Digital Image Processing Algorithms Research Articles

Automated identification of kinetics model in elastomer vulcanization using a rheometer MDR and Rubber-K pattern recognition software

This research developed a novel tool for the automatic characterization of elastomers during vulcanization. For this task, robust algorithms for digital image processing and pattern recognition related to curing kinetics were generated and integrated into Rubber-K software. Rheometer tests were performed under ASTM D2084 standard. Experimental data are imported in portable document format and fit the models established by Kamal-Ryan and Isayev-Deng, using a system of first-order ordinary differential equations. During the fitting of the experimental data, the reduced gradient method is used to minimize the error in the objective function. The models used in the algorithms allow higher accuracy and numerical stability compared to similar applications. Rubber-K software provides relevant information to the user during the injection molding parameterization process, allowing production optimization and significantly reducing non-conforming parts. In order to validate the results and accuracy of Rubber-K software, it was compared with data published by other software developers and similar models, whose findings confirm the power and applicability of the software.

Experimental study on the liquid film characteristics of annular flow in the helical rectangular channel

A detailed insight into the liquid film characteristics in the helical channel of single screw expander is of great significance for avoiding the leakage loss of higher pressure gas to lower pressure region. The liquid film thickness and fluctuation characteristics of the annular flow on the outer side of the helical rectangular channel is measured using the digital image processing algorithm, and they are evaluated under various flow rates, dimensionless curvature, and dimensionless pitch at different angle positions. Results show that increasing the Reg will decrease the instantaneous liquid film thickness, and a larger decrease in the liquid film fluctuation is found. A larger average liquid film thickness is located at the angle of 90° than that of the 180° and 270°. The instantaneous and average liquid film thicknesses are decreased with increasing the dimensionless curvature and dimensionless pitch at each angle position. Moreover, increasing the dimensionless curvature can weaken the effect of angle position on the liquid film thickness. The time distribution of the liquid film thickness changes dramatically in both wave height and amplitude as increasing the Rel at the smaller dimensionless pitch.

Reinforced concrete surface cracks length detection and length estimation by using digital image processing approach

Surface cracks are a common failure that occurs in reinforced concrete structures (RC). With the help of new technologies, access to crack properties should be easier and help the inspector to provide better results. However, most inspectors still prefer manual visual inspection approach, which leads to inconsistent results when investigating this flaw. Moreover, cracks with inconsistent shapes and irregularities are a difficult task for crack extraction, and inspectors overlook the details of cracks. Therefore, in this study, crack detection and thin crack appearance enhancement using various digital image processing algorithms (DSP) were proposed to improve the accuracy of crack length estimation. By using certain DSP on the captured crack images on RC, several algorithms were created and coded in MATLAB via the morphological approach to produce good quality of the original crack images. At the end of this study, the appearance of the thin crack was improved and helped to improve the estimate of the total length of the crack in pixels. The maximum percentage error between the estimated crack length was calculated and compared to the actual length and was 7.10 %. The surface crack detection algorithm and the has the potential as a helpful structural health monitoring (SHM) tool for crack inspection.

PLANT IDENTIFICATION USING MACHINE LEARNING

The preservation of therapeutic plants is crucial as it will help a broad variety of sectors, including medicine, botanic research, and plant taxonomy studies, among others. Existing technologies cannot imitate the range of therapeutic plant species present in India. The suggested technique facilitates the classification of medicinal plants by exploiting textural aspects crucial in leaf recognition and identification. The three key phases of the proposed technique are picture enhancement, feature extraction, and classification. The photographs of the leaves are shot using cellphones and then processed using digital image processing algorithms to extract the features that may be compared between them. Finally, the CNN classifier is employed to develop an automated classifier. Key Words: Plant identification, CNN algorithm, Image processing.

Remove ambiguous zones using floodfill algorithm in digital photoelasticity

Digital photoelasticity is a widely used technique for the full-field measurement of stress/strain distributions. However, ambiguous zones are present in the isochromatic phase map obtained with the commonly used ten-step phase shifting technique. Obtaining accurate isochromatic phase maps without wrapping can be challenging due to the presence of ambiguous zones. Existing methods to address this issue are complex and not fully automated. In this study, a novel approach is proposed to automatically and accurately remove the ambiguous zones using the Floodfill algorithm, an advanced digital image processing algorithm. The validity and robustness of the proposed method is demonstrated through benchmark tests on a disc and a ring subjected to diametral compression and a four-point bent beam, as well as a model with a complex geometry. The proposed method enhances the automation and simplicity of full-field stress/strain measurement in terms of the removal of ambiguous zones.

