Digital Image Research Articles

An efficient novel dual deep network architecture for video forgery detection

The technique of video copy-move forgery (CMF) is commonly employed in various industries; digital videography is regularly used as the foundation for vital graphic evidence that may be modified using the aforementioned method. Recently in the past few decades, forgery in digital images is detected via machine intellect. The second issue includes continuous allocation of parallel frames having relevant backgrounds erroneously results in false implications, detected as CMF regions third include as the CMF is divided into inter-frame or intra-frame forgeries to detect video copy is not possible by most of the existing methods. Thus, this research presents the dual deep network (DDN) for efficient and effective video copy-move forgery detection (VCMFD); DDN comprises two networks; the first detection network (DetNet1) extracts the general deep features and second detection network (DetNet2) extracts the custom deep features; both the network are interconnected as the output of DetNet1 is given to DetNet2. Furthermore, a novel algorithm is introduced for forged frame detection and optimization of the falsely detected frame. DDN is evaluated considering the two benchmark datasets REWIND and video tampering dataset (VTD) considering different metrics; furthermore, evaluation is carried through comparing the recent existing model. DDN outperforms the existing model in terms of various metrics.

Quantitative digital image correlation approach for the monitoring of fatigue damage development in 10CrMo9-10 steel in the as-received state and after extended service

In this paper, the quantitative Digital Image Correlation (DIC) approach was used to study the long-time degradation of two different states of 10CrMo9-10 (10H2M) power engineering steel. The specimens subjected to cyclic loading (R = 0) were monitored by using the DIC technique. The data obtained from DIC measurements were presented in form of strain distributions for specified, independent points within the strain gauge of specimens. Furthermore, the strain profiles were extracted for the particular stages of fatigue damage development (FDD). The presented methodology provides a different approach of DIC application, in which, the data could be treated more quantitative than qualitative.

Smartphone-Based Colourimetric Detection of Methyl Red, Co(II), Uric Acid, and Topotecan after Pre-concentration onto a Hectorite Clay-Hydroxyethylcellulose Hybrid

Objective:: This paper describes a new, digital image colourimetry-based format for the quantification of analytes in an aqueous solution. Method:: The proposed method is based on analyte pre-concentration by adsorption onto Bentone LT. Bentone LT pellet isolation comes after adsorption, followed by in-situ application of an analyteselective chromogenic reaction. The resulting pellet colouration is captured by the phone’s integrated camera and assessed using the free open-source image processing software, ImageJ. Responses are calibrated and quantified. Results:: We tested the applicability of the proposed methodology for the quantification of specific model analytes which are of concern in environmental matrices (methyl red, Co(II), uric acid, topotecan). The smartphone-based assay was proven reliable in quantifying the model analytes (standard recovery of 82-116%), alone or in mixture, from dilute aqueous solutions and was found to depict accurately the adsorption behaviour followed photometrically in solution. Lower limit of linearity was calculated at 0.05, 0.11, 0.85 and 0.20 μg/mL for methyl red, Co(II), uric acid, and topotecan, respectively. The proposed format was found superior when compared to alternative published photometric/ colourimetric assays in terms of the lower limit of linearity. In the presence of possible adsorption interferents, the lower limit of linear response was shifted to slightly higher concentrations for topotecan i.e. from 0.2 μg/mL to 0.5 μg/mL. Conclusion:: We here demonstrate the extended applicability of the proposed methodology for the smartphone-based quantification of the specific model analytes. The applicability of this analysis format likely extends to other analytes, where analyte-specific colour formation is feasible.

Multiple-view 3D digital image correlation based on pseudo-overlapped imaging

The restriction of the field of view (FOV) enlargement and spatial resolution increase during optical monitoring was investigated. Traditional optical instruments usually have a fixed FOV in one test; thus, they have low accuracy for small samples under large motions/deformations. To improve the spatial resolution in a relatively large FOV of an optical instrument, a multiple-view 3D digital image correlation (3D-DIC) method based on pseudo-overlapped imaging is proposed. Using a set of optical components, pseudo-overlapped imaging can image two FOVs with the same camera, which converts one pair of cameras to four virtual cameras. Each virtual camera has the same whole pixels. Therefore, compared with the conventional 3D-DIC system, the proposed method simultaneously enlarges FOVs and increases spatial resolutions by two times. The efficiency, accuracy, and feasibility of the technique were validated through experiments.

