Digital Image Research Articles

Object Detection using Convolutional Neural Network

During the last years, a noticeable growth is observed in the field of computer vision research. In computer vision, object detection is a task of classifying and localizing the objects in order to detect the same. The widely used object detection applications are human– computer interaction, video surveillance, satellite imagery, transport system, and activity recognition. In the wider family of deep learning architectures, convolutional neural network (CNN) made up with set of neural network layers is used for visual imagery. Deep CNN architectures exhibit impressive results for detection of objects in digital image. This paper represents a comprehensive review of the recent development in object detection using convolutional neural networks

Space/ground-borne techniques and petrographic microscopic dissection for geologic mapping in Gabal Ras Abda area, Northeastern Desert, Egypt

BackgroundGabal Ras Abda area as a part of the Red Sea Mountain range, is characterized by inaccessible and rugged terrains. The exposed rock units are hardly followed in the field because of the rigid topography. Thus, the present work proposes and develops an integrated approach to map the exposed rock units and extract the geologic structures using satellite imagery data followed by both field and petrographic verification, saving time, efforts and cost.ResultsTo achieve the target, both the measured spectral signature curves with Landsat-8 and Sentinel-2A data were used to develop and create the most enhanced Band Ratios and Principal Components for lithological discrimination and mapping which were (((Band7 + Band 4)/(Band 7), (Band 2)/(Band 2 + Band 5) and (Band 5) in RGB) and ((Band 9 + Band 11 + Band 12)/(Band 1), (Band 4 − Band 2) and (Band 11/Band 6) + (Band 6) in RGB)) with Principal Component Bands ((PC1, PC2 and PC3 in RGB) and (PC3, PC2 and PC1 in RGB)), respectively. Also, georeferenced Google Earth Pro, panchromatic band of Landsat-8 and ALOS PALSAR Digital Elevation Model images were used to extract the structural lineaments. Geologic, petrographic and field structural studies were emphasized the remote sensing results, indicating that the main rock types cropped out in Ras Abda area from the oldest to the youngest are older granitoids (quartz-diorites, tonalites and granodiorites), Dokhan volcanics (andesites, rhyodacites, rhyolites and their related tuffs), younger gabbros, younger granites (monzogranites, syenogranites and alkali-feldspar granites), post-granite dykes and offshoots (acidic, microgranitic and basic types) and Phanerozoic sedimentary rocks. Also, the study emphasized that the E-W trend is the main structural trend controlling the investigated area followed by WNW-ESE and NE-SW directions.ConclusionsThe results of remote sensing achieved compliance with the geologic, petrographic and structural investigation through distinctly differentiating the different rocks and extracting the lineaments, indicating the accuracy of the remote sensing results and emphasizing their importance and effective role in producing a precise and highly accurate geologic map.Graphical abstract

Resin infiltration versus fluoride varnish for visual improvement of white spot lesions during multibracket treatment. A randomized-controlled clinical trial

AimsThis study aimed to evaluate the visual improvement of resin infiltration of white spot lesions (WSL) during orthodontic treatment with the multibracket appliance (MBA) compared to fluoride varnish.MethodsPatients aged 12–17 years with at least one WSL with an International Caries Detection and Assessment System (ICDAS) score of 1–2 during an active MBA treatment were included and randomized to receive either resin infiltration (Icon) or fluoride application (Flairesse). Standardized digital images were obtained before, one-day, one-week, one-month, three-months and six-months after treatment using a DSLR camera and a matching polarization filter. A grey reference card was used for color standardization. A Matlab routine was used to measure the color difference between adjacent healthy enamel and treated WSL. The independent-samples t-test was used for intergroup and paired-samples t-test for intragroup comparison.ResultsImages of 116 teeth from 36 patients were analyzed. The ΔE for the “Icon” treated WSL was smaller (T1ICON = 5.0 ± 1.4) than in the fluoride group (T1Fluoride = 8.4 ± 3.2). Caries infiltration significantly improved the aesthetic appearance of WSL (p < 0.001), which remained satisfactory at six months (T5ICON = 5.2 ± 1.6).ConclusionWSL infiltration management during orthodontic treatment was superior to topical fluoridation in not only arresting the enamel lesions but also significantly improving the aesthetic appearance of demineralized regions around the brackets.Clinical relevanceWSL treatment in orthodontic patients is usually initiated after debonding. Research has shown that the earlier WSL is treated, the better the aesthetic outcome. There is limited data on the efficacy of resin infiltration of WSL during orthodontic treatment.