Influence of tool wear on chip-like burr formation during micro-milling, and image processing based measurement of inwardly-deflected burrs

During micro-milling, the regular top-burrs usually bend in a direction laterally opposite to the milled feature. This top-burr morphology changes as the cutting edge condition progressively deteriorates with inherent tool wear. An edge-chipped tool tends to produce unusual top-burr that resembles an incompletely separated chip. Such chip-like burr also has a tendency to flow inside the micro-milled feature. This article, for the first time, explores the integral roles of worn-out tool geometry, micro-milling mechanics and process parameters on chip-like burr formation on Ti–6Al–4V during machining using 0.5 mm diameter TiAlN-coated WC-6Co tools under minimum quantity lubrication. Round-edges of the coated micro-mill progressively pass through break-in wear, steady abrasive wear, adhesive wear, coating delamination, and edge-chipping. A comprehensive analysis is presented here to explore the mechanism of chip-like burr formation considering the changes in tool geometry and corresponding variations in chip-tool-work tribological contact, cutting forces, stress distribution, burr root thickness, burr nano-hardness and surface roughness. Further, an algorithm for digital image processing is developed for efficient and reliable measurement of the burr size and corresponding inwardly-bent fraction. Subsequently, the roles of process parameters are assessed to explore the scopes of controlling the inward bending of the chip-like burrs.

Morphological evolution of desiccation cracking in purple soil under the influence of aggregate size distribution

Desiccation cracking in purple soil subjected to rainfall-evaporation cycles is a common natural phenomenon, in the Three Gorges Reservoir area of China, that detrimentally affects the hydraulic and mechanical properties of soils. The significant spatial variability in the aggregate size distribution (ASD) of purple soil in this area leads to variations in crack morphology. In this study, we experimentally investigated the effects of ASD on morphological evolution of cracks and conducted a regression analysis based on the ASD indices of purple soil. The dynamic geometrical, topological, and statistical characteristics of the crack morphology were analysed using digital image processing and morphological algorithms. The results indicated that the process of crack evolution in purple soil mainly contained three stages, two intersection shapes, and three network forms. The crack morphology in soil with a larger initial mean aggregate size was more irregular and discontinuous. We proposed a cracking evolution function that depicts the crack geometry and topology considering the gravimetric water content as a variable, which proved to be a good fit for modeling the dynamic morphological characteristics of desiccation cracks. The distribution of crack length, crack area, and clod area were well described by the ExpDec function, whereas the crack width revealed a bimodal Gaussian distribution, with primary and secondary peaks of 1–1.5 and 2–3.5 mm, respectively. Moreover, Multiple linear regression models between the crack geometric parameters and ASD indices were established, which indicated that the ASD in the micro-aggregate state determines the final morphological characteristics of cracks, with the content of clay-sized aggregates having the most significant effect. The tensile stress of aggregates in the unsaturated state decreases with increasing radius, based on the microscopic mechanical analysis. These results could provides a theoretical basis for predicting the macroscopic cracking behavior of purple soil under various ASD conditions.

Design of a Low-Cost Diffuse Optical Mammography System for Biomedical Image Processing in Breast Cancer Diagnosis.

