Use Of Digital Image Processing Research Articles

Optical and Thermal Image Processing for Monitoring Rainfall Triggered Shallow Landslides: Insights from Analogue Laboratory Experiments

This study explores the innovative use of digital image processing (DIP) techniques, also named PhotoMonitoring, for analysing the triggering conditions of shallow landslides. The approach, based on the combination of optical and infrared thermographic imaging (IRT), was applied to a laboratory-scale slope, reproduced in a flume test apparatus. Three experiments were conducted to replicate rainfall-induced shallow landslides, applying change detection and digital image correlation analysis to both optical and thermal images. The method combines IRT’s ability to measure ground surface temperature changes with DIP’s capacity to track movement and displacement. Results showed the high reliability of the displacement time-series obtained through IRT-DIP with respect to the reference optical-DIP. The IRT-DIP technique also detects anomaly signals two minutes before landslide occurrence that can be regarded as a possible failure precursor. This study testifies to the potential of image analysis as a remote sensing technique, demonstrating the ability of DIP to capture the dynamics of shallow landslides, as well as the advantages of optical–IRT combinations to follow slope deformation processes during night-time. This approach, if properly adapted to real-scale scenarios, may contribute to a better understanding of landslide behaviour, improve landslide monitoring strategies, and promote more effective early warning systems (EWS).

National digital pathology projects in Switzerland: A2023 update.

The Swiss Digital Pathology Consortium (SDiPath) was founded in 2018 as aworking group of the Swiss Society for Pathology with the aim of networking, training, and promoting digital pathology (DP) at anational level. Since then, two national surveys have been carried out on the level of knowledge, dissemination, use, and needs in DP, which have resulted in clear fields of action. In addition to organizing symposia and workshops, national guidelines were drawn up and an initiative for anational DP platform actively codesigned. With the growing use of digital image processing and artificial intelligence tools, continuous monitoring, evaluation, and exchange of experiences will be pursued, along with best practices.

Bed joint reinforcement of shear walls using threaded titanium rods

Shear walls in historic constructions often require retrofit. The lateral load capacity is frequently insufficient to withstand seismic actions. Threaded titanium rods embedded in mortar bed joints have emerged as a viable solution to reinforce brickwork shear walls. The use of a high tensile strength and ductile material (titanium alloys), with a high degree of reversibility of the retrofit intervention, is promising as it represents a new opportunity in masonry conservation. Aiming at studying the effectiveness of the titanium rod repair, this research had a twofold goal: 1. laboratory tests were performed to investigate the structural response of reinforced walls at macro-scale level; 2. a numerical modelling strategy was implemented to fill the knowledge gap regarding the material properties of brickwork masonry (English and Flemish bond). Quasi-static cycling loading tests performed on full scale (1200x1200 mm, two wythes thickness) specimens demonstrated how bed joint reinforcement using threaded titanium rods produced a significant increase in the lateral load and deformation capacities. To provide an effective tool for predicting all material parameters, as input data, and for designing the proposed strengthening system, a step-by-step procedure - based on the use of digital image processing (DIP) techniques, homogenization theories and micro-modelling strategies - was therefore proposed.

Pieces Separation Applied to a RV-2AJ Industrial Manipulator

The objective of this work is to create a system for separating types of pieces for didactic purposes. An artificial vision system applied to a manipulator was used to perform this task of classifying and separating the pieces, through the use of digital image processing. The vision system uses a camera attached to the robot's end-effector, whose objective is to identify the area, perimeter and position of the centroid of the pieces to be separated. The separation of objects is performed between two classes: with round and non-round shapes. The tests were carried out with objects such as screws and nuts. After obtaining the positions of the objects to be separated by the vision system, some characteristics of these related to the reference system of the robot base are determined. Some difficulties were observed in the recognition of objects related to the colours and lighting conditions of the separation environment. To reduce these problems, black colour objects were used in order to reduce the reflectance. Another problem encountered in the separation process is related to the relative position of objects. In this case, two or more objects can be identified as a single object, depending on their relative positions. The system classifies with a rate of hits greater than 95%.

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.

