Digital Image Processing Research Articles

Review on graph theory-based image segmentation with its methods

In areas of digital image processing and computer vision, image segmentation is defined as a crucial process that divides an image into many segments for more straightforward and accurate object analysis. Making use of graph-based techniques as an effective tool for segmenting images has drawn more consideration recently. Since graph-based techniques are attractive and increasingly prevalent and can designate image properties, in this article, some of the primary graph-based techniques have been presented. This scheme utilizes graph theory to create a graph depiction of an image in which each pixel is represented as a node and the edges show the degree of similarity between two pixels. When items are represented by vertices and an edge connects them, a graph may be used to depict the relationship between them. To divide a graph into sub-graphs that reflect significant items of interest, this study explores some graph theoretical approaches for image segmentation, including minimum spanning tree, pyramid-based, graph cut-based, and interactive image segmentation and their employing in significant image processing fields such as medical image analysis for infection diagnosis, and remote sensing.

An eco-friendly approach for analysing sugars, minerals, and colour in brown sugar using digital image processing and machine learning

Brown sugar is a natural sweetener obtained by thermal processing, with interesting nutritional characteristics. However, it has significant sensory variability, which directly affects product quality and consumer choice. Therefore, developing rapid methods for its quality control is desirable. This work proposes a fast, environmentally friendly, and accurate method for the simultaneous analysis of sucrose, reducing sugars, minerals and ICUMSA colour in brown sugar, using an innovative strategy that combines digital image processing acquired by smartphone cell with machine learning. Data extracted from the digital images, as well as experimentally determined contents of the physicochemical characteristics and elemental profile were the variables adopted for building predictive regression models by applying the kNN algorithm. The models achieved the highest predictive capacity for the Ca, ICUMSA colour, Fe and Zn, with coefficients of determination (R2) ≥ 92.33 %. Lower R2 values were observed for sucrose (81.16 %), reducing sugars (85.67 %), Mn (83.36 %) and Mg (86.97 %). Low data dispersion was found for all the predictive models generated (RMSE < 0.235). The AGREE Metric assessed the green profile and determined that the proposed approach is superior in relation to conventional methods because it avoids the use of solvents and toxic reagents, consumes minimal energy, produces no toxic waste, and is safer for analysts. The combination of digital image processing (DIP) and the kNN algorithm provides a fast, non-invasive and sustainable analytical approach. It streamlines and improves quality control of brown sugar, enabling the production of sweeteners that meet consumer demands and industry standards.

Damage evolution in asphalt mixtures based on in-situ CT scanning

A novel approach utilizing in-situ Computed Tomography (CT) scanning and digital image processing techniques was developed to overcome the limitations of traditional CT image analysis in studying microcrack evolution in asphalt mixtures. Two types of graded asphalt mixtures (continuous gradation/AC and intermittent gradation/SMA) were scanned in-situ to capture internal structural changes. Digital image processing distinguished primary voids from cracks and extracted crack evolution. Multivariate regression analysis linked crack parameters to compressive strain. The study found that crack evolution during uniaxial compression in asphalt mixtures can be divided into densification, linear elastic deformation, and nonlinear failure stages. As load increases, the number, volume, and void percentage occupied by cracks rise, peak, and then decrease due to merging and engulfing after the ultimate load. The skeletal structure of cracks was obtained, and a damage evolution equation was established, linking crack parameters to damage evolution. Crack tortuosity was used to characterize the resistance effect of different gradations on crack propagation. This methodology provides new insights and technical support for studying damage evolution and understanding the failure mechanisms of asphalt mixtures.

Effectiveness of an X-ray shielding sheet with lower shielding ability to enable both bone mineral density determination and morphological diagnosis

Bone Mineral Density (BMD) can be determined by applying the Digital Image Processing (DIP) method using medical X-ray diagnosis. Although only the second metacarpal bone is analyzed in this approach, other parts of the body are exposed to X-ray radiation. We here propose a novel procedure in which parts of the hand surrounding the area of interest are shielded with an X-ray shielding sheet having low shielding performance. In our procedure, the main diagnostic area is not shielded, and other areas are covered with an X-ray shielding sheet with a low shielding performance. The sheet was fabricated by embedding Bi2O3 particles in resin sheet. We assessed the clinical performance of this method using three types of hand phantoms and conventional diagnostic X-ray equipment. The dose reduction for the entire hand region was evaluated by the Dose Area Product (DAPHand), which was measured with a small dosimeter, and the hand area was determined from the X-ray image. The X-ray image of the second metacarpal bone is affected by the contribution of X-rays that penetrate the object of interest and are scattered in other areas of the hand. Because our X-ray shielding sheet suppressed the generation of scattered X-rays, the pixel value of the second metacarpal bone and corresponding BMD value are varied. To address this issue, we developed a correction algorithm. We found that the X-ray shields with Dose Reduction Factor (DRF) values of 40–60% were appropriate for our methodology. Our method was estimated to have a percentage uncertainty of approximately 5% for the derivation of BMD values. Morphological information of the hand and bones could thus be clearly observed. We verified that both morphological diagnosis and quantitative determination of BMD are possible when DIP procedure is conducted using our shield.