Worldwide, breast cancer is the most common type of cancer that mainly affects women. Several diagnosis techniques based on optical instrumentation and image analysis have been developed, and these are commonly used in conjunction with conventional diagnostic devices such as mammographs, ultrasound, and magnetic resonance imaging of the breast. The cost of using these instruments is increasing, and developing countries, whose deaths indices due to breast cancer are high, cannot access conventional diagnostic methods and have even less access to newer techniques. Other studies, based on the analysis of images acquired by traditional methods, require high resolutions and knowledge of the origin of the captures in order to avoid errors. For this reason, the design of a low-cost diffuse optical mammography system for biomedical image processing in breast cancer diagnosis is presented. The system combines the acquisition of breast tissue photographs, diffuse optical reflectance (as a biophotonics technique), and the processing of digital images for the study and diagnosis of breast cancer. The system was developed in the form of a medical examination table with a 638 nm red-light source, using light-emitted diode technology (LED) and a low-cost web camera for the acquisition of breast tissue images. The system is automatic, and its control, through a graphical user interface (GUI), saves costs and allows for the subsequent analysis of images using a digital image-processing algorithm. The results obtained allow for the possibility of planning in vivo measurements. In addition, the acquisition of images every 30° around the breast tissue could be used in future research in order to perform a three-dimensional (3D) reconstruction and an analysis of the captures through deep learning techniques. These could be combined with virtual, augmented, or mixed reality environments to predict the position of tumors, increase the likelihood of a correct medical diagnosis, and develop a training system for specialists. Furthermore, the system allows for the possibility to develop analysis of optical characterization for new phantom studies in breast cancer diagnosis through bioimaging techniques.

Grape-Bunch Identification and Location of Picking Points on Occluded Fruit Axis Based on YOLOv5-GAP

Due to the short fruit axis, many leaves, and complex background of grapes, most grape cluster axes are blocked from view, which increases robot positioning difficulty in harvesting. This study discussed the location method for picking points in the case of partial occlusion and proposed a grape cluster-detection algorithm “You Only Look Once v5-GAP” based on “You Only Look Once v5”. First, the Conv layer of the first layer of the YOLOv5 algorithm Backbone was changed to the Focus layer, then a convolution attention operation was performed on the first three C3 structures, the C3 structure layer was changed, and the Transformer in the Bottleneck module of the last layer of the C3 structure was used to reduce the computational amount and execute a better extraction of global feature information. Second, on the basis of bidirectional feature fusion, jump links were added and variable weights were used to strengthen the fusion of feature information for different resolutions. Then, the adaptive activation function was used to learn and decide whether neurons needed to be activated, such that the dynamic control of the network nonlinear degree was realized. Finally, the combination of a digital image processing algorithm and mathematical geometry was used to segment grape bunches identified by YOLOv5-GAP, and picking points were determined after finding centroid coordinates. Experimental results showed that the average precision of YOLOv5-GAP was 95.13%, which was 16.13%, 4.34%, and 2.35% higher than YOLOv4, YOLOv5, and YOLOv7 algorithms, respectively. The average positioning pixel error of the point was 6.3 pixels, which verified that the algorithm effectively detected grapes quickly and accurately.

A novel method incorporating digital image processing and A-star algorithm for soil-rock mixture (SRM) slope stability

A novel method incorporating digital image processing and A-star algorithm for soil-rock mixture (SRM) slope stability

A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces

Cracks seriously endanger the safe and stable operation of dams. It is important to detect surface cracks in a timely and accurate manner to ensure the safety and serviceability of a dam. The above-water crack detection technology of dams has been widely studied, but due to the complex underwater environment, above-water crack detection technology on dam surfaces cannot be directly applied to underwater crack detection. To adapt to the underwater detection environment and improve the efficiency and accuracy of underwater crack detection, many methods have been proposed for underwater crack detection, including sensor detection and image detection. This paper presents a systematic overview of the development and application practices of existing underwater crack detection technologies for concrete dams, focusing on methods that use underwater robots as underwater mobile carriers to acquire images that are combined with digital image processing algorithms to identify, locate, and quantify underwater cracks in dams. This method has been widely used for underwater crack detection on dam surfaces with the advantages of being non-contact, non-destructive, having high efficiency, and wide applicability. Finally, this paper looks further forward to the development trends and research challenges of detection technologies for underwater cracks on concrete dam surfaces, which will help researchers to complete further studies on underwater crack detection.

Irregular characteristic analysis of 3D particles—A novel virtual sieving technique

For digitizing the granular material systems, including asphalt mixture, the sieving size of the particles plays an irreplaceable role. However, the traditional methods based on equivalent conversion performed terribly in measuring the size of individual particles. This study combined digital image processing, ensemble learning, and over-sampling algorithms to develop a novel sieving method. Besides, the comprehensive importance ranking of variables was put forward to assist in optimizing the random forest. The results show that the data collection method proposed in this study can quickly gain bulks of digital aggregates without a complicated reconstruction process. The synthetic minority over-sampling algorithm was conducive to the accuracy ascension by balancing the database. By scoring variables' relative importance and function, the variable selection method can effectively reduce system complexity, thus improving computational efficiency and robustness. Based on the above-improving processes, the measuring precision of the aggregates' sieving opening was raised by over 20%.

