimization techniques in image fusion, but the problem of local optimization restricted their searching flow to find optimum search results. In this paper, the Grey Wolf Optimization (GWO) algorithm with the help of the Shuffled Frog Leaping Algorithm (SFLA) has been proposed. That helps to find the object and allows doctors to take some action. The optimization algorithm is examined with a demonstrated example in order to simplify its steps. The result of the proposed algorithm is compared with other optimization algorithms. The proposed method's performance was always the best among them.

Sugarcane nitrogen nutrition estimation with digital images and machine learning methods

The color and texture characteristics of crops can reflect their nitrogen (N) nutrient status and help optimize N fertilizer management. This study conducted a one-year field experiment to collect sugarcane leaf images at tillering and elongation stages using a commercial digital camera and extract leaf image color feature (CF) and texture feature (TF) parameters using digital image processing techniques. By analyzing the correlation between leaf N content and feature parameters, feature dimensionality reduction was performed using principal component analysis (PCA), and three regression methods (multiple linear regression; MLR, random forest regression; RF, stacking fusion model; SFM) were used to construct N content estimation models based on different image feature parameters. All models were built using five-fold cross-validation and grid search to verify the model performance and stability. The results showed that the models based on color-texture integrated principal component features (C-T-PCA) outperformed the single-feature models based on CF or TF. Among them, SFM had the highest accuracy for the validation dataset with the model coefficient of determination (R2) of 0.9264 for the tillering stage and 0.9111 for the elongation stage, with the maximum improvement of 9.85% and 8.91%, respectively, compared with the other tested models. In conclusion, the SFM framework based on C-T-PCA combines the advantages of multiple models to enhance the model performance while enhancing the anti-interference and generalization capabilities. Combining digital image processing techniques and machine learning facilitates fast and nondestructive estimation of crop N-substance nutrition.

Statistical analysis of the compressive strength of concrete using 2D DIP technology and Finite Element Method

Finite element models for studying concrete usually consider materials homogeneous, while concrete heterogeneously consists of aggregates, cement paste, and air voids. In this study, based on the 2D models of concrete with real heterogeneous behavior, the compressive strength of concrete was evaluated using digital image processing (DIP) techniques. It was attempted to create 2D models in Abaqus software for two concrete cubes under the conditions of the actual model of aggregates, cement paste, and air voids to achieve force-displacement curves. In this study, statistical analyses were performed to find out which slice leads to accurate results, and the variation coefficient of maximum forces corresponding to concrete compressive strength through specimens was obtained accordingly. The standard deviation obtained from the middle one-third of concrete cubes was about 3–10 %. According to nearly 100 % saving in processing time by analyzing 2D images, this study can be an auxiliary alternative for destructive methods or maybe for non-destructive testing methods in the near future.

Deep learning enhanced Watershed for microstructural analysis using a boundary class semantic segmentation

The mechanical properties of the materials are determined by the size and morphology of fine microscopic features. Quantitative microstructural analysis is a key factor to establish the correlation between the mechanical properties and the thermomechanical treatment under which material condition has been achieved. As such, microstructural analysis is a very important and complex task within the manufacturing sector. Published standards are used for metallographic analysis but typically involve extensive manual interpretation of grain boundaries, resulting in measurements that are slow to produce, difficult to repeat and highly subjective. Computer vision and the evolution of artificial intelligence in the past decade can offer solutions to such problems. Deep learning and digital image processing techniques allow digital microstructural analysis to be automated using a fast and repeatable method. This paper proposes a novel boundary class semantic segmentation approach (BCSS) to identify each phase of the microstructure and additionally estimate the location of the grain boundaries. The BCSS is then combined with more traditional segmentation techniques based on the Watershed Transform to improve the identification and measurement of each feature within the microstructure using a new, hybrid automated digital microstructure analysis approach. The new method is validated on a published dataset of two-phase titanium alloy microstructure pictures captured using a scanning electron microscope. Measurements match the level of accuracy of accepted manual standards, and the method is demonstrated to be more reliable than other automated approaches. The influence of the subjective nature of manual labelling, required to train the proposed network, is also evaluated.

