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For the autonomous and intelligent operation of robots in unknown environments, simultaneous localization and mapping (SLAM) is essential. Since the proposal of visual odometry, the use of visual odometry in the mapping process has greatly advanced the development of pure visual SLAM techniques. However, the main challenges in current monocular odometry algorithms are the poor generalization of traditional methods and the low interpretability of deep learning-based methods. This paper presented a hybrid self-supervised visual monocular odometry framework that combined geometric principles and multi-frame temporal information. Moreover, a post-odometry optimization module was proposed. By using image synthesis techniques to insert synthetic views between the two frames undergoing pose estimation, more accurate inter-frame pose estimation was achieved. Compared to other public monocular algorithms, the proposed approach showed reduced average errors in various scene sequences, with a translation error of 2.211 % and a rotation error of 0.418 ∘ / 100 m. With the help of the proposed optimizer, the precision of the odometry algorithm was further improved, with a relative decrease of approximately 10 % intranslation error and 15 % in rotation error. [ABSTRACT FROM AUTHOR]

The paper concerns a nonlinear second-order system of coupled PDEs, having the principal part in divergence form and subject to in-homogeneous dynamic boundary conditions, for both θ (t , x) and φ (t , x) . Two main topics are addressed here, as follows. First, under a certain hypothesis on the input data, f 1 , f 2 , w 1 , w 2 , α , ξ , θ 0 , α 0 , φ 0 , and ξ 0 , we prove the well-posedness of a solution θ , α , φ , ξ , which is θ (t , x) , α (t , x) ∈ W p 1 , 2 (Q) × W p 1 , 2 (Σ) , φ (t , x) , ξ (t , x) ∈ W ν 1 , 2 (Q) × W p 1 , 2 (Σ) , ν = min { q , μ } . According to the new formulation of the problem, we extend the previous results, allowing the new mathematical model to be even more complete to describe the diversity of physical phenomena to which it can be applied: interface problems, image analysis, epidemics, etc. The main goal of the present paper is to develop an iterative scheme of fractional-step type in order to approximate the unique solution to the nonlinear second-order system. The convergence result is established for the new numerical method, and on the basis of this approach, a conceptual algorithm, alg-frac_sec-ord_u+varphi_dbc, is elaborated. The benefit brought by such a method consists of simplifying the computations so that the time required to approximate the solutions decreases significantly. Some conclusions are given as well as new research topics for the future. [ABSTRACT FROM AUTHOR]

The registration of full-moon remote sensing images constitutes a pivotal stage in the fusion analysis of multiple lunar remote sensing datasets. Addressing prevailing issues in automatic registration, such as the broad width of full-moon data, significant internal distortion, and texture distortion in high-latitude regions, this paper proposes a method for automatic matching and correction based on triangulation constraints. The approach employs a matching strategy progressing from coarse to fine and from sparse to dense. It optimizes and combines multiple existing matching algorithms, enhances the extraction of initial network points, constructs irregular triangulation networks using these points, conducts dense matching with each triangulation network as a basic unit, and introduces a geometric correction method based on triangulation network + grid (TIN + GRID) for the registration of full-moon data. For the matching of full-moon remote sensing images in high-latitude regions, a novel approach involving memory projection forward transformation-matching-projection inverse transformation is adopted. Through registration experiments with full-moon image data and an analysis of registration accuracy at different latitudes, the average mean square error is found to be less than 2 pixels. These results signify the efficacy of the proposed method in effectively addressing the automatic registration challenges encountered in full-moon remote sensing images. [ABSTRACT FROM AUTHOR]

Sports video image has always been a hot topic in sports video processing. The theoretical and experimental analysis of digital image noise reduction technology is a challenging topic. In this paper, a sports video denoising algorithm is designed by combining the excellent characteristics of curvilinear transformation theory and fuzzy support vector machine. Firstly, the image with noise is curvilinear, and the conversion coefficient is obtained. Then, according to the distribution characteristics of the system noise, the system parameters are divided into space, and the system learning features are constructed. The fuzzy classification of high-frequency curves is realized using the adaptive threshold denoising method. Then, the noise reduction coefficient is reconstructed by the curve-wave method to obtain the processed image. The simulation results show that this method can overcome the pseudo-Gibbs effect effectively and suppress the noise well. This algorithm has a good application prospect in sports video image processing. [ABSTRACT FROM AUTHOR]