Experimental analysis of the compressive behaviour of perforated brick masonry using digital image correlation

The paper presents an experimental program on the characterisation of the mechanical behaviour of perforated brick masonry (PBM) under uniaxial compression. The study is aimed to provide a better understanding on the compressive strength and elastic behaviour of a kind of masonry hardly dealt with in the literature. The mechanical response of the investigated masonry shows actually two complexities, linked respectively to the perforated nature of the bricks and the asymmetric cross-section of the units. The latter feature is particularly recurrent in exposed brickwork composed of perforated bricks, whenever one of the face shells has a larger thickness than the other one. For this purpose, an experimental campaign was carried out on units and PBM subjected to uniaxial compressive loading. The mechanical response of PBM is investigated to accurately determine the masonry Young’s modulus and the compressive strength. Measurements obtained with conventional sensors are compared with those derived from the Digital Image Correlation (DIC) technique. Lastly, the experimental findings in terms of compressive strength and Young’s modulus are cross-referenced with the estimations derived from empirical criteria available in Eurocode 6 and ACI 530.1–02.

Label-free, high-throughput holographic imaging to evaluate mammalian gametes and embryos.

Assisted reproduction is one of the significant tools to treat human infertility. Morphological assessment is the primary method to determine sperm and embryo viability during IVF cycles. It has the advantage of being a quick, convenient, and inexpensive means of assessment. However, visual observation is of limited predictive value for early embryo morphology. It has led many to search for other imaging tools to assess the reproductive potential of a given embryo. The limitations of visual assessment apply to both humans and animals. One recent innovation in ART imaging is interferometric phase microscopy (IPM), also known as holographic microscopy. IPM/QPI is the next likely progression of analytical microscopes for the assisted reproduction laboratory. The IPM system analyzes waves produced by the light as it passes through the specimen observed. The microscope collects the light waves produced and uses the algorithm to create a hologram of the specimen. Recently, IPM has been combined with quantitative phase imaging (QPI), which joins phase contrast microscopy with holographic microscopy. These microscopes collect light waves produced and use the algorithm to create a hologram of the specimen. Unlike other systems, IPM can provide a quantitative digital image, and it can make 2-D and 3-D images of the samples. This review summarizes some newer and more promising QPI microscopy systems for evaluating gametes and embryos. Studies clearly show that QPI is superior to bright field microscopy-based evaluation methods when evaluating sperm and oocytes prior to IVF and embryos prior to transfer. However, further assessment of these systems for efficacy, reproducibility, cost-effectiveness, and embryo/gamete safety must take place before they are widely adopted.

“Image-Spectral” fusion monitoring of small cotton samples nitrogen content based on improved deep forest