Application of Image Processing in Different Machining Processes: A Short and Technical Review

This article discusses the use of digital image processing in a variety of machining processes and the benefits that it brings to the industry. In this article, we will also cover the benefits and drawbacks of using digital image processing techniques instead of the various different sensors that are utilized in machining in order to increase product quality. This article provides a concise introduction to several image processing methods that are utilized in the machining process. This paper contains a discussion of a comprehensive analysis of the applications of image processing that have been used in machining during the past ten years. In addition, an illustration of one approach to image texture analysis that may be applied for cutting tool condition identification through the examination of photographs of machined surfaces is shown. A general conclusion that can be drawn from this and leads to the necessary further research in this area has also been discussed.

Development of a Model for Recognizing Cracks on Concrete Surfaces Using Digital Image Processing Techniques

Development of a Model for Recognizing Cracks on Concrete Surfaces Using Digital Image Processing Techniques

Utilizing Multi-Class Support Vector Machine to Detect Plant Leaf Diseases

Abstract: Plant disease has a large influence on agricultural output and food security. For successful illness management, early detection and correct diagnosis of these disorders are critical. Digital image processing techniques have emerged as a viable tool for automated plant leaf disease identification in recent years. The goal of this study is to use image processing to create a more efficient and accurate system for detecting leaf illness. K-means clustering, Gray-Level Co-Occurrence Matrix (GLCM), and Support Vector Machine are used to create the model. Kmeans clustering is employed for image segmentation, GLCM, and multiclass SVM for feature extraction and illness classification respectively. The maximum accuracy is 98.4%

Quantitative detection of internal defects in objects using holographic double exposure

Recent years have witnessed an increasing demand for high-quality materials in all industries; therefore, internal defects are disastrous for maintaining the required industrial quality. In this study, qualitative and quantitative analysis of the internal defects of objects is conducted using differential holographic double-exposure and digital image processing techniques. The objects underwent thermal loading, and two holograms were recorded at different temperatures. Internal defects were qualitatively and quantitatively analysed by processing and comparing the interference fringes produced by removing and reproducing the two holograms via digital image processing. The fringes at the location of the sample defects differed from the changes in surrounding fringes, and the defect size was determined by fitting the fringe region at the defect location. Finally, the experimental results were validated using a structured light 3D measurement method to identify the size of the defects. Interesting results were obtained, with a maximum error of 9.637 % for irregular defects and a minimum error of 3.528 % for regular defects.

DETEKSI AREA KERUSAKAN PADA CITRA TERUMBU KARANG AKIBAT CORAL BLEACHING BERBASIS PENGOLAHAN CITRA DIGITAL

Coral reefs have a very large role in marine ecosystems, because they provide rich habitats and support biodiversity. Segmentation on images of coral reefs experiencing coral bleaching is also very important. Coral bleaching is a phenomenon in which coral reefs lose their symbiotic algae pigments, resulting in bleaching and possibly death of coral reefs. By segmenting coral reef images that experience coral bleaching, we can separate areas affected by bleaching from areas that are still healthy. This study aims to detect areas of damage that occur in underwater images of coral reefs that have experienced coral bleaching. By using techniques in digital image processing and segmentation based on colour intensity, satisfactory results are obtained. The test results show that the RAE and ME values are quite low, namely 0.051 and 0.035 respectively with an average processing time of 0.2 seconds. This research is expected to assist in further analysis and modelling related to coral bleaching to understand the causative factors and develop appropriate protection strategies.

Integrated digital image processing techniques and deep learning approaches for wheat stripe rust disease detection and grading

This study proposes integrated image processing and deep learning approaches for wheat rust disease detection and grading. Wheat stripe rust is one of the diseases affecting the crop in nearly all wheat-growing countries, where humidity and high temperatures characterize the growing season. The early and prompt detection of this disease is essential for establishing timely remedial measures to contain its spread. We present several image-processing and deep-learning approaches for detecting wheat stripe rust. The image processing techniques used in this research include single-band and dual-band processing, computing the Visual Atmospheric Resistance Index (VARI), image segmentation, and image cropping. These techniques are evaluated with different deep learning approaches like transfer learning and other Convolutional Neural Networks (CNNs) architectures. The results obtained on the dataset show that processing the images to eliminate the background as much as possible before training a suitable deep learning model with a low number of epochs optimally classifies the wheat stripe rust into the target classes (i.e., healthy, resistant, and susceptible) with an overall accuracy of 84.1% on the holdout test set. Besides the comparative analysis of various image processing approaches with different deep learning models, this study suggests multiple future research directions.

