Digital Image Processing Methods Research Articles

Fractal study on the damage induced by shaped charge blasting with uncoupled eccentric charge

In this study, a dynamic caustic experimental system is used to examine the damage distribution characteristics as well as the crack formation/propagation mechanism in polymethyl methacrylate (PMMA) caused by uncoupled eccentric shaped charge blasting under different decoupling coefficients and charge positions. Using PMMA as the experimental material, the damage variables around the hole are calculated by the digital image processing method. The stress wave propagation law under different charge positions is revealed. Furthermore, based on the fractal theory, the damage values of shaped and unshaped cracks are calculated. The results show that the range of the crushed zone and failure zone on the coupled side around the hole of eccentric uncoupled charge is larger than that on the uncoupled side, which can reduce the damage on the reserved side and break the excavation side more fully. Under the condition of eccentric uncoupled charging, the range of the crack zone and failure zone decreases with the increase in the decoupling coefficient. When the decoupling coefficient is 2, the smoothness of the main crack is the best, and the total and average lengths of the main crack are also maximum. The damage distribution caused by explosive crack conforms to the fractal law, and the fractal dimension can accurately represent the damage degree of PMMA after the explosion.

IMPLEMENTASI DIGITAL IMAGE PROCESSING SEBAGAI PENDETEKSI POSISI MATAHARI PADA PERANCANGAN DUAL AXIS SOLAR TRACKER

One of the uses of new renewable energy (EBT) in Indonesia which has great potential is solar panels. Solar panels are electrical devices that are used to convert solar energy into electrical energy. The limited surface area of the panels requires users to be able to pay attention to the work efficiency of solar panels. The aspect that has the most influence on optimizing the efficiency of solar panel performance is the placement of the surface of the solar panel against incoming solar radiation. Therefore, it is necessary to have a method to detect the position of the sun to provide input to the actuator so that the surface of the solar panel is positioned in the direction of the sun's rays. This study describes the accuracy of detecting the position of the sun using digital image processing methods. This process needs to be done as a reference in designing a dual axis solar tracker. Detection of the sun's position using this method gets 100% accurate results in cloudless sky conditions and gets 80% success results in cloudy sky conditions.

Digital image processing of warm mix asphalt enriched with nanocolemanite and nanoulexite minerals

Digital image processing (DIP) is a method used to view the internal structure of hot mix asphalt. However, in this study, the DIP of warm mix asphalt (WMA), which reduces environmental emissions and saves energy, was been investigated. Firstly, colemanite and ulexite minerals, of which the global reserves are abundant, were ground in a ball mill and nano-sized particles were observed for the first time in TEM micrograph. Secondly, these minerals were added to the foamed WMA in varying amounts of micron/nano-size. Marshall stability and repeated creep tests were performed and the void ratios in the mixtures were analyzed. Thirdly, a DIP system was designed. The primary purpose of this method was to scrutinize the impacts of voids and bitumen ratios around aggregates and the adhesion effect between the bitumen and aggregate. Finally, the void ratios identified by these tests were utilized to compare with those specified by the DIP method. The best deformation resistance for the ulexite appeared to result in a 5 %U + 53 μ mixture, whereas the highest deformation resistance for the colemanite yielded in 5 %C − 25 μ and 10 %C − 25 μ mixtures, according to repeated creep test results. The samples' void ratios identified around the aggregates were the lowest (1.35%) in the 5 %U − 25 μ mixture and the highest (3.72%) in the 10 %U − 25 μ mixture. It was concluded that the designed DIP was quite good in the interpretation of WMA with nanocolemanite and nanoulexite additives.

Correlations between mineral composition and mechanical properties of granite using digital image processing and discrete element method

This study investigated the correlations between mechanical properties and mineralogy of granite using the digital image processing (DIP) and discrete element method (DEM). The results showed that the X-ray diffraction (XRD)-based DIP method effectively analyzed the mineral composition contents and spatial distributions of granite. During the particle flow code (PFC2D) model calibration phase, the numerical simulation exhibited that the uniaxial compressive strength (UCS) value, elastic modulus (E), and failure pattern of the granite specimen in the UCS test were comparable to the experiment. By establishing 351 sets of numerical models and exploring the impacts of mineral composition on the mechanical properties of granite, it indicated that there was no negative correlation between quartz and feldspar for UCS, tensile strength (σt), and E. In contrast, mica had a significant negative correlation for UCS, σt, and E. The presence of quartz increased the brittleness of granite, whereas the presence of mica and feldspar increased its ductility in UCS and direct tensile strength (DTS) tests. Varying contents of major mineral compositions in granite showed minor influence on the number of cracks in both UCS and DTS tests.