Comparison of a conventional image processing approach with an artificial neural network approach to three‐dimensionally trace multiple particles in dynamic x‐ray microtomography experiments under laboratory conditions

AbstractThis paper compares a classic digital image processing approach to trace particles in laboratory x‐ray micro‐computed tomography (µCT), which is based on a combination of random sample consensus (RANSAC) algorithm and least squares ellipse fitting (LSF), with an approach based on artificial neural networks (ANNs). In order to be able to perform the comparison, dynamic experiments were carried out in a laboratory microtomography facility. During active scans with a duration of 30–75 s several sedimentation experiments have been carried out with an exposure time of 0.13 s/projection. Through the movement of the particles during the scan curved motion artefacts in the reconstructed data occur, where the vertex marks the coordinate of the particle. It could be shown that both approaches enable the tracing of particles in laboratory x‐ray µCT with deviations from a manually evaluated result of exemplarily 1.25% for the conventional digital image processing (CDIP) and 0.48% for the ANN. It was found that ANNs are able to identify particle positions in non‐symmetrical motion artefacts, appearing around the first and last position of the particles in the scan, allowing an extension of the motion range of the particles that can be evaluated. Both methods have their advantages and disadvantages. Due to the high complexity and size as well as partly black box structures of neural networks, they are not 100% comprehensible whereas conventional image processing is 100% transparent and understandable. But because of the complexity of the tracing of particles, the CDIP code offers many parameters that can be set, which is why the application is therefore slightly more complex.

Sonine filters and their applications in fringe pattern analysis

This work aims to define Sonine filters and introduce them for digital image processing. Their fundamental properties are described and an optimizing performance procedure is explained, exploiting the unique advantage that this kind of filters possess, which consists of having simple analytical finite Fourier transforms for circular, elliptical and rectangular domains. A comparison with various existing filters is also presented, like Gaussian, Hann, Hamming, Blackmann, etc. Some relations are provided to facilitate their applicability. Also, comprehensive optimized filter designs are provided. Finally, an experimental application for intensity normalization and noise reduction of fringe patterns is described, showing that this kind of filters could be helpful in digital image processing and particularly in the analysis of fringe patterns.

Digital image processing for preliminary detection of infected porang (Amorphophallus muelleri) seedlings

Digital image processing for preliminary detection of infected porang (Amorphophallus muelleri) seedlings

Morphology characteristics of filler particles and their effects on the low–temperature cracking behavior of asphalt mastics

The article concerned on particle morphology of mineral filler and its impact on mastic cracking property at low temperatures. Limestone and granite fillers with four-sized groups were prepared and further investigated using a simplex-centroid experimental design. The particle morphologies of each filler were captured using a digital image processing. By applying bending beam rheometer tests, mastic cracking behavior was investigated at low temperatures. Results show that compared with granite filler, each limestone filler shows less regular contour shape (except l1), less abundant angularity (except l1, l2, l5 and l17) and surface texture when performed by a simplex-centroid experimental design. The established fractional derivative Burgers model considering creep damage can effectively describe mastic cracking behavior at low temperatures. Composite fractal dimension is the decisively relevant factor with mastic relaxation behavior. Moreover, mastic creep damage is obviously affected by composite morphology characteristics and filler lithology with increasing loading time. All the results are instructive to inform researches of the selection of mineral filler with appropriate particle morphology that will produce asphalt materials with desired performance.

Fractional fourier transform and its application

The Fourier Transform (FT) is a linear transformation for the primitive function. It takes some set of functions to be an orthogonal basis. Its physical meaning is to transfer the primitive function onto each set of base functions. Because it can convert functions between the time and frequency domains, the FT is widely employed in many fields. The Fractional Fourier Transform (FrFT) is an improvement and progress based on the FT. This paper will define the FT and FrFT. Then the distinction between FrFT and FT is discussed. Finally, specific examples of its application in processing digital image are provided. FrFT is the process of transforming an image function into a series of periodic functions. The FrFT is used as a powerful mathematical tool to understand non- smooth signals, nonlinear systems and complicated phenomena, which is significant and has broad possibilities in the fields of signal processing, communication, image processing, optical imaging and quantum information processing.