Hybrid calibration method for a single camera stereo digital image correlation system

In single camera stereo digital image correlation systems, the existing calibration methods have problems with poor accuracy and violation of theoretical constraints, so we have proposed a hybrid calibration method. In this study, we first explain the principles of the existing single camera stereo measurement calibration methods. Then, on the basis of the classical camera calibration method combined with the characteristics of planar mirror reflection imaging, a hybrid calibration method is proposed that not only satisfies the constraints of the same intrinsic parameters but also has higher accuracy. Next, we complete the mathematical model of the hybrid calibration method and explain the specific calibration process. Finally, the experimental results of point distance reconstruction and static displacement measurement show that, compared with the existing calibration methods, the hybrid calibration method has lower error and higher reconstruction accuracy.

Employing Digital Images In Outdoor Road Advertising

يعد الاعلان الطرقي مصدر للمعلومات عن العناصر بأختلاف شكلها مما يحويهِ على معلومات عن منتج معين لاسيما أن اغلب المنتجات تستخدم وسيلة الإعلان لترويج عن منتجاتها , وكان عندهُ التساؤل التالي وهو : كيف يتم توظيف الصورة الرقمية في الأعلانات الضوئية للطرق الخارجية ؟

Collaborative Optimization of English Online Teaching Informatization Based on Intelligent Multimedia Image Technology

With the rapid development of computer technology and the deepening of educational concepts, the traditional English teaching methods have also changed. As a new teaching method, online teaching has received extensive attention. Online teaching has introduced intelligent multimedia image technology in English teaching, which has transformed the essence of English teaching by organically integrating traditional teaching and multimedia teaching. With the rapid growth of Internet and intelligent multimedia image technology, the number of digital images is growing exponentially. How to extract the required image from the large amount of multimedia image data has been an important topic at present. In this case, intelligent multimedia image technology emerges as the times require, which extracts the features of the image from the content of the image itself, and then retrieves it through the features. This article proposed a retrieval and classification method based on intelligent multimedia image technology. Among them, Support Vector Machine (SVM) was used to classify multimedia resources, which had a good effect. The results of this experiment showed that before the experiment, the listening score of the multimedia group was 65.5, and the speaking score was 59.7. After the experiment, the listening score of the multimedia group was 79.8, and the speaking score was 80.6. It can be found that the performance of the multimedia group has been greatly improved after the experiment, which shows that online English teaching based on intelligent multimedia image technology is more conducive to the improvement of students’ academic performance.

Co to jest chaosmotechnika?

This article considers the problem of the scale of locality as a matter of placing technology in an (alter)cosmological perspective. It engages in a dialogue with two Simondon-inspired projects: on the one hand, Yuk Hui’s concept of cosmotechnics, and on the other, the transductive theory of systems outlined in the Deleuzoguattarian Thousand Plateaus. A new reading of a marginal thread of the latter, i.e., the universe of technical objects associated with the nomadism of the Great Steppe of the Bronze and Iron Ages, leads to the crystallization of the concept of chaosmotechnics, according to which the substrate of technical activities is the contingency of the cosmos. Answering the question of locality, the chaosmotechnic perspective allows for the untimely emergence of an alternative cosmology of the contemporary digital image from nomadic metal technical objects. Key-words: cosmos, technology, art, digital image, Witold Gombrowicz, Yuk Hui, Gilbert Simondon, Gilles Deleuze, Félix Guattari, Laura U. Marks

Using Grayscale Photos to Introduce High School Statistics Teachers to Reasoning with Digital Image Data

Statistics teaching at the high school level needs modernizing to include digital sources of data that students interact with every day. Algorithmic modeling approaches are recommended, as they can support the teaching of data science and computational thinking. Research is needed about the design of tasks that support high school statistics teachers to learn new statistical and computational approaches such as digital image analysis and classification models. Using our design framework, the construction of a task is described that introduces classification modeling using grayscale digital images. The task was implemented within a teaching experiment involving six high school statistics teachers. Our findings from this exploratory study indicated that the task design seemed to support statistical and computational thinking practices related to classification modeling and digital image data.