The Use of Digital Image Processing Method to Estimate the Foam Characteristics of Polyurethane

AbstractFour air‐bubbled polyurethane (PU) foams with different polyol:PMDI wt.% are produced, respectively. The chemical reaction mechanisms of polyurethane and bubble formation are proposed by performing standard Gibbs free energy calculations using the DFT M06‐2X/6‐31+G(d,p) method. The local minima, transition states, and intermediates in reaction mechanisms are detected. It is concluded that both reactions are exothermic. Then, raw images of the produced PU foams are taken with a 13 MP mobile phone camera, which can be considered inexpensive, and the mean radii of the pores are calculated by an image processing based method (IPBM) on a standard desktop computer with an i5 processor. It is determined that there is a close relationship between the calculated mean radius and instrumentally measured thermal conductivity coefficient of the foams. However, the thermal conductivity coefficients are independent of the calculated number and percentage of the pores. The mean radii of the samples calculated by the proposed IPBM are close to that of the SEM, with acceptable relative errors of less than 10%. Finally, it is concluded that IPBM, which is a more cost‐effective, cleaner, and faster method than SEM, might replace SEM in the air bubble analysis of PU foams.

Implementation of space imageries, remote sensing and GIS techniques in the geological and geomorphological analysis of Wadi Fatima drainage basin, Saudi Arabia

This research aims to apply various digital image processing methods of satellite imageries and remote sensing techniques in the geological and geomorphological analysis of Wadi Fatima area, Saudi Arabia. This area includes a sequence of volcano-sedimentary rocks of Arabian Shield and it is characterized by diagnostic drainage lines. Landsat-7 images and digital elevation model (DEM) in a 30 m resolution data have been used in this work. These data were also used in the geological interpretations, extracting morphometric parameters, and geomorphological analysis of Wadi Fatima drainage basin.The geological map of Wadi Fatima drainage basin was constructed and interpreted from the processed Landsat ETM + space imageries. The structural lineaments map of Wadi Fatima drainage basin was extracted from the digital data of panchromatic Landsat ETM + band. The geomorphological analysis identified, delineated and gave the main geomorphic characteristics of Wadi Fatima drainage basin. The statistical analysis of structural lineaments and the stream orders of Wadi Fatima basin showing that the main directional trends of both have a common trend in the directions of NE-SW, N-S & E-W. This means that the main wadies of Wadi Fatima drainage basin are structurally controlled and follow up the main faults and shear zones in the area. This study results in accurate delineation and constructive detailed geological and geomorphological aspect maps of Wadi Fatima basin. Wadi Fatima basin is less dangerous due to its rectangular shape than the circular shape, because the top of the flood reaches after a long time.

Detection of Early Blight Tomato Leaf Using k-Means Clustering

Early blight is one of the major diseases of tomatoes that affects the leaves and fruit quality. Detection and estimation of the disease severity are performed using the visual observation method. Visual detection requires significant time for visual inspection of a large cultivated area. Thus, image processing techniques have proven to be an effective method as compared to visual analysis. In this study, digital image processing methods and techniques were used to detect early blight of tomato (EBT), estimate the disease severity, and classify tomato leaves. Totally, 198 infected plants were randomly taken from the Haramaya University research site "Rare" at four different times. Diseased potato leaf images were captured, resized, and stored for experimentation. The stored images were processed using median filtering to remove noise while preserving useful features in an image and image enhancement. The RGB images were transformed to gray scale and CIELAB color space, and the k-means clustering was applied to estimate the disease severity of the potato leaves, and Otsu’s thresholding algorithm was applied to estimate the disease severity of both the detached and live leaves. MATLAB algorithms will be developed to determine the total area and infected lesion area of the leaf samples.