Image Analysis Techniques Applied in the Drilling of a Carbon Fibre Reinforced Polymer and Aluminium Multi-Material to Assess the Delamination Damage

Due to the high abrasiveness and anisotropic nature of composites, along with the need to machine different materials at the same time, drilling multi-materials is a difficult task, and usually results in material damage, such as uncut fibres and delamination, hindering hole functionality and reliability. Image processing and analysis algorithms can be developed to effectively assess such damage, allowing for the calculation of delamination factors essential to the quality control of hole inspection in composite materials. In this study, a digital image processing and analysis algorithm was developed in Python to perform the delamination evaluation of drilled holes on a carbon fibre reinforced polymer (CFRP) and aluminium (Al) multi-material. This algorithm was designed to overcome several limitations often found in other algorithms developed with similar purposes, which frequently lead to user mistakes and incorrect results. The new algorithm is easy to use and, without requiring manual pre-editing of the input images, is fully automatic, provides more complete and reliable results (such as the delamination factor), and is a free-of-charge software. For example, the delamination factors of two drilled holes were calculated using the new algorithm and one previously developed in Matlab. Using the previous Matlab algorithm, the delamination factors of the two holes were 1.380 and 2.563, respectively, and using the new Python algorithm, the results were equal to 3.957 and 3.383, respectively. The Python results were more trustworthy, as the first hole had a higher delamination area, so its factor should be higher than that of the second one.

New geographic distribution record of Dactylogyrus labei Musselius and Gusev (1976) (Platyhelminthes, Monogenea) of Labeo rohita (Teleostei: Cyprinidae) from Mizoram, northeast India

Dactylogyrus labei is a dactylogyrid monogenean parasite originally described by Musselius and Gusev (1976) from Labeo rohita (Hamilton, 1822) (Teleostei: Cyprinidae) in two alternative forms: a typical from Lucknow, north India and an atypical from Kalyani, east India. Fresh specimens of D. labei were collected from Mizoram in northeast India, and examined morphologically using high-resolution digital image processing and analysis. The recovered monogeneans most closely resembled the atypical form of D. labei, with only minor differences, indicating conspecificity. The atypical form of D. labei is distinguished from other species of the genus by the following combination of characters: the dorsal bar wing-shaped, with a fine membrane on the anterior margin and a fenestration in the middle; the ventral bar casing nail-shaped with two small antero-lateral processes (3-rayed); the male copulatory tube loosely S-shaped, slender, with an inflated initial part; the accessory piece proximally tubular, distally a complex of multi-layered sheath guiding the copulatory tube, and the vagina tubular, with one small loop and vaginal pore surrounded with a flap-like structure. This study expands the geographical distribution of D. labei while improving its morphological descriptions.

Characterizing Splitting Failure of Concrete Influenced by Material Heterogeneity Based on Digital Image Processing Techniques

Concrete, as a composite material, is subject to heterogeneity in its mechanical properties and damage characteristics responding to load. In this paper, a numerical approach for analyzing the heterogeneous characteristics and the mechanical behavior of concrete specimens in tensile splitting tests using DIP techniques is introduced. The experiment involves the preparation of three types of concrete specimens with different strengths and performances of the tensile splitting test. The contour and position information of the different components in the split surface of a concrete specimen are reflected in the numerical model using the DIP techniques and the fracture of the split surface is realized by three types of cohesive elements in the finite element software ABAQUS. The results of the proposed numerical model are highly consistent with the experimental results with a maximum error of 4.77%, whereby the evolution of the splitting process is discussed. The simulation shows that the concrete fracture develops from the periphery towards the center of the concrete and the ITZ region splits first at similar strain levels, followed by the mortar region and finally the aggregate region. In addition, a simplified modeling scheme with faster computational efficiency and higher accuracy is proposed, which indicates that the shape of the heterogeneous components in concrete has a low effect on mechanical strength. The proposed model can accurately reflect the splitting fracture process of concrete which is instantaneous in the actual process, contributing to the understanding of the mechanism of the splitting fracture process and proposing a new methodology for simulating the fracture process of heterogeneous materials (e.g., concrete, rock). This work contributes to the understanding of the effect of material heterogeneity on concrete’s mechanical behavior and fracturing process and provides valuable hints for the research on the non-destructive prediction of concrete strength.