Development of digital image processing algorithms based on the Winograd method in general form and analysis of their computational complexity

The fast increase of the amount of quantitative and qualitative characteristics of digital visual data calls for the improvement of the performance of modern image processing devices. This article proposes new algorithms for 2D digital image processing based on the Winograd method in a general form. An analysis of the obtained results showed that the use of the Winograd method reduces the computational complexity of image processing by up to 84% compared to the traditional direct digital filtering method depending on the filter parameters and image fragments, while not affecting the quality of image processing. The resulting Winograd method transformation matrices and the algorithms developed can be used in image processing systems to improve the performance of the modern microelectronic devices that carry out image denoising, compression, and pattern recognition. Research directions that show promise for further research include hardware implementation on a field-programmable gate array and application-specific integrated circuit, development of algorithms for digital image processing based on the Winograd method in a general form for a 1D wavelet filter bank and for stride convolution used in convolutional neural networks.

Measurement of nuclear fuel assembly’s bow from visual inspection’s video record

The bow of the nuclear fuel assembly is a well-known phenomenon. One of the vital criteria during the history of nuclear fuel development has been fuel assembly’s mechanical stability. Once present, the fuel assembly bow can lead to safety issues like excessive water gap and power redistribution or even incomplete rod insertion (IRI). The extensive bow can result in assembly handling and loading problems. This is why the fuel assembly’s bow is one of the most often controlled geometrical factors during periodic fuel inspections for VVER when compared e.g. to on-site fuel rod gap measurements or other instrumental measurements performed on-site. Our proposed screening method uses existing video records for fuel inspection. We establish video frames normalization and aggregation for the purposes of bow measurement. The whole process is done by digital image processing algorithms which analyze rotations of video frames, extract angles whose source is the fuel set torsion, and reconstruct torsion schema. This approach provides results comparable to the commonly utilized method. We tested this new approach in real operation on 19 fuel assemblies with different campaign numbers and designs, where the average deviation from other methods was less than 2 % on average. Due to the fact, that the method has not yet been validated during full scale measurements of the fuel inspection, the preliminary results stand for that we recommend this method as a complementary part of standard bow measurement procedures to increase measurement robustness, lower time consumption and preserve or increase accuracy. After completed validation it is expected that the proposed method allows standalone fuel assembly bow measurements.

Drilling-induced delamination measurement using a novel digital image processing algorithm

Drilling-induced delamination measurement using a novel digital image processing algorithm

Fast wettability assessment on small rock samples using a 3D, high-resolution, image-based Amott-like test

In this paper, we propose a new experimental set-up to carry out a full Amott-like cycle on small core samples (4 mm. diameter, 15 mm. length), imaged with a high-resolution micro-CT scanner. We conduct this experiment on two core samples from a carbonate reservoir. Each step of the cycle was followed by a 3D highresolution scan along the entire sample length and analysed using digital image-processing algorithms. The characterization of wettability is based on multiple criteria: a) a pseudo-wettability index, analogous to the Amott-Harvey index, calculated from both resolved and unresolved pores; b) experimental observations during the spontaneous phases, and c) analysis of the spatial fluid distribution in the pore space. This new type of experiment has several advantages: a) it respects the experimental sequence of the industry standard Amott test, yet with a significantly reduced test duration; b) it provides complementary information describing the spatial distribution of the fluids in the visible pore network during spontaneous and forced phases; c) it is less sensitive to imaging resolution than existing approaches based on topological characteristics and contact angles.