In recent years, deep convolutional neural networks (DCNNs) have shown promising performance in medical image analysis, including breast lesion classification in 2D ultrasound (US) images. Despite the outstanding performance of DCNN solutions, explaining their decisions remains an open investigation. Yet, the explainability of DCNN models has become essential for healthcare systems to accept and trust the models. This paper presents a novel framework for explaining DCNN classification decisions of lesions in ultrasound images using the saliency maps linking the DCNN decisions to known cancer characteristics in the medical domain. The proposed framework consists of three main phases. First, DCNN models for classification in ultrasound images are built. Next, selected methods for visualization are applied to obtain saliency maps on the input images of the DCNN models. In the final phase, the visualization outputs and domain-known cancer characteristics are mapped. The paper then demonstrates the use of the framework for breast lesion classification from ultrasound images. We first follow the transfer learning approach and build two DCNN models. We then analyze the visualization outputs of the trained DCNN models using the EGrad-CAM and Ablation-CAM methods. We map the DCNN model decisions of benign and malignant lesions through the visualization outputs to the characteristics such as echogenicity, calcification, shape, and margin. A retrospective dataset of 1298 US images collected from different hospitals is used to evaluate the effectiveness of the framework. The test results show that these characteristics contribute differently to the benign and malignant lesions' decisions. Our study provides the foundation for other researchers to explain the DCNN classification decisions of other cancer types. [ABSTRACT FROM AUTHOR]

The prevalence of myopia, a common refractive error causing blurred distant vision, has been steadily increasing. In more severe cases, myopia manifests as high myopia and pathological myopia, which can lead to irreversible vision impairment due to associated complications like retinal detachment and macular degeneration. High myopia and pathological myopia pose serious threats to visual health, necessitating accurate and early detection for effective intervention. This research focuses on leveraging Convolutional Neural Networks (CNNs) for the automated detection and classification of high myopia and pathological myopia from fundus images. CNNs have proven to be powerful tools in image analysis tasks, particularly in discerning intricate patterns and features. Fundus photographs and optical coherence tomography scans are employed to capture detailed anatomical structures associated with high myopia and pathological myopia. [ABSTRACT FROM AUTHOR]

During the maintenance and management of solar photovoltaic (PV) panels, how to efficiently solve the maintenance difficulties becomes a key challenge that restricts their performance and service life. Aiming at the multi-defect-recognition challenge in PV-panel image analysis, this study innovatively proposes a new algorithm for the defect detection of PV panels incorporating YOLOv7-GX technology. The algorithm first constructs an innovative GhostSlimFPN network architecture by introducing GSConv and depth-wise separable convolution technologies, optimizing the traditional neck network structure. Then, a customized 1 × 1 convolutional module incorporating the GAM (Global Attention Mechanism) attention mechanism is designed in this paper to improve the ELAN structure, aiming to enhance the network's perception and representation capabilities while controlling the network complexity. In addition, the XIOU loss function is introduced in the study to replace the traditional CIOU loss function, which effectively improves the robustness and convergence efficiency of the model. In the training stage, the sample imbalance problem is effectively solved by implementing differentiated weight allocations for different images and categories, which promotes the balance of the training process. The experimental data show that the optimized model achieves 94.8% in the highest mAP value, which is 6.4% higher than the original YOLOv7 network, significantly better than other existing models, and provides solid theoretical and technical support for further research and application in the field of PV-panel defect detection. [ABSTRACT FROM AUTHOR]

White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation in brain dMRI scans. Articles on PubMed, ScienceDirect [NeuroImage, NeuroImage (Clinical), Medical Image Analysis], Scopus and IEEEXplore databases and Conference proceedings of Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI) and International Symposium on Biomedical Imaging (ISBI), were searched in the range from January 2013 until September 2023. This systematic search and review identified 619 articles. Adhering to the specified search criteria using the query, "white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation," 59 published studies were selected. Among these, 27% employed direct voxel-based methods, 25% applied streamline-based clustering methods, 20% used streamline-based classification methods, 14% implemented atlas-based methods, and 14% utilized hybrid approaches. The paper delves into the research gaps and challenges associated with each of these categories. Additionally, this review paper illuminates the most frequently utilized public datasets for tract segmentation along with their specific characteristics. Furthermore, it presents evaluation strategies and their key attributes. The review concludes with a detailed discussion of the challenges and future directions in this field. [ABSTRACT FROM AUTHOR]