Numerical study of the gas–solid flow pattern in methanol to olefiens fluidized bed reactor: Cold model

AbstractIn the present study, the hydrodynamic behaviour of a three‐dimensional bubbling fluidized bed reactor was studied using the finite volume method and granular kinetic theory. The effect of two types of gas distributors (i.e., perforated and porous plates) on the gas/solid hydrodynamic was investigated. The residence time distribution model was utilized to check the deviation of the gas flow pattern from the ideal flow patterns for two types of distributors. The parameters indicating the degree of back‐mixing, such as mean residence time and dimensionless variance, were calculated. Also, the model was verified through the comparison of the particle velocity with experimental data obtained by the particle image velocimetry–digital image processing (PIV‐DIA) technique. It was found that the gas flow pattern in the present bubbling fluidized bed is closer to the mixed flow than the plug flow pattern. It was also found that the porous plate distributor gives more uniform hydrodynamic distributions of gas and solid in the bed, which results in better gas–solid mixing due to the formation of the smaller bubbles.

Microstructural evolution and coarsening behavior of the precipitates in 2205 duplex stainless steel aged at 850 °C

The formation of the secondary phases in the 2205 duplex stainless steel (DSS) influences greatly its mechanical properties. Studies on the microstructural evolution and coarsening behavior of the precipitates in the 2205 DSS have both scientific and technological significance. In the present work, the evolution of the composition and morphology of the precipitates in the 2205 DSS aged at 850 °C for periods up to 200 h are systematically investigated by SEM/EDS (Scanning Electron Microscope/Energy Dispersive Spectrometer) and TEM (Transmission Electron Microscope). In addition, the size statistics for intermetallic precipitates, especially for the σ phase, are carried out based on the SEM images by using deep learning and digital image processing technique. Based on the above, the average interphase energy between the σ and γ phases is reasonably estimated according to the Ostwald coarsening mechanism. This work provides a comprehensive understanding of the microstructural evolution and coarsening behavior of the precipitates in 2205 DSS.

Assessing the Photovoltaic Power Generation Potential of Highway Slopes

The solar photovoltaic (PV) power generation system (PGS) is a viable alternative to fossil fuels for the provision of power for infrastructure and vehicles, reducing greenhouse gas emissions and enhancing the sustainability of road transport systems. A highway slope is generally an idle public area with high accessibility, which is the ideal application scenario for a PV PGS. The assessment of PV power generation potential (PGP) is key for the planning and design of PV PGS projects. Previous approaches to potential assessments are mainly based on digital maps and image processing techniques, which do not fully consider the impacts of the highway orientation, the slope geometric characteristics, and the PV panel placement scheme on the evaluation results. Therefore, this study proposes an assessment method for the PV PGP on highway slopes using the design or calculated highway and slope geometric parameters and the solar radiation received by PV panels under the desirable placement scheme. Highway segmentation and geometric parameter calculation methods were established, and the optimal PV array placement schemes for typical slope orientations were determined by simulating the PV power generation in the software PVsyst (version 7.2). Afterwards, the theoretical PGP could be calculated using the received solar radiation and the available slope area. By subtracting the energy loss caused by temperature changes, the operation of inverters, and the PV modules’ performance decay, the actual PV PGP could be obtained. Finally, a case study of the solar PGP assessment of a 1.97 km long highway section is provided, and the feasibility of the proposed method is verified.

An Automatic Approach for Bone Tumor Detection from Non-Standard CT Images

Image processing techniques are applied in many fields of science. This study aims to detect tumors in the foot and create 3D models via computed tomography (CT), as well as to produce biometric data. 1 039 CT images were obtained from a server. The parameters used were a collimation of 64 detectors, a scanning thickness of 0,5-3 mm, and a pixel size of 512 x 512, with a radiometric resolution of the 16-bit gray levels. Noise reduction, segmentation, and morphological analysis were performed on CT scans to detect bone tumors. In addition, this study used digital image processing techniques to create a virtual three-dimensional (3D) model of bone tumors. The performance of our proposal was evaluated by analyzing the receptor operating characteristics (ROC). According to the results, the sensitivity, specificity, and precision in tumor detection were 0,96, 1, and 0,98%, respectively, with a 0,99% average F-measure. Radiologist reports were used for the sake of comparison. The proposed technique for detecting bone tumors of the foot via CT can help radiologists with its increased precision, sensitivity, specificity, and F-measure. This method could improve the diagnosis of foot and ankle tumors by allowing for the multidirectional quantification of abnormalities.