Detection of internal corrosion by long-pulse thermography and digital image processing

This paper addresses the problem of internal pitting corrosion in metals. The approach to solving this problem is carried out by means of long-pulse thermography (step heating) and digital image processing, using the median filter in the pre-processing and the Fourier transform in the processing. The long pulse thermography technique is an excellent way of detecting corrosion in metals. According to the state of the art, most of the works reviewed detect only the presence of external corrosion. In the experiments plates of different thickness were evaluated, one of 2 mm and another of 8 mm; this is another point in favor since most of the reviewed articles only focus on a single sample. In the development of this project, the following lines of research have been identified: implementation of new digital image processing methods, improvement of the thermography technique using other energy sources to heat the plates in a shorter time, exploring other functions of image processing in programming languages, carrying out tests with plates of different thickness and size, measuring the percentage of corrosion.

Application of Digital Image Processing Techniques to Detect Through-Thickness Crack in Hole Expansion Test

Advanced high-strength steels (AHSS) have become increasingly popular in the automotive industry due to their high yield and ultimate tensile strengths, enabling the production of lighter car body structures while meeting safety standards. However, they have some setbacks compared to conventional steels, such as edge cracking through sheet thickness caused by forming components with shear-cut edges. When characterizing the formability of sheet metal materials, the hole expansion test is an industry-standard method used to evaluate the stretch-flangeability of their edges. However, accurately visualizing the first cracking is usually tricky and may be subjective, often leading to inconsistent results and low reproducibility with some impact of the operator on both direct and post-processing measurements. To address these issues, a novel digital image processing method is presented to reduce operator reliance and enhance the accuracy and efficiency of the hole expansion test results. By leveraging advanced image processing algorithms, the proposed approach detects the appearance of the first edge cracks, enabling a more precise determination of the hole expansion ratio (HER). Furthermore, it provides valuable insights into the evolution of the hole diameter, allowing for a comprehensive understanding of the material behavior during the test. The proposed method was evaluated for different materials, and the corresponding HER values were compared with the traditional method.

Data processing pipeline for multiple-exposure photo-plate digital archives

Abstract Photo-plates are an invaluable historical legacy that have been used for over a hundred years to capture images of celestial objects. By digitizing these photo-plates and processing the images with digital image processing methods, scientists can study celestial objects that exhibit temporal variations. Multiple-exposure photo-plates are a unique type of observation data that can capture images of the same sky at different observation times in a single photo-plate. Such photo-plates can be used to discover flares or moving targets with rapid variations, but they are difficult to process automatically due to their complex observation strategies. This paper proposes a pipeline based on classical data-processing algorithms and machine-learning algorithms to detect flares or moving targets in multiple-exposure photo-plate images automatically. The pipeline was used to process several digitized multiple-exposure photo-plate images from the China Astronomical Plates Data, and preliminary results indicate that the pipeline is effective. In the future, we plan to use our method to discover more celestial objects with temporal variations from photo-plate digital archives.

In Vivo Laser-Induced Vasoactive Microenvironmental Setting via a Stimuli-Responsive Microstructured Depot.

A stimuli-responsive polymeric three-dimensional microstructured film (PTMF) is a 3D structure with an array of sealed chambers on its external surface. In this work, we demonstrate the use of PTMF as a laser-triggered stimulus-response system for local in vivo targeted blood vessels stimulation by vasoactive substances. The native vascular networks of the mouse mesentery were used as model tissues. Epinephrine and KCl were used as vasoactive agents that were sealed into individual chambers upon precipitation in the amount of pictograms. We demonstrated the method for non-damaged one-by-one chamber activation using a focused 532 nm laser light passed through biological tissues. To avoid laser-induced photothermal damage to biological tissues, the PTMF was functionalized with Nile Red dye, which effectively absorbs laser light. Chemically stimulated blood vessel fluctuations were analyzed using digital image processing methods. Hemodynamics changes were measured and visualized using the particle image velocimetry approach.