Automated Leukemia Screening and Sub-types Classification Using Deep Learning

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human body. It produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells (RBCs) and white blood cells (WBC), and platelets. Leukemia can be diagnosed manually by taking a complete blood count test of the patient’s blood, from which medical professionals can investigate the signs of leukemia cells. Furthermore, two other methods, microscopic inspection of blood smears and bone marrow aspiration, are also utilized while examining the patient for leukemia. However, all these methods are labor-intensive, slow, inaccurate, and require a lot of human experience and dedication. Different authors have proposed automated detection systems for leukemia diagnosis to overcome these limitations. They have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast cells. However, these systems are more efficient, reliable, and fast than previous manual diagnosing methods. However, more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class similarity. In this paper, we have proposed a robust automated system to diagnose leukemia and its sub-types. We have classified ALL into its sub-types based on FAB classification, i.e., L1, L2, and L3 types with better performance. We have achieved 96.06% accuracy for subtypes classification, which is better when compared with the state-of-the-art methodologies.

Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation.

Digital image processing techniques and algorithms have become a great tool to support medical experts in identifying, studying, diagnosing certain diseases. Image segmentation methods are of the most widely used techniques in this area simplifying image representation and analysis. During the last few decades, many approaches have been proposed for image segmentation, among which multilevel thresholding methods have shown better results than most other methods. Traditional statistical approaches such as the Otsu and the Kapur methods are the standard benchmark algorithms for automatic image thresholding. Such algorithms provide optimal results, yet they suffer from high computational costs when multilevel thresholding is required, which is considered as an optimization matter. In this work, the Harris hawks optimization technique is combined with Otsu's method to effectively reduce the required computational cost while maintaining optimal outcomes. The proposed approach is tested on a publicly available imaging datasets, including chest images with clinical and genomic correlates, and represents a rural COVID-19-positive (COVID-19-AR) population. According to various performance measures, the proposed approach can achieve a substantial decrease in the computational cost and the time to converge while maintaining a level of quality highly competitive with the Otsu method for the same threshold values.

Detecting the Same Pattern in Choreography Balinese Dance Using Convolutional Neural Network and Analysis Suffix Tree

The Balinese dances that are popular today were created by maestros who have existed since time immemorial. To develop the dances made by the existing maestro, one must know the characteristics of each dance based on the motion used. The help of digital image processing and string algorithm analysis methods will help to determine the characteristics of a dance. The algorithm used for dance analysis is the Suffix Tree, where the suffix tree is one of the algorithms that can be used to find patterns from input strings. The string to be analyzed is a series of codes performed by the classifier. The classifier used is Convolutional Neural Network. This method uses an image as its input, which will later perform convolution operations and perform a full-connected layer. The results were obtained using the Convolutional Neural Network method with Alexnet architecture as the classification and confusion matrix to calculate the level of accuracy of the test set, the best accuracy for the head is by using parameter learning rate 0.001, epoch 150, and RGB color space obtained 95% accuracy, 88% precision, 78% recall, and 82% f1-score. For the full body, using a learning rate of 0.01, epoch 150, and RGB color space, the accuracy is 85%, precision is 79%, recall is 64%, and f1-score is 69%. For the legs, using a learning rate of 0.001, epoch 150, and RGB color space, the accuracy is 92%, precision is 84%, recall is 59%, and f1-score is 65%. The results of the suffix tree analysis between codes that use ground truth and classification results have similar values, although the results of the movement patterns obtained by the suffix tree algorithm have not varied, which is dominated by class A because class A is the dominant class in each dance.

Feature Engineering for Microstructure–Property Mapping in Organic Photovoltaics

Linking the highly complex morphology of organic photovoltaic (OPV) thin films to their charge transport properties is critical for achieving high performance material systems that facilitate cost-efficient energy harvesting. In this paper, the current Materials Knowledge Systems (MKS) framework was extended so that it was able to establish reduced-order high-fidelity structure–property linkages for OPV films. Specifically, the following extensions were needed: (i) the proper application of digital image processing algorithms to identify the salient local material states in OPV microstructures controlling the charge transport phenomenon, (ii) computationally efficient feature engineering that not only utilized 2-point spatial correlations and principal component analysis, but also two new distance-based metrics, and (iii) the successful application of a localized version of the Gaussian process (laGP) together with an active learning Cohn (ALC) for building the desired surrogate models linking the OPV microstructures to their short-circuit currents. It is demonstrated that the extended MKS framework can produce high-fidelity structure–property linkages for OPV films.