This paper discusses the method of measuring and analyzing the parameters of the retina with subsequent diagnosis based on them of pathological changes due to diabetic retinopathy, which is crucial in the field of medicine to help doctors in timely detection and treatment of the disease. The main problem of biomedical image data analysis is insufficient pre-processing of images for further clear determination of informative indicators. This paper explores the application of machine learning and image processing techniques to develop an effective method for the diagnosis of diabetic retinopathy. The main focus is on obtaining the optimal model using machine learning and different types of neural networks. This paper considered and analyzed such methods of image preprocessing as: median filtering, grayscale conversion, cropping of non-informative areas of the image, selection of contours. The classification results of three rules (Classical Neural Networks (CNNs), Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) were analyzed, and through experimental studies it was determined that the ANN performed the task best (accuracy=87.1 %, reliability=84.6 %, sensitivity=91.6 %, specificity=84 %). An information model was obtained to support decision-making in assessing the condition of the retina using the processing of the obtained microscopic images and further analysis of informative parameters, and a database of more than 35,000 samples and informative features of the retina was formed. Given the sufficient quality of classification and the availability of software and hardware, this method can be developed and applied in practice in medical institutions after conducting all the necessary clinical studies [ABSTRACT FROM AUTHOR]

Purpose: This paper explores different machine learning (ML) algorithms for analyzing diffusion nuclear magnetic resonance imaging (dMRI) models when analytical fitting shows restrictions. It reviews various ML techniques for dMRI analysis and evaluates their performance on different b-values range datasets, comparing them with analytical methods. Materials and Methods: After standard fitting for reference, four sets of diffusion-weighted nuclear magnetic resonance images were used to train/test various ML algorithms for prediction of diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), and kurtosis (K). ML classification algorithms, including extra-tree classifier (ETC), logistic regression, C-support vector, extra-gradient boost, and multilayer perceptron (MLP), were used to determine the existence of diffusion parameters (D, D*, f, and K) within single voxels. Regression algorithms, including linear regression, polynomial regression, ridge, lasso, random forest (RF), elastic-net, and support-vector machines, were used to estimate the value of the diffusion parameters. Performance was evaluated using accuracy (ACC), area under the curve (AUC) tests, and cross-validation root mean square error (RMSECV). Computational timing was also assessed. Results: ETC and MLP were the best classifiers, with 94.1% and 91.7%, respectively, for the ACC test and 98.7% and 96.3% for the AUC test. For parameter estimation, RF algorithm yielded the most accurate results The RMSECV percentages were: 8.39% for D, 3.57% for D*, 4.52% for f, and 3.53% for K. After the training phase, the ML methods demonstrated a substantial decrease in computational time, being approximately 232 times faster than the conventional methods. Conclusions: The findings suggest that ML algorithms can enhance the efficiency of dMRI model analysis and offer new perspectives on the microstructural and functional organization of biological tissues. This paper also discusses the limitations and future directions of ML-based dMRI analysis. [ABSTRACT FROM AUTHOR]

The paper brings to the attention of researchers the morphological changes of metal oxides, which appear as a result of the process of physical solar vapor deposition (SPVD) based on experiments carried out at the CNRS-PROMES laboratory, UPR 8521, belonging to the French National Centre for Scientific Research (CNRS). The SPVD process is an innovative tool who has been developed in 2 kW solar furnaces at Odeillo-Font Romeu, France, to synthesis pure and doped nanoparticles, such as: ZnO, CeO2, ZrO2, BiO2, SiO. A variety of metal oxides nanoparticles have been obtained by focusing solar energy on pellets of commercial powders through the controlled process of vaporization followed by condensation directed on a cooper tube or on nanoporous filter. After the micrograph analysis the change of shape and dimension can be observed depending on the type of oxide and the process parameters. It is noticed the appearance of new morphologies, not found in other synthesis methods. The paper brings new information about morphological and dimensional changes after synthesis by physical process which can be essential for researchers, in the choice of methods for the elaboration of nanomaterials. [ABSTRACT FROM AUTHOR]