Accurate monitoring of nitrogen nutrition is crucial for improving cotton yield and quality, as well as the ecological environment. The mainstream method for monitoring nutrition is to establish traditional machine learning (ML) models using a single data source. However, this approach has limitations such as limited access to feature information, model-fitting problems, and limited generalization. Deep learning (DL), on the other hand, has shown promise in complex nonlinear modeling tasks due to its flexible structure. However, it has its limitations, such as the fact that agronomic sample collection and testing are usually labor- and material-intensive, resulting in sample sizes that are too small to meet its training conditions. Therefore, there is an urgent need for DL models that can effectively integrate features from multiple data sources and accurately monitor crop nitrogen content, especially in scenarios with small samples. In this study, we conducted indoor pot experiments using the cotton variety Xinluzao 53 and subjected it to six nitrogen treatments. The data sources for our analysis included hyperspectral and digital images of the cotton leaves. To enhance representation learning capabilities, we enriched the multi-class base learners within each layer of the deep forest (DF) model and introduced skip connections. These enhancements improved the quality of inversion for both hyperspectral and digital image datasets. We then developed image-spectral fusion models, which combined the DF structure with stacking ensemble learning. Our focus was on three levels of fusion: feature-level fusion, decision-level fusion, and secondary decision-level fusion. This approach aimed to further enhance the accuracy and stability of nitrogen content inversion. The DF model satisfied the training condition for small samples. Compared to traditional ML algorithms and the original DF algorithm, the improved DF model achieved an increase in validation set R2 of 13.4–28.5% and 10.9–14.9%, respectively. These findings highlight the enhanced accuracy and stability of the improved DF model. Additionally, compared to the optimal inversion model using two single data sources, the “Image-Spectral” three-level fusion models exhibited improvements in validation set R2 of 8.6–9.3%, 10.5–11.2%, and 11.8–12.5% for feature-level, decision-level, and secondary decision-level fusion, respectively. The improved DF and three-level fusion model collectively contributed to the increased accuracy of cotton nitrogen content inversion. Among these models, the secondary decision-level fusion model demonstrated the most marked improvement. This methodology provides valuable insights into monitoring crop phenotypic parameters in situations with limited sample sizes.

A methodology for phase characterization in pellet feed using digital microscopy and deep learning

This work proposes a new method for characterizing pellet feed, employing Deep Learning (DL) and Convolutional Neural Networks (CNNs). The main minerals in the composition of the studied pellet feed are hematite, magnetite, goethite, and quartz. Over time, several characterization methodologies have been developed that use Digital Microscopy and Image Analysis tools. The greatest difficulties in this characterization lie in differentiating the textures of hematite particles, the different shapes of their crystals, or discriminating between quartz and resin in reflected light optical microscopy images. This work proposes a mineral characterization methodology based on the Mask R-CNN algorithm. The goal is to perform instance segmentation, that is, to identify, classify, and segment objects in the images. Two DL models were combined: the BF Model performs instance segmentation for the compact, porous, martite, and goethite classes in images obtained in Bright Field mode, and the CPOL Model uses images acquired in Circularly Polarized Light to segment the monocrystalline, polycrystalline, and martite classes. An F1-score of around 80% was obtained for the BF Model and around 90% for the CPOL Model. The results were promising and can be improved as the training image database increases.

The toughness of interlocking metasurfaces

Interlocking metasurfaces (ILMs) are arrays of autogenous latching unit cells patterned across a surface. These create structural joints similar to bio‐inspired suture joints but patterned over a 2D surface rather than a 1D seam. This enables ILMs to be an alternative to conventional joining technologies such as bolts, welds, and adhesives. However, compared to conventional joining methods, relatively little is known of the engineering considerations for designing structural ILMs. In this study, the interfacial toughness of an archetypal ILM was examined for the first time. Under the conditions studied here, the ILM was substantially tougher than the material from which it was made; in this case exhibiting up to a 50% increase in interfacial crack initiation energy over the solid base material, a photocured 3D printed polymer. Through experimental tests using in‐situ digital image correlation along with complementary computational analyses, the mechanism of toughening in the ILM structure and the origins of toughness anisotropy were revealed. The increase in toughness is associated with cross‐cell interactions, i.e. load‐sharing across unit cells, which gives rise to a finite process zone length with different effective material properties. In this way, ILM toughening is analogous to crack blunting in ductile materials or fiber bridging in composites; yet here, the ILM was composed of a single‐phase base material and so the architected toughening is geometric in nature and hence amenable to future topological optimization.This article is protected by copyright. All rights reserved.