MODCN: Fine-Tuned Deep Convolutional Neural Network with GAN Deployed to Forecast Diabetic Eye Damage in Fundus Retinal Images

Diabetic Retinopathy (DR) and Glaucoma are two of the most common causes of vision loss world-wide. However, it can be averted if therapy is begun early enough. In biomedical applications, the use of digital image processing has assisted in the automated identification of some ailments at an earlier stage. To make this prediction generally neural network classifier models were previously used, but these models have the drawback of being unable to detect multiple illnesses that occur in the eye at the same time and require a big database for successful classifier training. As a result, a model is needed to reliably distinguish DR and Glaucoma in diabetic individuals more accurately and with minimum dataset images. In this view, this study introduced Mayfly Optimized Deep Convolutional Network (MODCN) model for automated disease detection in the fundus retina images. In the MODCN model, the images are initially preprocessed, segmented at generator in the GAN model then a discriminator readily gives synthesis of real images of the fundus retina, thus a wide database has been created and considered as training images for the MODCN classifier. MODCN classifier has a modified high-density layer as a transition layer to avoid overfitting and the errors are minimized by tuning the hyperparameters using Mayfly Optimization Algorithm. After feature mapping, the classes normal, DR and Glaucoma are labeled and stored. At the testing stage, images are preprocessed, feature mapped and classified in the MODCN model. Thus, the proposed MODCN model detects multiple illness such as Diabetic Retinopathy and Glaucoma at the same time even with a small amount of database that performs a successful classifier training. This model is then evaluated and gives an accuracy of 99% that was higher compared to previous models.

Creating a digital platform with a deep neural network for detecting plant diseases using information technology

This article is devoted to the detection of plant diseases using a platform with a deep neural network using information technologies. The goal of the work is to create a publicly available platform for detecting plant diseases, which is based on a model of a deep neural network trained in 45 classes of 15 crops (apple, corn, blueberry, rice, cherry, grapes, peach, orange, bell pepper, potatoes, raspberries, soybeans, strawberries, tomato and tea). The use of digital image processing is proposed to detect diseases. The study of many plant species has shown that this method has a high potential for determining the yield and quality of plants and is superior to traditional methods. Based on the finished Plant Disease Expert image data set taken from Kaggle, an EfficientNetB3 model was created that showed impressive results in the average accuracy of determining plant diseases - 98.1%. The article is supplied with graphic materials and tables, as well as a detailed description of each stage of the study.

Research on Digital Preprocessing of Minority Costume Images

The development of minority traditional culture plays a very important role in the historical process of the development of our Chinese national culture. However, with the passage of time and the acceleration of the process of social modernization, our traditional minority culture is gradually disappearing. This makes more and more scholars begin to attach importance to the protection of traditional minority culture. As an important part of Minority minority culture, the focus of this paper is to protect and inherit the Minority minority costumes images through the use of digital image processing technology.By analyzing the current research status of the digitalization of minority costumes and the related technologies of the digitalization processing of minority costume images, this paper proposes that the digitalization pre-processing of minority costume images should be carried out before the construction of the minority costume resource database, which improves the construction process of the minority costume resource database. Through the research on the related technologies of the digital processing of minority costumes images, combined with the minority costumes resource database constructed in this paper to achieve the classification and recognition of minority costumes and target detection tasks. This paper mainly studies the image segmentation, scaling, edge detection and other technologies, and improves the relevant image processing technologies, completes the preprocessing of minority costumes images, and constructs a minority costumes resource library that can be used for the classification and recognition of minority costumes.

Modification of the weld penetration characteristics in laser deep welding by the use of instationary gas flows

The present report focuses on the integration of a technology for generating temporally alternating (pulsed) gas flows in the field of laser welding. The technical realization required the specific adaptation of the three core elements of the gas pulse system (valve, section of measurements, control system) to realize new parameters for laser welding. These parameters allow for a positive influence on the joining process and on the results of welding, respectively. By means of temporal control of the gas volume flow in combination with the laser welding process, it was possible to produce a force effect on the molten pool and subsequently to improve the characteristics of laser-welded seams. The effects occurring through the use of an instationary gas flow were evaluated by using the "classically" metallography and through the use of digital image processing. In parallel, the effect of a pulsating gas flow on the melting zone was simulated by using specific software in order to be able to make further statements about the effects.