Eye health has become a global health concern and attracted broad attention. Over the years, researchers have proposed many state‐of‐the‐art convolutional neural networks (CNNs) to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely. However, most existing methods were dedicated to constructing sophisticated CNNs, inevitably ignoring the trade‐off between performance and model complexity. To alleviate this paradox, this paper proposes a lightweight yet efficient network architecture, mixed‐decomposed convolutional network (MDNet), to recognise ocular diseases. In MDNet, we introduce a novel mixed‐decomposed depthwise convolution method, which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low‐resolution and high‐resolution patterns by using fewer computations and fewer parameters. We conduct extensive experiments on the clinical anterior segment optical coherence tomography (AS‐OCT), LAG, University of California San Diego, and CIFAR‐100 datasets. The results show our MDNet achieves a better trade‐off between the performance and model complexity than efficient CNNs including MobileNets and MixNets. Specifically, our MDNet outperforms MobileNets by 2.5% of accuracy by using 22% fewer parameters and 30% fewer computations on the AS‐OCT dataset. [ABSTRACT FROM AUTHOR]

The 230 GHz lightcurves of Sagittarius A* (Sgr A*) predicted by general relativistic magnetohydrodynamics and general relativistic ray-tracing (GRRT) models by the Event Horizon Telescope Collaboration have higher variability M Δ T compared to observations. In this series of papers, we explore the origin of such large brightness variability. In this first paper, we performed large GRRT parameter surveys that span from the optically thin to the optically thick regimes, covering the ion-to-electron temperature ratio under strongly magnetized conditions, R Low, from 1 to 60. We find that increasing R Low can lead to either an increase or a reduction in M Δ T depending on the other model parameters, making it consistent with the observed variability of Sgr A* in some cases. Our analysis of GRRT image snapshots finds that the major contribution to the large M Δ T for the R Low = 1 models comes from the photon ring. However, secondary contributions from the accretion flow are also visible depending on the spin parameter. Our work demonstrates the importance of the electron temperature used for modeling radiatively inefficient accretion flows and places new constraints on the ion-to-electron temperature ratio. A more in-depth analysis for understanding the dependencies of M Δ T on R Low will be performed in subsequent papers. [ABSTRACT FROM AUTHOR]

Brain extraction is an important prerequisite for the automated diagnosis of intracranial lesions and determines, to a certain extent, the accuracy of subsequent lesion identification, localization, and segmentation. To address the problem that the current traditional image segmentation methods are fast in extraction but poor in robustness, while the Full Convolutional Neural Network (FCN) is robust and accurate but relatively slow in extraction, this paper proposes an adaptive mask-based brain extraction method, namely AMBBEM, to achieve brain extraction better. The method first uses threshold segmentation, median filtering, and closed operations for segmentation, generates a mask for the first time, then combines the ResNet50 model, region growing algorithm, and image properties analysis to further segment the mask, and finally complete brain extraction by multiplying the original image and the mask. The algorithm was tested on 22 test sets containing different lesions, and the results showed MPA = 0.9963, MIoU = 0.9924, and MBF = 0.9914, which were equivalent to the extraction effect of the Deeplabv3+ model. However, the method can complete brain extraction of approximately 6.16 head CT images in 1 second, much faster than Deeplabv3+, U-net, and SegNet models. In summary, this method can achieve accurate brain extraction from head CT images more quickly, creating good conditions for subsequent brain volume measurement and feature extraction of intracranial lesions. [ABSTRACT FROM AUTHOR]