Digital image comparison of cellular yield in bronchial brushing, pre- and post-biopsy lavage cytology.

Background Bronchoscopy is a useful diagnostic tool capable of performing core biopsy, forceps biopsy, bronchoalveolar lavage, and bronchial brushing. This study compares the cellularity of bronchial cytology including pre- and post-biopsy lavage by digital image analysis, aiming to increase diagnostic and tumor yield by optimizing the sequence and combination of bronchial biopsy and cytology. Methods Alveolar macrophage, bronchial epithelium, and tumor cell cellularity from liquid-based cytology preparations of bronchial brushing and pre-biopsy and post-biopsy bronchoalveolar lavage were annotated on digitized whole-slide images and compared. Secondary analysis on the relationship of tumor cell and non-lesional cell yield was performed. Results 118 cytology specimens from 43 patients were retrieved in total. Bronchial epithelium count was higher in pre-biopsy than post-biopsy lavage (p<0.01) but not for alveolar macrophages nor tumor cell (p>0.05). Tumor cell count was higher for bronchial brushing cytology samples than lavage (p=0.018). The alveolar macrophage count was higher in post-biopsy lavage than bronchial brushing (p=0.033), otherwise brushing showed consistently higher bronchial epithelium and tumor cell counts. There were 33 false negative (tumor cell absent) specimens, and the combination of bronchial brushing and pre-biopsy lavage yielded the lowest false negative cases. Correlation between bronchial epithelium and alveolar macrophage counts with tumor cell count were weak (correlation coefficient=-0.168-0.203) except for post-biopsy lavage (correlation coefficient=0.412-0.479, p<0.05). Conclusion Bronchial brushing yields a greater amount of tumor cell than lavage, and timing lavage before or after core biopsy does not affect tumor cell yield. Combining bronchial brushing and pre-biopsy lavage results in the lowest false negative rate.

Diagnostics of premature damage to surface-hardened industrial concrete floors

The article presents the diagnostic results on surface-hardened industrial concrete floors. Selected examples of floors showcased premature damage to surface layers, characterized by intense dusting, delamination, and local spalling, while the structural system remained unaffected. Quantitative petrographic analysis of concrete was applied to core specimens from the floors, involving the examination of digital images from a polarizing optical microscope and a scanning electron microscope. The hardening compound and powdered specimens of the cement matrix were characterized using differential thermal analysis and X-ray diffraction. A multiple microindentation method was employed to assess local variations in mechanical properties. Concrete cross-section analysis revealed areas with a non-uniform distribution of air voids, identified regions exhibiting increased porosity, highlighted areas of cracking in the concrete, indicated local variability in the phase composition of cement hydration products, and pointed out the presence of carbonated areas. The causes of the damage were discussed based on these findings,. The crucial role of quantitative petrographic analysis in diagnosing premature surface damage to industrial floors was demonstrated.

Determination of acetylcysteine and cysteine in pharmaceutical formulations using a smartphone-based digital image colorimetric method

Determination of acetylcysteine and cysteine in pharmaceutical formulations using a smartphone-based digital image colorimetric method

Deciphering tertiary lymphoid structure heterogeneity reveals prognostic signature and therapeutic potentials for colorectal cancer: a multicenter retrospective cohort study.