Association between HDL cholesterol with diabetic retinopathy in diabetic patients: a cross-sectional retrospective study

ObjectiveDiabetic patients are often comorbid with dyslipidemia, however, the relationship between high-density lipoprotein cholesterol(HDL-C) and diabetic retinopathy (DR) in the adult diabetic population remains to be fully elucidated.The aim of this study is to evaluate the associations between HDL-C and DR in the United States adults with diabetes.MethodsA total of 1708 participants from the National Health and Nutrition Examination Survey (NHANES) 2005–2008 were enrolled in the present study. Fundus images of all study subjects were captured and evaluated using a digital camera and an ophthalmic digital imaging system, and the diagnosis of DR was made by the severity scale of the Early Treatment Diabetic Retinopathy Study (ETDRS).Roche Diagnostics were used to measure serum HDL-C concentration. The relationship of DR with HDL-C was investigated using multivariable logistic regression. The potential non-line correlation was explored with smooth curve fitting approach.ResultsThe fully-adjusted model showed that HDL-C positively correlated with DR(OR:1.69, 95%CI: 1.25–2.31).However, an inverted U-shaped association between them was observed by applying the smooth curve fitted method. The inflection point of HDL-C(1.99mmol/l) was calculated by utilizing the two-piecewise logistic regression model. In the subgroup analysis, the inverted U-shaped nonlinear correlation between HDL-C and DR was also found in female, Non-Hispanic White, and lower age groups.ConclusionOur study revealed an inverted U-shaped positive relationship between HDL-C and DR.The findings may provide us with a more comprehensive understanding of the association between HDL-C and DR.

Phenotyping methodologies of log end splitting in eucalyptus (Eucalyptus spp.)

AbstractThis study addresses the crucial consideration of log end splitting in breeding programmes for treated wood. There is a paucity of research focused on efficiently optimizing the phenotyping process for this particular trait. The study aimed to compare methodologies for log end splitting phenotyping and develop an image‐based crack evaluation approach. Initially, 32 eucalyptus clones underwent phenotyping using manual measurement, digital image analysis and visual evaluation. Results showed similar phenotypic values, but image analysis demonstrated better clone discrimination, reducing evaluation time to 78 h compared to manual measurement. The second part focused on testing convolutional neural network architectures (UNet, LinkNet and FPN) using real and synthetic images. U‐Net exhibited slight superiority based on higher Intersection over Union (IoU) values, exhibiting a high correlation (.89) with true values. This approach significantly reduced evaluation time to approximately 10.15 h, emphasizing its efficiency compared to traditional methods.

Interactive machine learning for segmenting pores of sandstone in computed tomography images

In the realm of gas field development and CO2/H2 geological storage, the precise characterization of reservoir parameters stands as a critical role. In recent years, digital rock technology has emerged as a cutting-edge tool for examining micro-pore structures, permeability parameter characteristics, and reservoir flow mechanisms. However, there are still certain defects in image segmentation, where low-density pore bodies in low-resolution images make it difficult to clearly distinguish between pores and solid minerals, affecting the accuracy of pore segmentation. This article aims to improve the accuracy of sandstone pore segmentation. We utilized the interactive machine learning segmentation method to segment pore structures and compare the segmented images obtained with the traditional ImageJ grayscale threshold segmentation methods. Furthermore, we evaluated the effectiveness of different segmentation methods quantitatively using the confusion matrix. The research results indicate that the segmentation quality (F1 score) of the machine learning method is higher than the grayscale methods, and the segmentation effectiveness of machine learning method is more stable. To further validate the accuracy of the machine learning method, we simulated the permeability and the results were compared with the experimental results, and the results show that the deviation in permeability segmented through machine learning methods is the smallest. Therefore, our results demonstrate the effective application of the machine learning method in segmenting sandstone pores in the digital rock images.

Experimental approach for characterizing the nonlinear, time and temperature‐dependent constitutive response of open‐cell polyurethane foams