Deep Self-Attention Network (Transformer) is an encoder–decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with the inductive bias of convolutional neural network (CNN), Transformer has been gradually applied to medical image processing, including kidney image processing. It has become a hot research topic in recent years. To further explore new ideas and directions in the field of renal image processing, this paper outlines the characteristics of the Transformer network model and summarizes the application of the Transformer-based model in renal image segmentation, classification, detection, electronic medical records, and decision-making systems, and compared with CNN-based renal image processing algorithm, analyzing the advantages and disadvantages of this technique in renal image processing. In addition, this paper gives an outlook on the development trend of Transformer in renal image processing, which provides a valuable reference for a lot of renal image analysis. [ABSTRACT FROM AUTHOR]

The goal of automatic image captioning in computer vision and natural language processing is to provide precise and insightful image captions. The paper provides a novel approach for automatic image captioning, which combines an attention mechanism with a deep learning model based on gated recurrent units (GRUs). Using the attention mechanism, which dynamically weighs the image's attributes, the model may focus on relevant portions of the image during the caption production process. This makes it easier to match related words in the generated captions with the features of the image. The output captions accurately depict word relationships and mimic the sequential structure of natural language through the application of recurrent neural networks of the GRU variety. With a large collection of images and captions, the network is taught to generate rational and contextually relevant descriptions for various image types. By assessing the proposed model with widely-used metrics such as BLEU, METEOR, ROUGE, and CIDEr, we demonstrate its ability to generate high-quality captions. The findings show that the approach outperforms baseline techniques, highlighting the advantages of combining GRU with an attention mechanism for image captioning. The method produces captions that are accurate and convey a deeper understanding of the visual content in the photos, making it highly applicable in real-world applications such as image interpretation, accessibility and content suggestion. [ABSTRACT FROM AUTHOR]

The decade of research concentrating on the area of Eastern Slavonia revealed an abundance of large and complex Middle and Late Neolithic sites. It changed profoundly how we perceive Middle and Late Neolithic settlements, including space, size and organization. The vast majority of these sites were detected through aerial reconnaissance and satellite image analysis. The observation of the sites was followed by intensive field surveys, which confirmed their attribution to the Middle and Late Neolithic period by surface finds. On those confirmed sites in the vicinity of Đakovo, Croatia, a magnetic survey was conducted on five sites, and the results confirmed the presence of large-scale Middle and Late Neolithic settlements with complex spatial organization and enclosure(s). The most complex remains so far are the sites Gorjani, Kremenjača and Topole, which we present in this paper, where one or two settlements remain covering an area of 70 hectares which is currently in the process of formal protection as a cultural landscape by the Ministry of Culture and Media of the Republic of Croatia. The special focus of this paper is the application of remote sensing in the detection, archaeological confirmation and protection of the site of Gorjani Topole. [ABSTRACT FROM AUTHOR]

The approach of using more than one processor to compute in order to overcome the complexity of different medical imaging methods that make up an overall job is known as GPU (graphic processing unit)-based parallel processing. It is extremely important for several medical imaging techniques such as image classification, object detection, image segmentation, registration, and content-based image retrieval, since the GPU-based parallel processing approach allows for time-efficient computation by a software, allowing multiple computations to be completed at once. On the other hand, a non-invasive imaging technology that may depict the shape of an anatomy and the biological advancements of the human body is known as magnetic resonance imaging (MRI). Implementing GPU-based parallel processing approaches in brain MRI analysis with medical imaging techniques might be helpful in achieving immediate and timely image capture. Therefore, this extended review (the extension of the IWBBIO2023 conference paper) offers a thorough overview of the literature with an emphasis on the expanding use of GPU-based parallel processing methods for the medical analysis of brain MRIs with the imaging techniques mentioned above, given the need for quicker computation to acquire early and real-time feedback in medicine. Between 2019 and 2023, we examined the articles in the literature matrix that include the tasks, techniques, MRI sequences, and processing results. As a result, the methods discussed in this review demonstrate the advancements achieved until now in minimizing computing runtime as well as the obstacles and problems still to be solved in the future. [ABSTRACT FROM AUTHOR]