A Secure Method for Authenticity Verification of Handwritten Signatures Through Digital Image Processing and Artificial Neural Networks

This paper aims to propose a method to do authenticity verification of handwritten signatures based on the use of digital image processing and artificial neural networks techniques through the backpropagation learning algorithm with 500 and 901 approaches, in order to optimize this verification process and act as a decision support tool, in an automated way. The results showed an average percentage error of 20% in the first and of 5.83% in the second, while the performance of a trained professional for that has an average error of 6.67%. Thus, we could observe the efficiency of the proposed method, as well as the difference and evolution of approaches through the relevance of the results.

Soil Shrinkage Characterization of Low Plasticity Soil Using Digital Image Analysis Process

The goal of this paper is to better understand the shrinkage behavior of low plasticity (CL) soil, which is common in Warangal, India. Understanding the tensile strength and hydro-mechanical response of soil requires an understanding of the contraction behavior of soil caused by drying during wet-dry moisture cycles. With the use of simple and reliable experimental approaches, the shrinkage mechanism and behavior with suction variation are characterized and described in this study. The findings are related to the change in suction, water content, and void ratio of soil that has been air-dried from complete saturation to dryness. The proposed framework uses two simple approaches to understand soil water retention during the drying phase: one to quantify the suction potential and the other to characterize the shrinkage mechanism. In addition, the use of digital image processing (DIP) to capture sample shrinkage using a simple experimental setup is thoroughly discussed. ImageJ software has been found to be feasible for DIP and to quantify the volume change of laboratory samples. The absence of well-structured soil phases or macropores is attributable to the presence of only normal shrinkage and zero shrinkage phases in the tested soil. In terms of water–retention behavior, the shrinkage curve, and the suction curve are very similar. The effects of capillary suction on the shrinkage response of low plastic soil have been observed, emphasizing the importance of characterizing soil shrinkage behavior using suction as a stress-state variable in volume change studies.

Avaliação das relações entre propriedades de forma de agregados, compactação, parâmetros do esqueleto mineral e textura de revestimentos asfálticos aeroportuários a partir do Processamento Digital de Imagens (PDI)

Este trabalho busca contribuir, na fase de projeto, para a previsão de macro e microtextura de misturas asfálticas aeroportuárias, relacionando-as com propriedades de forma dos agregados, métodos de compactação e parâmetros do esqueleto mineral. Para tanto, foi validado o uso do Processamento Digital de Imagens (PDI), a partir do equipamento AIMS2, cujas avaliações de macro e microtextura apresentam correspondência satisfatória com o ensaio de Mancha de Areia (MA) e a textura superficial da matriz pétrea, respectivamente. Observou-se que o aumento do Grau de Compactação (GC) e a utilização de rolos tandem provocam uma redução na macrotextura e uma melhoria nas características do esqueleto mineral. Além disso, a compactação em laboratório pode dar suporte à previsão da macrotextura em campo, porém a microtextura pode ser melhor prevista a partir da análise isolada da textura superficial dos agregados, sendo necessário para isso o desenvolvimento de correspondências entre classificações estatísticas e físicas.

Segmentation of Aggregate and Asphalt in Photographic Images of Pavements

Particle size distribution of aggregate in asphalt pavements is used for determining important characteristics like stiffness, durability, fatigue resistance, etc. Unfortunately, measuring this distribution requires a sieving process that cannot be done directly on the already mixed pavement. The use of digital image processing could facilitate this measurement, for which it is important to classify aggregate from asphalt in the image. This classification is difficult even for humans and much more for classical image segmentation algorithms. In this paper, an expert committee approach was used, including classical adaptive Otsu, k-means vector quantization over a set of 8 principal components obtained from 26 features, and a Gaussian mixture model whose parameters are estimated through the expectation-maximization algorithm. A novel cellular automata approach is used to coordinate these expert opinions. Finally, a simple heuristic is used to reduce sub- and over-segmentation. The segmentation results are comparable to those obtained by a human expert, while the sieve size of the segmented images corresponds very well with that obtained from the sieving process, validating the proposed method of segmentation. The results show that with the digital imaging procedure it was possible to detect particles with a size of 100 m with 90% of success with respect to time-consuming manual techniques. In addition, with these results it is possible to establish the homogeneity of the sample and the distribution of the particles within the asphalt mixture.