AbstractElastomeric foams are composite materials comprising of a polymeric elastomer and interconnected gas‐filled pores, endowing them with exceptional compliance and the ability to undergo large, reversible deformations along with substantial volume change. These foams find extensive utility in contexts demanding compliance and compressibility, such as impact protection and cushioning, spanning a diverse range of applications. Changing temperature can dramatically alter foam stiffness, strength and deformation characteristics specifically around the material's glassy‐rubbery transition temperature (). With the aim of informing new constitutive model developments for elastomeric foams, we conducted a comprehensive series of large deformation, homogeneous compression and tension tests across strain rates from 10−2 s−1 to 100 s−1 and ambient temperatures ranging from −10°C to 50°C covering an even range around the material's of 20°C. To achieve precise control of ambient temperatures during mechanical testing, we constructed a custom‐designed environmental chamber for controlling the ambient temperature from −10°C to 50°C with a variation of less than 1°C. The obtained digital image correlation based stress‐strain data shows significant tension‐compression asymmetry as well as significant dependence on strain rate and ambient temperature, especially above and below the glass transition temperature. We provide full access to these data sets for the future development of rate‐ and temperature‐dependent constitutive models.

Non-Invasive Retinal Vessel Analysis as a Predictor for Cardiovascular Disease

Cardiovascular disease (CVD) is the most frequent cause of death worldwide. The alterations in the microcirculation may predict the cardiovascular mortality. The retinal vasculature can be used as a model to study vascular alterations associated with cardiovascular disease. In order to quantify microvascular changes in a non-invasive way, fundus images can be taken and analysed. The central retinal arteriolar (CRAE), the venular (CRVE) diameter and the arteriolar-to-venular diameter ratio (AVR) can be used as biomarkers to predict the cardiovascular mortality. A narrower CRAE, wider CRVE and a lower AVR have been associated with increased cardiovascular events. Dynamic retinal vessel analysis (DRVA) allows the quantification of retinal changes using digital image sequences in response to visual stimulation with flicker light. This article is not just a review of the current literature, it also aims to discuss the methodological benefits and to identify research gaps. It highlights the potential use of microvascular biomarkers for screening and treatment monitoring of cardiovascular disease. Artificial intelligence (AI), such as Quantitative Analysis of Retinal vessel Topology and size (QUARTZ), and SIVA–deep learning system (SIVA-DLS), seems efficient in extracting information from fundus photographs and has the advantage of increasing diagnosis accuracy and improving patient care by complementing the role of physicians. Retinal vascular imaging using AI may help identify the cardiovascular risk, and is an important tool in primary cardiovascular disease prevention. Further research should explore the potential clinical application of retinal microvascular biomarkers, in order to assess systemic vascular health status, and to predict cardiovascular events.

Prediction of Mismatch Repair Status in Endometrial Cancer from Histological Slide Images Using Various Deep Learning-Based Algorithms

The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&amp;E)-stained slides has been reported in recent years, mainly for gastric and colon cancers. In this study, we investigated the potential use of H&amp;E-stained endometrial cancer slide images to predict the associated mismatch repair (MMR) status. H&amp;E-stained slide images were collected from 127 cases of the primary lesion of endometrial cancer. After digitization using a Nanozoomer virtual slide scanner (Hamamatsu Photonics), we segmented the scanned images into 5397 tiles of 512 × 512 pixels. The MMR proteins (PMS2, MSH6) were immunohistochemically stained, classified into MMR proficient/deficient, and annotated for each case and tile. We trained several neural networks, including convolutional and attention-based networks, using tiles annotated with the MMR status. Among the tested networks, ResNet50 exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.91 for predicting the MMR status. The constructed prediction algorithm may be applicable to other molecular profiles and useful for pre-screening before implementing other, more costly genetic profiling tests.

Tension and Shear Behaviour of Basalt Fiber Bio-Composites with Digital Image Correlation and Acoustic Emission Monitoring

This research investigates the mechanical behavior and damage evolution in cross-ply basalt fiber composites subjected to different loading modes. A modified Arcan rig for simultaneous acoustic emission (AE) monitoring was designed and manufactured to apply quasi-isotropic shear, combined tensile and shear loading, and pure tensile loading on specimens with a central notch. Digital image correlation (DIC) was applied for high-resolution strain measurements. The measured failure strengths of the bio-composite specimens under different loading angles are presented. The different competing failure mechanisms that contribute to the local reduction in stress concentration are described. Different damage mechanisms trigger elastic waves in the composite, with distinct AE signatures that closely follow the sequence of fracture mechanisms. AE monitoring is employed to capture signals associated with structural damage initiation and progression. The characteristic parameters of AE signals are correlated with crack modes and damage mechanisms. The evolution of AE parameters during the peak load transition is presented, which enables the timely AE detection of the maximum load transition. The combination of DIC and AE monitoring improves understanding of the mechanical response and failure mechanisms in cross-ply basalt fiber composites, offering valuable insights for possible performance monitoring and structural reliability in diverse engineering applications.