Tertiary lymphoid structures (TLSs) exert a crucial role in the tumor microenvironment (TME), impacting tumor development, immune escape, and drug resistance. Nonetheless, the heterogeneity of TLSs in colorectal cancer (CRC) and their impact on prognosis and treatment response remain unclear. We collected genome, transcriptome, clinicopathological information, and digital pathology images from multiple sources. An unsupervised clustering algorithm was implemented to determine diverse TLS patterns in CRC based on the expression levels of 39 TLS signature genes (TSGs). Comprehensive explorations of heterogeneity encompassing mutation landscape, TME, biological characteristics, response to immunotherapy, and drug resistance were conducted using multi-omics data. TLSscore was then developed to quantitatively assess TLS patterns of individuals for further clinical applicability. Three distinct TLS patterns were identified in CRC. Cluster 1 exhibited upregulation of proliferation-related pathways, high metabolic activity, and intermediate prognosis, while Cluster 2 displayed activation of stromal and carcinogenic pathways and a worse prognosis. Both Cluster 1 and Cluster 2 may potentially benefit from adjuvant chemotherapy. Cluster 3, characterized by the activation of immune regulation and activation pathways, demonstrated a favorable prognosis and enhanced responsiveness to immunotherapy. We subsequently employed a regularization algorithm to construct the TLSscore based on 9 core genes. Patients with lower TLSscore trended to prolonged prognosis and a more prominent presence of TLSs, which may benefit from immunotherapy. Conversely, those with higher TLSscore exhibited increased benefits from adjuvant chemotherapy. We identified distinct TLS patterns in CRC and characterized their heterogeneity through multi-omics analyses. The TLSscore held promise for guiding clinical decision-making and further advancing the field of personalized medicine in CRC.

Insights into the fracture properties of recycled ceramic and rubber composite cement-based materials: Fracture mechanics, acoustic emission, and digital image correlation

Recycling plays a pivotal role in construction waste management, offering a path toward sustainability for the industry. Although waste ceramics can compensate for the mechanical properties of rubberized cement-based materials, the smooth glaze surface of waste ceramics increases the risk of brittle failure. This study investigated the effect of ceramic waste aggregate (CWA) on the fracture properties of rubberized mortar through the three-point bending experiment. The fracture parameters were calculated using the Double-K fracture criterion. Simultaneously, acoustic emission (AE) and digital image correlation (DIC) technologies were used to explore the fracture damage mechanism and the crack evolution process. The results indicate that the integration of 15 % and 45 % CWA significantly enhances both initiation (KICini) and unstable (KICun) fracture toughness, whereas incorporating 90 % CWA leads to a 16.8 % and 8.2 % decrease in (KICini)and (KICun), respectively. The fracture energy (GF) of rubberized mortar exhibits a continuous decline as a function of increasing CWA substitution rates. Furthermore, GMM-based RA-AF analysis elucidates a positive correlation between CWA content and tensile crack proportion in specimens. When the CWA content reaches 90 %, the proportion of tensile cracks is quantified at 29.83 %, surpassing that in standard mortar. Crack tip opening displacement (CTOD) at 0.5post-Pmax for specimens with 15 %, 45 %, and 90 % CWA substitution reveal improvements of 49.1 % and 17.2 %, and a decrease of 23.3 %, respectively, compared to standard mortar, indicating that 15 % and 45 % CWA effectively preserve the ductility of rubberized mortar. Elevated CWA substitution leads to an amplified strain localization phenomenon around the crack tip, heightening the propensity for brittle failure.

High Performance Mg Alloy with Designed Microstructure and Phases.

A high strength and ductile Mg-Gd-Y-Zn-Zr alloy was designed and fabricated. The local strain evolution of the alloys during plastic deformation was analyzed using high-resolution digital image correlation (DIC). The results showed that the β particles, nano-sized γ' phases, and LPSO phases were distributed in the as-extruded alloy and a bimodal microstructure was exhibited, including elongated un-dynamic recrystallized grains and fine dynamic recrystallized grains. With increasing extrusion ratio, the grain size remained, with the volume fraction of dynamic recrystallization of the as-extruded alloy increasing from 30% to 75%, and the as-extruded alloy exhibited a high strength-ductility synergy, which is attributed to the grain refinement, extensive β particles, and elongated block-shaped LPSO phases. The strain evolution analysis showed that a strain-transfer from un-DRXed regions to adjacent DRXed regions and LPSO phases can promote uniform plastic deformation, which tends to improve the ductility of the alloy.