Proposition of an aggregate-binder interface index based on the adhesiveness test assessed by digital image processing

Aggregate-binder adhesiveness impacts asphalt mixture performance. Evaluation of this interface within mixes typically involves visual assessment and digital image processing (DIP), often resulting in an average value for this complex property. Current computational simulations overlook the possibility of incomplete aggregate adhesion to the binder, a common occurrence in cases of adhesive/cohesive failure between materials. This study proposes an interface index determined from adhesiveness test results in order to improve computational simulations. For this purpose, twelve adhesiveness tests, following Brazilian standards, including mineral and steel-slag aggregates, and neat and modified binders, were conducted. Software with a graphical user interface (GUI) was developed to quantify the percentage of aggregate area coated by binder (%Coated) post-testing. Descriptive statistics and probability distributions were applied to %Coated values, in addition to the quality of measurements using DIP. The DIP values exhibited sensitivity across all examined covering scenarios, with statistical tests suggesting a high likelihood of accurately representing the %Coated parameter from the assessed probability distributions. In conclusion, the developed software enables satisfactory quantification of %Coated. Furthermore, this study indicates that the adhesion test results must be represented considering the data variability, with the Beta distribution being a good way to represent the behavior.

Phenotypic characteristics of the mycelium of Pleurotus geesteranus using image recognition technology.

Phenotypic analysis has significant potential for aiding breeding efforts. However, there is a notable lack of studies utilizing phenotypic analysis in the field of edible fungi. Pleurotus geesteranus is a lucrative edible fungus with significant market demand and substantial industrial output, and early-stage phenotypic analysis of Pleurotus geesteranus is imperative during its breeding process. This study utilizes image recognition technology to investigate the phenotypic features of the mycelium of P. geesteranus. We aim to establish the relations between these phenotypic characteristics and mycelial quality. Four groups of mycelia, namely, the non-degraded and degraded mycelium and the 5th and 14th subcultures, are used as image sources. Two categories of phenotypic metrics, outline and texture, are quantitatively calculated and analyzed. In the outline features of the mycelium, five indexes, namely, mycelial perimeter, radius, area, growth rate, and change speed, are proposed to demonstrate mycelial growth. In the texture features of the mycelium, five indexes, namely, mycelial coverage, roundness, groove depth, density, and density change, are studied to analyze the phenotypic characteristics of the mycelium. Moreover, we also compared the cellulase and laccase activities of the mycelium and found that cellulase level was consistent with the phenotypic indices of the mycelium, which further verified the accuracy of digital image processing technology in analyzing the phenotypic characteristics of the mycelium. The results indicate that there are significant differences in these 10 phenotypic characteristic indices ( ), elucidating a close relationship between phenotypic characteristics and mycelial quality. This conclusion facilitates rapid and accurate strain selection in the early breeding stage of P. geesteranus.

Zigzag persistence for image processing: New software and applications

Topological image analysis is a powerful tool for understanding the structure and topology of images, being persistent homology one of its most popular methods. However, persistent homology requires a chain of inclusions of topological spaces, which can be challenging for digital images. In this article, we explore the use of zigzag persistence, a recent variant of traditional persistence, for digital image processing. To this end, new algorithms are developed to build a simplicial complex associated to a digital image and to compute the relationships between homology classes of a sequence of binary images via zigzag persistence. Additionally, we provide a simple software to use them. We demonstrate its effectiveness by applying it to a real-world problem of analyzing honey bee sperm videos.

Formation of a panoramic image of the inner surface of the pipe

Background. Visual non-destructive testing of the inner surface of pipes is an important aspect in their production and operation. A defect detected and corrected in a timely manner can significantly reduce the number of defects in production and prevent various emergency incidents during operation. Formation of a complete panoramic image of the inner surface of pipes suitable for quality analysis is an urgent and sought-after task that can be solved using computer vision systems. Aim. This work is the research and development of television methods for forming a complete panoramic image of the inner surface of a pipe, which can be analyzed to search for defects. Methods. To form a cylindrical panoramic image, mathematical models for the formation of an equidistant projection of spherical images obtained using a fisheye lens were used. For high-quality stitching of the resulting frames, digital image processing methods were used, including brightness and contrast transformations, and searching for special points using the MSER algorithm. Theoretical results are verified by full-scale modeling. Results. The result of this work is an algorithm for stitching frames of a video sequence generated by a television camera with a fisheye optical system, uniformly moved along the longitudinal axis of the pipe, into a single panoramic image of the internal surface. Conclusion. The algorithm ensures the formation of a high-quality image of a full panorama of the inner surface of the pipes with the absence of brightness artifacts.