Mesoscopic precursor anomaly characteristics of red sandstone under uniaxial compression after cyclic high-temperatures

The deformation and failure characteristics of rock caused by periodic changes of temperature are not only significantly different from that under normal temperature, but also significantly different from that produced by a single high-temperature event. Here, using a three-dimensional digital image correlation (3D-DIC) integrated test system, mechanical and micro-damage tests of red sandstone are performed under cycles of high-temperature and natural-cooling with a temperature range of 25°C to 300°C and the number of cycles varying between 0 and 25. Macroscopic, mesoscopic and microscopic characteristics such as rock strength, deformation, strain field and fracture morphology are examined and their evolution with the number of heating-cooling cycles is discussed. Using the time-history evolution of the differentiation rate of the precursor anomaly (En), the characteristic coefficient (K) and the damage variable (DF), three kinds of characteristic time points are put forward to quantify the incubation and development time of precursor abnormal characteristics and are used to divide the whole process of deformation and failure. These time points are the precursor abnormal differentiation time (PADT) tE, the precursor abnormal startup time (PAST) tK, and the precursor abnormal failure time (PAFT) tD. Test results show that: (1) The action of cyclic high-temperature natural-cooling not only shows the strengthening effect of temperature, but also shows the strengthening effect of a certain number of cycles. (2) The evolution sequence of the three precursor abnormal characteristic moments is PADT, PAST and PAFT in turn, and this sequence can intuitively describe the whole complex process of rock deformation and failure, and there are wave peaks in the evolution curve with the number of high-temperature cycle. (3) The effect of high temperature (i.e., 300°C) cyclic heating on rock properties is similar to the threshold effect of a single high-temperature heating event.

Identification of Novel Loci Precisely Modulating Pre-Harvest Sprouting Resistance and Red Color Components of the Seed Coat in T. aestivum L.

The association between pre-harvest sprouting (PHS) and seed coat color has long been recognized. Red-grained wheats generally exhibit greater PHS resistance compared to white-grained wheat, although variability in PHS resistance exists within red-grained varieties. Here, we conducted a genome-wide association study on a panel consisting of red-grained wheat varieties, aimed at uncovering genes that modulate PHS resistance and red color components of seed coat using digital image processing. Twelve loci associated with PHS traits were identified, nine of which were described for the first time. Genetic loci marked by SNPs AX-95172164 (chromosome 1B) and AX-158544327 (chromosome 7D) explained approximately 25% of germination index variance, highlighting their value for breeding PHS-resistant varieties. The most promising candidate gene for PHS resistance was TraesCS6B02G147900, encoding a protein involved in aleurone layer morphogenesis. Twenty-six SNPs were significantly associated with grain color, independently of the known Tamyb10 gene. Most of them were related to multiple color characteristics. Prioritization of genes within the revealed loci identified TraesCS1D03G0758600 and TraesCS7B03G1296800, involved in the regulation of pigment biosynthesis and in controlling pigment accumulation. In conclusion, our study identifies new loci associated with grain color and germination index, providing insights into the genetic mechanisms underlying these traits.