Sugarcane Harvesting Quality by Digital Image Processing

Sugarcane, a major crop in the Brazilian agribusiness sector, has had significant changes in its cultivation over the years. The harvest of sugarcane was usually carried out manually. However, due to the great demand for labours and high health hazards, manual harvesting was replaced by a mechanized process. Harvesters have a basal cutting mechanism composed of blades that are responsible for cutting the sugarcane. The blades must be continuously analysed and replaced if necessary. The analysis of these blades is performed qualitatively, in which the operator analyses the current conditions of the blades (based on prior experiences); or quantitatively, using a digital calliper to measure the thickness of the blade, wherein the increase in thickness indicates greater wear. Both approaches require the harvester to be stationary and are extremely time-consuming. In order to obtain more efficient results, the present study proposes a novel method to analyse blade wear and harvesting quality through the use of digital image processing. The change in geometric characteristics (area, perimeter, rectangularity, and length) of the blades over time was evaluated along with blade wear and harvesting quality indices (damage index to stalks). The obtained results indicated that the proposed methodology was effective at assessing the quality of the harvesting operation of sugarcane and the wear in the mechanisms of basal cutting present in sugarcane harvesters. The error in estimating the damage index ranged between 0.030 and 0.033%.

Fault diagnosis method of mechanical power system based on image processing technology

Fault detection and diagnosis become one of today’s hot spots, which describes that image information is an important form of fault information, it can quickly through the image processing technique, and can accurately extract the characteristic signal. This article selects the color of the particle image, the integrated use of digital image processing, pattern recognition theory, the characteristic parameters of tribology knowledge, as well as the extraction, optimization, and digital; verifies the feasibility of iron spectrum of abrasive fault recognition, and provides a new efficient ferrographic wear particle image recognition method. Firstly, the grindstone image of the original color diesel engine was preprocessed, and the grindstone image of the ferrograph was identified by directly selecting grindstone from the preprocessed ferrograph image and selecting the target grindstone. According to the two types of abrasive particles, the characteristic parameters were first classified, and then the values of the characteristic parameters were obtained through the training and learning of the sample abrasive particles. In view of the large number of characteristic parameters of ferro-spectrum abrasive particles, this article determined the characteristic parameters suitable for the identification of abrasive particles in this article through the feature optimization and proved the correctness of the identification of characteristic parameters of abrasive particles through the test.

Study of structure and morphology of surface layers formed on TRIP steel by the femtosecond laser treatment

Purpose: The purpose of the work is to demonstrate the possibility of using a femtosecond laser for forming surface layers with an adjustable microstructure on the surface of TRIP steel 03X13AG19, and processing the obtained images using digital complexes. Design/methodology/approach: A laser treatment of TRIP steel (03X13AG19) with pulses of femtosecond duration was carried out in a melting mode. The source of the radiation is a femtosecond titanium-sapphire Ti:Al2O3 complex consisting of a predefining femtosecond generator “Mira Optima 900-F” and regenerative amplifier Legend F-1K-HE. Peculiarities of the surface structure of irradiated samples were studied using a Solver P47-PRO atomic force microscope. The structural-geometric parameters of the surface of the investigated steel treated with the femtosecond laser were determined using the software package Nova 1.0.26.1443 and the functions of the Image Analysis. Microstructural analysis was performed using a raster electron microscope JSM 6700F and a METAM-1P microscope. In this work, the digitization of images of microstructures obtained as a result of surface irradiation by highly concentrated energy streams of femtosecond duration has been carried out. The analysis of the surface structure of laser-structured materials was carried out using a metallographic complex with the software ImProcQCV. Findings: It has been revealed that the predetermined change of the laser treatment mode changes the microrelief and the shape and size of the fragments of the surface structure of the investigated steel. The use of digital image processing allowed to generalize the morphological features of the surface structure, to assess in detail the character of the microrelief, and to monitor under in-situ mode the structure and properties of the surface of the material being studied. Research limitations/implications: The obtained research results can be applied to stainless steels of various structural classes. Practical implications: Surface digitization significantly reduces the time for research, improves the quality and accuracy of the data obtained, makes it possible to conduct in-situ researches with the further implementation of the results using the Internet of Things technologies. Originality/value: A comprehensive approach is proposed for the estimation of parameters of laser-induced periodic surface structures (LIPSS) using a metallographic complex with the software ImProcQCV.