A comparison among a fuzzy algorithm for image rescaling with other methods of digital image processing

The aim of this paper is to compare the fuzzy-type algorithm for image rescaling introduced by Jurio et al., 2011, quoted in the list of references, with some other existing algorithms such as the classical bicubic algorithm and the sampling Kantorovich (SK) one. Note that the SK algorithm is a recent tool for image rescaling and enhancement that has been revealed to be useful in several applications to real world problems, while the bicubic algorithm is widely known in the literature. A comparison among the abovementioned algorithms (all implemented in the MatLab programming language) was performed in terms of suitable similarity indices such as the Peak-Signal-to-Noise-Ratio (PSNR) and the likelihood index $S$.

Research on Detection Technology of Micro- Components on Circuit Board Based on Digital Image Processing

Aiming at the stability of the circuit board image in the acquisition process, this paper realizes the accurate registration of the image to be registered and the standard image based on the SIFT feature operator and RANSAC algorithm. The device detection model and data set are established based on Faster RCNN. Finally, the number of training was continuously optimized, and when the loss function of Faster RCNN converged, the identification result of the device was obtained.

Artificial intelligence approach to predict ladle aluminium content and to protect its refractory lining

ABSTRACT This research focuses on optimising steelmaking processes, specifically in the Basic Oxygen Furnace (BOF) and ladle operations, with an emphasis on ladle basicity and Aluminium dosage for deoxidation. The steelmaking process, conducted in the Basic Oxygen Furnace, involves oxidation, flux addition, and tapping of purified steel into ladles. Maintaining desired ladle basicity is crucial for high-quality steel production, and deoxidation with Aluminium is essential to prevent Ferro Alloy oxidation. Secondary metallurgy is performed to maintain ladle basicity and Aluminium content in the steel. The study introduces Artificial Intelligence (AI) integration, utilising Random Forest Regression model to predict Aluminium consumption. This AI-based approach enhances ladle basicity optimisation, reduces Aluminium usage, and ensures high-quality steel production. The research explores infrared technology, digital image processing, and colour visualisation for slag prevention during ladle tapping. The development of a Random Forest Regressor model surpasses empirical equations in predicting Aluminium consumption, resulting in superior ladle basicity maintenance. Comparative analysis demonstrates the accuracy of AI-aided ladles, optimising Al2O3 inclusion formation and sustaining ladle basicity. The research concludes by highlighting AI’s benefits, including improved steel quality, reduced secondary metallurgy, and enhanced operational efficiency, positioning AI as a valuable tool for cost-effective, energy-efficient, and reliable high-quality steel production.

Drones and Sustainability: How Technology Can Assist in the Automatic Detection of Waste In Hard-to-Access Areas

Purpose: This work aims to present Unmanned Aerial Vehicles (UAVs), known as drones, as an alternative for environmental monitoring, considering the difficulty of identifying events of illegal dumping in remote areas, whether due to government omission or the difficulty of accessing these locations. These devices provide several benefits, including portability and the capacity to take pictures in real-time from remote and difficult-to-reach places at a cheaper cost without endangering the operator's safety. Design/methodology/approach: The quantity of data gathered by drones needs an enormous amount of effort to analyze and interpret properly due to Rio de Janeiro's difficult topography and large geographic area. To overcome this challenge, artificial intelligence should be used to automatically evaluate and georeference the photos and videos gathered during the investigation. The present work aims to demonstrate the feasibility of this technique in the early and effective identification of remote areas with illegal solid waste dumping by combining a variety of methods, including the use of a global positioning system (GPS), high-precision cameras and sensors on an electronic device for remote sensing (drone), and image processing and interpretation using computational resources. Findings: Following extensive remote sensing in the selected areas, with the assistance of a UAV, the obtained photos were batch-processed utilizing Deep Learning algorithms and digital image processing techniques. After a significant amount of training, these algorithms were able to automatically identify and classify photos that showed signs of solid waste disposal from those that did not, showing that this rapid-automated segregation produced a 92% efficacy. Discussion: Rio de Janeiro produces a lot of trash, much of it is improperly disposed of and indiscriminately deposited in urban and surrounding regions, on hills, and in difficult-to-reach locations. This issue is particularly worse in areas where the government is absent and ineffective, causing citizens to dispose of their garbage in dangerous and hard-to-reach locations. This study offers a Deep Learning-based method for automatically identifying underprivileged communities that require government intervention to reduce irregular garbage disposal and all of the associated environmental consequences.