Construction of a painting image classification model based on AI stroke feature extraction

Abstract A large number of digital painting image resources cannot be directly converted into electronic form due to their differences in painting techniques and poor preservation of paintings. Moreover, the difficulty of extracting classification features can also lead to the consumption of human time and misclassification problems. The aim of this research is to address the challenges of converting various digital painting image resources into electronic form and the difficulties of accurately extracting classification features. The goal is to improve the usefulness and accuracy of painting image classification. Converting various digital painting image resources directly into electronic format and accurately extracting classification features are challenging due to differences in painting techniques and painting preservation, as well as the complexity of accurately extracting classification features. Overcoming these adjustments and improving the classification of painting features with the help of artificial intelligence (AI) techniques is crucial. The existing classification methods have good applications in different fields. But their research on painting classification is relatively limited. In order to better manage the painting system, advanced intelligent algorithms need to be introduced for corresponding work, such as feature recognition, image classification, etc. Through these studies, unlabeled classification of massive painting images can be carried out, while guiding future research directions. This study proposes an image classification model based on AI stroke features, which utilizes edge detection and grayscale image feature extraction to extract stroke features; and the convolutional neural network (CNN) and support vector machine are introduced into image classification, and an improved LeNet-5 CNN is proposed to achieve comprehensive assurance of image feature extraction. Considering the diversity of painting image features, the study combines color features with stroke features, and uses weighted K-means clustering algorithm to extract sample features. The experiment illustrates that the K-CNN hybrid model proposed in the study achieved an accuracy of 94.37% in extracting image information, which is higher than 78.24, 85.69, and 86.78% of C4.5, K-Nearest Neighbor (KNN), and Bi directional Long short-term Memory (BiLSTM) algorithms. In terms of image classification information recognition, the algorithms with better performance from good to poor are: the mixed model &gt; BiLSTM &gt; KNN &gt; C4.5 model, with corresponding accuracy values of 0.938, 0.897, 0.872, and 0.851, respectively. And the number of fluctuation nodes in the mixed model is relatively small. And the sample search time is significantly shorter than other comparison algorithms, with a maximum recognition accuracy of 92.64% for the style, content, color, texture, and direction features of the image, which can effectively recognize the contrast and discrimination of the image. This method effectively provides a new technical means and research direction for digitizing image information.

Fractional Fourier-transform off-axis digital holographic imaging

A fractional Fourier-transform digital holographic imaging method with resolution enhancement features is presented. In an optical configuration, an extended fractional Fourier-transform optical setup is set in the object arm of an off-axis digital holographic recording system to record a fractional Fourier-transform hologram via the optical interference of the fractional Fourier-transform wavefront of an object wave with a reference wave. For reconstruction imaging, the reconstruction approach for fractional Fourier-transform holograms is given. In the experiment, the fractional Fourier-transform digital holograms are recorded under the different recording parameters, and their amplitude images are effectively reconstructed. The imaging results demonstrate that the reconstruction-imaging resolution of fractional-order Fourier-transform holograms is obviously enhanced compared to that of conventional image-plane holograms. The presented fractional Fourier-transform digital holographic imaging with resolution enhancement and optical configuration flexibility provides, to our knowledge, a novel way for off-axis digital holographic imaging.

A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

To divide a digital image into individual parts that share similar characteristics is known as digital image segmentation, and it is a vital research subject in the field of computer vision. Object recognition, medical imaging, surveillance, and video processing are just a few of the many real-world contexts where this study could prove useful. While digital image segmentation research has come a long way, there are still certain obstacles to overcome. Segmentation algorithms frequently encounter challenges in achieving both accuracy and efficiency when confronted with intricate settings, noisy pictures, or fluctuating lighting conditions. The absence of established evaluation standards adds complexity to the process of performing equitable comparisons among different segmentation methodologies. Due to the subjective nature of photo segmentation, attaining consistent results among specialists can be challenging. The integration of machine learning and deep neural networks into segmentation algorithms has introduced new challenges, including the need for large amounts of annotated data and the interpretability of the outcomes. Given these challenges, the objective of this study is to enhance the segmentation model. To this end, this research suggests a model of convolutional neural networks that is optimal for digital picture segmentation. The model is based on a dense convolution neural network, and it incorporates a transfer learning technique to significantly boost the model’s robustness and the quality of picture segmentation. The model’s adaptability to new datasets is improved by the incorporation of a transfer learning method. As demonstrated by experimental results on two publicly available datasets, the suggested methodology considerably enhances the resilience of digital picture segmentation.