Study on a novel defrost control method based on the surface texture of evaporator image with gray-level cooccurrence matrix, new characterization parameter combination and machine learning

Defrost control using digital image processing is a potential, economical, and energy-saving solution for air-source heat pump or refrigeration system, comparing with currently studied or used direct/indirect measuring methods. However, under complex operating condition of different shooting angles, lighting conditions, and pixel level, which are common in refrigeration system, the accuracy of frost state recognition with existing digital image processing methods decrease dramatically. Therefore, a new method based on optimized gray level co-occurrence matrix and new characterization parameter combination to extract image texture characteristics, combining extreme learning machine algorithm (OGLCM-ELM), is proposed for the first time. An experimental rig for evaporator frosting image under different lighting intensity, shooting angle and pixel level is set up. The collected experimental image data are divided into 3 classifications (Frostless, light frost, and heavy frost) or 2 classifications ((Frostless + light frost, and heavy frost)). The results show for ternary classification, OGLCM-ELM reveals significantly higher recognition accuracy then existing methods, average accuracy is as higher as 98.14%. It is also 5.28% and 3.14% higher than those of OGLCM-SVM, OGLCM-BP. Other performance parameters, precision, recall, F1-score, and calculating time are also totally better than other methods. For binary classification. the average accuracy of OGLCM-ELM even reaches 99% under complex operating condition, indicating it is a practical and potential technology for defrost control.

Wetting and evaporative performance analysis of wet channels in indirect evaporative cooler with hydrophilic nano-coating

The wetting condition is an important factor that affects the cooling performance of an indirect evaporative cooler (IEC). Applying hydrophilic coatings to the wet channels is an effective way to enlarge the wetting area and enhance evaporative cooling. However, long-term hydrophilic durability tests for the coated surface are rarely discussed in existing studies. Moreover, the evaluation of performance improvement of a hydrophilic-coated IEC is usually based on testing the prototype as a whole, without giving attention to the internal wetting behavior inside the core. Therefore, a TiO2/SiO2 nano-coated polypropylene (PP) IEC is manufactured for studying the wetting and evaporative performance by considering the hydrophilic durability and internal wetting behavior with a specially-designed visualized experiment, aiming at promoting its application and guiding the design. Firstly, the microstructure, thermal conductivity, drop-surface characteristics and hydrophilic durability of the coated samples were studied. Secondly, the internal water distribution was studied under the influence of spray time (5 s ∼ 180 s), water flow rate (2 L/min ∼ 6 L/min), air velocity (2 m/s ∼ 3.5 m/s) and nozzle height (50 mm ∼ 250 mm) by combining fluorescence tracer and digital image processing methods. Lastly, the evaporative performance of the nano-coated IEC was tested under two spray strategies. Results show the hydrophilicity of polished nano-coated PP is durable in a dark and water-scouring environment with a steady contact angle of 30.9°. The coated core shows super hydrophilicity by forming a thin water film and being fully wetted within 60 s. Compared to the continuous spray, the intermittent spray can provide a larger evaporative mass transfer coefficient by 23.5% and a higher coefficient of performance by 114%.

Research on the particle characteristics of manufactured sands affecting the flow ability of fresh mortar

This paper combined digital image processing (DIP) and discrete element method (DEM) to investigate the relationship between the particle characteristics of manufactured sand and the flow ability of mortar, and clarified the key characteristics parameters of particle shape influencing the flow ability of mortar. A method for batch calculation of shape characteristic parameters (elongation ne, angularity index na and fullness ratio nf) of aggregates based on DIP was proposed. The fluidity tests and corresponding simulations of mortar at different water-cement ratios were carried out respectively, and the results showed that the errors of simulated fluidity (fm) were around 7%. The simulation results demonstrated that the fm decreased with increasing content of large-size aggregates in the mortar. The augment of both ne and na reduced the flowability of mortar. The magnitude of rolling/sliding friction coefficients of aggregates are the important parameters that determine the flowability of mortar.