Non-planar parallel sections: A new approach in digital image analysis of CT volume datasets to improve the assessment of pottery-forming methods

The paper presents a newly developed technique for displaying sections of CT reconstructions of pottery fragments. Currently, planar tangential sections are the primary way to show a view perpendicular to the surface of the pottery fragment. This section is essential as it displays structural phenomena critical to interpreting pottery-forming methods. However, the planar tangential sections in curved vessel walls show only a limited area of the wall and, even in this area, the sections cannot display structural phenomena at a constant depth of the wall. Consequently, we designed the construction of a non-planar section that follows the vessel’s shape and can be embedded at a defined depth from the surface so that it maps structural phenomena in defined subsurface layers coaxial with the vessel surface. The newly designed imaging technique was applied to the selection of archaeological pottery from the Czech Republic dated to the La Tène period. We demonstrate the usefulness of the non-planar sections for visual inspection of the diagnostic features related to pottery forming as well as for more accurate calculation of the orientation of the components of the ceramic body.

Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology.

A deep learning model using attention-based multiple instance learning (aMIL) and self-supervised learning (SSL) was developed to perform pathologic classification of neuroblastic tumors and assess MYCN-amplification status using H&E-stained whole slide digital images. The model demonstrated strong performance in identifying diagnostic category, grade, mitosis-karyorrhexis index (MKI), and MYCN-amplification on an external test dataset. This AI-based approach establishes a valuable tool for automating diagnosis and precise classification of neuroblastoma tumors.

Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images.

The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma.

Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images

BackgroundNottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate grade, NHG2. Here, we evaluate if DeepGrade, a previously developed model for risk stratification of resected tumour specimens, could be applied to risk-stratify tumour biopsy specimens.MethodsA total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional neural network model, was applied for the prediction of low- and high-risk tumours. It was evaluated against clinically assigned grades NHG1 and NHG3 on the biopsy specimen but also against the grades assigned to the corresponding resection specimen using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis.ResultsBased on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resected clinically-assigned NHG2 tumours, 281 (65%) were classified as DeepGrade-low and 151 (35%) as DeepGrade-high. Using a multivariable Cox proportional hazards model the hazard ratio between DeepGrade low- and high-risk groups was estimated as 2.01 (95% CI: 1.06; 3.79).ConclusionsDeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to identify high-risk tumours based on preoperative biopsies, thus improving early treatment decisions.

Characterizing thermo-hydro-mechanical behavior of rock using a grain interface-based discrete element model (GIB-DEM)

Understanding the meso-structure evolution and macro-mechanical property changes in rocks under thermo-hydro-mechanical (THM) treatment is crucial for advancing and managing deep geological projects. However, due to complex discontinuity networks in rocks, achieving an accurate characterization that quantitatively correlates the micro-macro mechanical behaviors under THM conditions remains challenging. This study introduces a groundbreaking multi-scale analysis method called the grain interface-based discrete element model (GIB-DEM) to tackle this issue. By utilizing digital imaging processing (DIP) and meso-mechanical measurements, the GIB-DEM directly determines the meso-mechanical parameters of bonds, eliminating the need for complex calibration found in traditional methods. After validating the GIB-DEM through experimental and numerical Brazilian Splitting tests, we investigate the macro- mechanical behaviors of rock under different THM conditions. Our results indicate that the increase in the tensile strength at 75 °C is attributed to the tensile strength increase of quartz grain interfaces. The development of micro-cracks on the interfaces of feldspar and feldspar-quartz accounts for the decrease in tensile strength of rock under thermal and hydraulic treatment. Overall, this pioneering research provides valuable insights for optimizing Enhanced Geothermal System (EGS) projects and effectively managing Hot Dry Rock (HDR) resources.