Ultraviolet Radiation Transmission in Building’s Fenestration: Part II, Exploring Digital Imaging, UV Photography, Image Processing, and Computer Vision Techniques

The growing demand for sustainable and energy-efficient buildings has highlighted the need for reliable and accurate methods to detect fenestration deterioration and assess UV radiation transmission. Traditional detection techniques, such as spectrophotometers and radiometers, discussed in Part I, are often expensive and invasive, necessitating more accessible and cost-effective solutions. This study, which is Part II, provides an in-depth exploration of the concepts and methodologies underlying UV bandpass-filtered imaging, advanced image processing techniques, and the mechanisms of pixel transformation equations. The aim is to lay the groundwork for a unified approach to detecting ultraviolet (UV) radiation transmission in fenestration glazing. By exploiting the capabilities of digital imaging devices, including widely accessible smartphones, and integrating them with robust segmentation techniques and mathematical transformations, this research paves the way for an innovative and potentially democratized approach to UV detection in fenestration glazing. However, further research is required to optimize and tailor the detection methods and approaches using digital imaging, UV photography, image processing, and computer vision for specific applications in the fenestration industry and detecting UV transmission. The complex interplay of various physical phenomena related to UV radiation, digital imaging, and the unique characteristics of fenestration glazing necessitates the development of a cohesive framework that synergizes these techniques while addressing these intricacies. While extensively reviewing existing techniques, this paper highlights these challenges and sets the direction for future research in the UV imaging domain.

Improving Endoscopic Image Quality through the Use of High Dynamic Range Imaging-Like Method with Real-Time Performance.

High Dynamic Range (HDR) imaging is a digital image processing technique used to produce a wider range of brightness and color by using multiple captures of a scene taken with different exposures times. It enables capturing more details and producing a more natural-looking image with less washed-out highlights and deeper, more saturated colors. In medical endoscopy, HDR imaging enhances the visibility and clarity of images captured during endoscopy procedures. It provides enhanced visualization of subtler details in both dark cavities and bright areas, resulting in a uniformly exposed view and improved contrast among various tissue types. Standard HDR imaging methods are often complex and computationally demanding, making them unsuitable for performance-critical applications like endoscopy, where real-time performance is crucial. This paper introduces a more efficient and less complex method for achieving HDR-like image quality in real time. The method takes a high-pixel-bit-depth frame and generates multiple low-pixel-bit-depth frames and uses them to generate the high quality image. The focus of the paper is to enhance endoscopic image quality using HDR imaging, and the proposed method is demonstrated to be effective in achieving this goal with real-time performance. The method is implemented in the FPGA System-on-a-Chip (SoC) of a bronchoscope video processor system, and its effectiveness is verified through a simulated study using a phantom, which confirms the improved image quality and real-time performance.

Spatial-temporal mapping of forest vegetation cover changes along highways in Brunei using deep learning techniques and Sentinel-2 images

Infrastructure development is a leading driver of forest cover loss in the tropics, resulting in a significant decrease in biodiversity. With recent advancements in digital image processing and satellite imagery techniques, this study presents an approach for tracking forest cover alterations along a recently constructed highway in Brunei. Specifically, this paper harnesses the power of cutting-edge deep learning (DL) models; U-Net and Deeplabv3+, to classify forest and non-forest features from Sentinel-2 satellite images captured along the Telisai–Lumut highway between 2018 and 2022. A computational performance analysis and network comparison revealed that the Deeplabv3+ had a remarkable semantic segmentation performance, with a mean intersection over union of 0.9247, followed by an ensemble of four networks, with a mean intersection over union of 0.9171. The best model consistently outperformed other models in segmenting forest and non-forest features, with an intersection over union (IOU) of 0.9463 and 0.9031, respectively. Moreover, deploying hyperparameter optimization allowed us to assess the model's sensitivity to different hyperparameters for better results. Furthermore, forest change statistics were computed using the best model's generated forest change maps, and the results indicated a 16.7% gain in the overall forest vegetation cover along the highway, demonstrating the validity of the results. These reproducible findings will provide forest managers and ecologists with near real-time information on the status, health, and extent of forest ecosystems. This study's results have critical implications for using DL to monitor forest cover changes and will be of great interest to the scientific community as they open new avenues for future research and underline the need for continued innovation in this important field.