Image Processing Approaches as a Diagnostic Parameter to Determine Pollution by Using Satellite Imagery, Northern Iraq

This work highlights the estimation of the Al-Khoser River water case that disposes of its waste directly into the Tigris River within Mosul city. Furthermore, the work studies the effects of environmental and climate change and the impact of pollution resulting from waste thrown into the Al-Khoser River over the years. Al-Khoser River is located in the Northern Mesopotamia of Mosul city. This study aims to detect the polluted water area and the polluted surrounding area. Temporal remote sensing data of different Landsat generations were considered in this work, specifically Enhanced Thematic Mapper Plus of 2000 and Operational Land Imager of 2015. The study aims to measure the amount of pollution in the study area over 15 years using a supervised classification approach and other tools in ERDAS Imagine Software version 2014. Supervised classification is favored for remote sensing data processing because it contains different digital image processing methods. It is noticed by applying to preprocess and post-processing techniques adopted in the polluted section of Al-Khoser River and monitoring the changes in the objects around it. Hence, the river’s water has been classified into clear water and contaminated water, which shows the impact of pollution over the years. The analysis detected a polluted area in the river that enlarged over the years 2000 to 2015 from 4.139 km² to 21.45 km², respectively. The study showed the differences in the size of objects around the river. The study concludes that daily wastes produced by the residential areas through which Al-Khoser and Tigris rivers pass would cause the polluted sections of the river to increase.

Video images compression method based on floating positional coding with an unequal codograms length

The subject of research in the article are the video images compression and encryption processes during the critically important objects managing process. The goal is to develop a method for compressing video images based on floating positional coding with an uneven codegrams length to simultaneously ensure information reliability and confidentiality during its transmission with a given time delay. Objectives: analyzing existing approaches to ensuring the video images confidentiality; development a method for compressing video images based on floating positional coding with an uneven codegrams length; evaluate the developed method effectiveness. The methods used are: digital image processing methods, digital image compression methods, image encryption and scrambling methods, structural-combinatorial coding methods, statistical analysis methods. The following results are obtained. The technology of floating encoding of an uneven sequence of blocks is proposed. Code values are formed from elements of different video image blocks. For this, a scheme for linearizing an image point coordinates from its four-dimensional representation on a plane into a one-dimensional element coordinate in a vector has been developed. The four-dimensional element coordinate on the plane describes the image block coordinates and the coordinates of the element in this block. Code values are formed under conditions of control their binary representation's length. At the same time, coding is implemented for an indeterminate number of video image elements. The number of elements depends on the length of the code word. Accordingly, codegrams with an indeterminate length are formed. Their length depends on the service data values, generated during the encoding process. Service data acts as a key element. Conclusions. The one-stage polyadic image encoding method in a differentiated basis has been further improved. The developed encoding method provides image compression without information quality loss. The original images volume compression provides by 3–20 % better compared to the TIFF data presentation format and by 4–15 % compared to the PNG format. The overhead amount is less than 2.5 % of the entire codestream size.

Deformation Behaviors Investigation of CoCr Alloy Lattice Structures under Compression Test

ith the development of the additive manufacturing method, the production of lattice structures with complex geometries attracts increasing attention. These lattice structures can be designed with the desired properties, and they are encountered in many areas such as automotive, aerospace and aviation, and manufacturing industries, as they offer the freedom to control their physical, mechanical and geometric properties. The high strength characteristic of lattice structures that can be designed at any scale makes these structures useful for producing different designs. Since the mechanical responses of the lattice structures depend on the lattice design parameters, such as the large number of independent struts forming the lattice, cell size and cell geometry, the mechanical behaviour of these structures should be examined. In this study, a porous lattice structure with four different cell models, namely Dode Medium, Diamond, Rhombic Dodecahedron, and Dode Thin, was produced by Selective Laser Melting (SLM) method. In order to reveal the mechanical properties and deformation responses of the porous lattice structures, they were analyzed under compression test and by the finite element method, and experimental and numerical procedures were compared. The effect of the compression test on the lattice properties and how the deformation is distributed throughout the lattice structure were investigated. The finite Element Analysis and Digital Image Processing (DIP) method was used to determine how the lattices deform. The results obtained will be useful for designing new metallic lattice structures with more excellent deformation resistance in future studies.