Embedded Linux System for Digital Image Recognition using Internet of Things

The present paper describes the use of Digital Image Processing and Internet of Things for gestures recognition using depth sensors available on Kinect device. Using the open source libraries OpenCV and libfreenect, image data are translated and used for communicating Raspberry Pi embedded Linux system with PIC microcontroller board for peripheral devices controlling. A LED is triggered according to the hand gesture representing the corresponding number. Data are stored in a PHP Apache server running locally on Raspberry Pi. The proposed system can be used as a multifunctional tool in areas such as learning process, post-traumatic rehabilitation and visual and motor cognition time. Using image binarization and Naive-Bayes classifier, the achieved results show error lower than 5%.

Use of Remote Sensing and Geographic Information System for the Classification of Agricultural Land Uses and Land Cover in the Al-Sad Al-Adhim sub District – Iraq

Land use refers to the human activity associated with a particular area of land. The land cover refers to the pattern of appearances located on the surface of the earth. Survey, inventory, monitoring and classification of land use and land cover are a fundamental step in the land use planning process, in evaluating and comparing alternatives and in choosing the best and sustainable use of land for development, accomplishment economic and social well-being. Remote sensing and Geographic Information System provided advantages that conventional methods could not provide for surveys and monitoring of natural and human resources, and classification of agricultural land uses and land cover in the area of the Al-Sad Al-Adhim sub District – Iraq. Depending on the Anderson system and others to classify land uses and land cover, through the integration of digital interpretation with the use of Digital Image Processing (ERDAS IMAGINE) software, and visual interpretation using ArcGIS software. Classification of agricultural land use and land cover up to the third level, with over all accuracy of the map 90%. the percentage distribution of the areas shows that the agricultural lands ranked first and occupy 52%, then grassland occupies 19%, barren land is occupied 17%, urban areas and built up occupy 9% water is ranked last occupy 3% of the total area of the study area.

Landmark of the past in the Antequera megalithic landscape: A multi-disciplinary approach to the Matacabras rock art shelter

The background of this paper is the biographical relationship between the Menga dolmen and La Peña de los Enamorados mountain (a conspicuous and highly-recognisable natural formation), both part of the Antequera megalithic landscape. Our main aim is to provide a high-resolution characterisation of the Matacabras rock art shelter, located on the northern side of La Peña de los Enamorados. This is achieved through a photogrammetric topographic survey, a detailed assessment of the graphic motifs identified through the use of digital image processing and various types of physical and chemical analysis, a geo-chemical characterisation of pottery found on its surface, and a comparative stylistic analysis of its motifs. Our study suggests that Matacabras (and the site of Piedras Blancas I, located just below it), played an important role in the genesis of Menga, which perhaps makes it the most important rock art location of Spanish Late Prehistory.

Quantitative Visualisation of Compressible Flows

Abstract The paper demonstrates the feasibility of quantitative flow visualisation methods for investigation of transonic and supersonic flows. Two methods and their application for retrieving compressible flow field properties has been described: Background Oriented Schlieren (BOS) and Particle Image Velocimetry (PIV). Recently introduced BOS technique extends the capabilities of classical Schlieren technique by use of digital image processing and allow to measure density gradients field. In the presented paper a review of applications of BOS technique has been presented. The PIV is well established technique for whole field velocity measurements. This paper presents application of PIV for determination of the shock wave position above airfoil in transonic flow regime. The study showed that application of quantitative flow visualisation techniques allows to gain new insights on the complex phenomenon of supersonic and transonic flow over airfoils like shock-boundary layer interaction and shock induced flow separation.