An Experimentally Validated Selection Protocol for Biochar as a Sustainable Component in Green Roofs

Green roofs contribute to more sustainable cities, but current commercial substrates suffer from important limitations. If carefully selected, biochar could serve as a viable option for a more sustainable green roof substrate. We propose a protocol to select an optimal biochar for green roof substrate amendment. Coffee husks, medium-density fiberboard, palm date fronds, and a mixture of waste wood, tree bark, and olive stone kernels are selected as residues for biochar production to develop a selection protocol. The residues are pyrolyzed at 350, 450, 500, and 550 °C in a lab-scale reactor. A pyrolysis temperature of 450 °C is selected for upscaling and is based on biochar yield, pH, salinity, and elemental composition. From evaluating the biochar characteristics after upscaling, it can be concluded that the biochar’s carbonization degree is mainly controlled by pyrolysis temperature, while yield, pH, and salinity are more dependent on the biomass properties. Ultimately, our procedure evaluates the presence of important contaminants, the biochar’s water holding capacity, salinity, pH, and carbonization degree. To validate the developed protocol, plant coverage experiments on green roofs are performed, which are quantified using a novel digital image processing method, demonstrating its efficient use to facilitate future biochar selection in substrates.

Underwater polarization imaging based on two-layer multi-index optimization

<sec>Underwater imaging is of great significance in exploring seabed resource , monitoring marine environment, implementing underwater rescue and military reconnaissance, etc. by providing clear vison. Among various underwater imaging techniques, the polarization imaging is considered to be an effective way to improve the quality of underwater imaging. It can realize underwater image restoration by using the difference in polarization characteristic between the target light and backscattered light. A classical underwater active polarization imaging method was presented by Treibitz [Treibitz T, Schechner Y Y <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://doi.org/10.1109/TPAMI.2008.85">2009 <i>IEEE Trans. Pattern Anal. Mach. Intell.</i> <b>31</b> 385</ext-link>], in which the degrees of linear polarization (DoLPs) of target light and backscattered light are used to recover clear image. A variety of improved methods have been derived from this, but most of them require background areas and human-computer interaction. Then, a new underwater active polarization imaging method without prior knowledge was presented by Zhao [Zhao Y, He W, Ren H, Li Y, Fu Y <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://doi.org/10.1016/j.optlaseng.2021.106777">2022 <i>Opt. Lasers Eng.</i> <b>148</b> 106777</ext-link>], in which the DoLPs of target light and backscattered light can be automatically obtained without background region. However, sometimes the above two parameters are very close and thus introduce a lot of noise into the restored images, for this method takes only the contrast into account.</sec><sec>In this work, an underwater active polarization imaging method based on two-layer multi-index optimization is proposed. First, the mutual information and contrast are taken as the upper objective functions, and the Pareto optimal solution set is obtained by the multi-objective genetic optimization algorithm. Second, the information entropy is taken as the lower objective function to obtain the optimal parameters from this optimal solution set. Based on the optimal parameters, the restored images are obtained. According to the difference between the DoLPs of target light and backscattered light, these restored images are further improved by the digital image processing method.</sec><sec>The experimental results indicate that our method can not only enhance image details effectively but also balance various evaluation indexes of the imaging quality to obtain high-quality restored images. The proposed algorithm is suitable for underwater targets with low and high DoLPs, with or without background regions.</sec>

A Deep Learning-Based Experiment on Forest Wildfire Detection in Machine Vision Course

As an interdisciplinary course, Machine Vision combines AI and digital image processing methods. This paper develops a comprehensive machine vision experiment on forest wildfire detection that organically integrates digital image processing, machine learning and deep learning technologies. Although the research on wildfire detection has made great progress, many experiments are not suitable for students to operate. Also, the detection with high accuracy is still a big challenge. In this paper, we divide the task of forest wildfire detection into two modules, which are wildfire image classification and wildfire region detection. We propose a novel wildfire image classification algorithm based on Reduce-VGGnet, and a wildfire region detection algorithm based on the optimized CNN with the combination of spatial and temporal features. The experimental results show that the proposed Reduce-VGGNet model can reach 91.20% in accuracy, and the optimized CNN model with the combination of spatial and temporal features can reach 97.35% in accuracy. Our framework is a novel way to combine research and teaching. It can achieve good detection performance and can be used as a comprehensive experiment for Machine Vision course, which can provide the support for talent cultivation in machine vision area.