As industrial computerized tomography (ICT) is widely used in the non-destructive testing of a solid rocket motor (SRM), the problem of how to automatically discriminate defect types and measure defect sizes with high accuracy in ICT images of SRM grains needs to be urgently solved. To address the problems of low manual recognition efficiency and data utilization in the ICT image analysis of SRM grains, we proposed an automated defect analysis (ADA) system for ICT images of SRM grains based on the YOLO-V4 model. Using the region proposal of the YOLO-V4 model, a region growing algorithm with automatic selection of seed points was proposed to segment the defect areas of the ICT images of grains. Defect sizes were automatically measured based on the automatic determination of defect types by the YOLO-V4 model. In this paper, the image recognition performance of YOLO-V4, YOLO-V3, and Faster R-CNN models were compared. The results show that the average accuracy (mAP) of the YOLO-V4 model is more than 15% higher than that of the YOLO-V3 and Faster R-CNN models, the F1-score is 0.970, and the detection time per image is 0.152 s. The ADA system can measure defect sizes with an error of less than 10%. Tests show that the system proposed in this paper can automatically analyze the defects in ICT images of SRM grains and has certain application value. [ABSTRACT FROM AUTHOR]

Background and Aim: Ultrasound (US) imaging is increasingly preferred over other more invasive modalities in preclinical studies using animal models. However, this technique has some limitations, mainly related to operator dependence. To overcome some of the current drawbacks, sophisticated data processing models are proposed, in particular artificial intelligence models based on deep learning (DL) networks. This systematic review aims to overview the application of DL algorithms in assisting US analysis of images acquired in in vivo preclinical studies on animal models. Methods: A literature search was conducted using the Scopus and PubMed databases. Studies published from January 2012 to November 2022 that developed DL models on US images acquired in preclinical/animal experimental scenarios were eligible for inclusion. This review was conducted according to PRISMA guidelines. Results: Fifty-six studies were enrolled and classified into five groups based on the anatomical district in which the DL models were used. Sixteen studies focused on the cardiovascular system and fourteen on the abdominal organs. Five studies applied DL networks to images of the musculoskeletal system and eight investigations involved the brain. Thirteen papers, grouped under a miscellaneous category, proposed heterogeneous applications adopting DL systems. Our analysis also highlighted that murine models were the most common animals used in in vivo studies applying DL to US imaging. Conclusion: DL techniques show great potential in terms of US images acquired in preclinical studies using animal models. However, in this scenario, these techniques are still in their early stages, and there is room for improvement, such as sample sizes, data preprocessing, and model interpretability. [ABSTRACT FROM AUTHOR]

Accidents and Osteoporosis are two prime reasons of femur bone fractures. Osteoporosis causes weakening of bone in elder patients. Proper treatment during surgery will help patients for early rehabilitation. This paper portrays a review of concerned literature identified with Biomedical Image analysis and modelling of femur bone fractures for an operative planning. Paper intended to review literature for Biomedical image preprocessing to identify the proper region of Interest (ROI) by using an appropriate image modality, building up a 3D model of femur bone structure using image database with software, analysis of created 3D model for applying implant and its parametric analysis for evaluation of virtual surgery and operative planning to surgeon depending on assessment report produced utilising earlier advances. Such sort of work will keep away from mismatches of fracture which may prompt genuine implanted complications for patients including secondary fractures, poor anatomical alignment and bone debilitating too. Paper will summarise conclusion of concern literature to guide new researcher to start with [ABSTRACT FROM AUTHOR]

Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation. With the continuous development of artificial intelligence technology, the use of deep learning methods for interpreting remote-sensing images has matured. Existing neural networks disregard the spatial relationship between two targets in remote sensing images. Semantic segmentation models that combine convolutional neural networks (CNNs) and graph convolutional neural networks (GCNs) cause a lack of feature boundaries, which leads to the unsatisfactory segmentation of various target feature boundaries. In this paper, we propose a new semantic segmentation model for remote sensing images (called DGCN hereinafter), which combines deep semantic segmentation networks (DSSN) and GCNs. In the GCN module, a loss function for boundary information is employed to optimize the learning of spatial relationship features between the target features and their relationships. A hierarchical fusion method is utilized for feature fusion and classification to optimize the spatial relationship information in the original feature information. Extensive experiments on ISPRS 2D and DeepGlobe semantic segmentation datasets show that compared with the existing semantic segmentation models of remote sensing images, the DGCN significantly optimizes the segmentation effect of feature boundaries, effectively reduces the noise in the segmentation results and improves the segmentation accuracy, which demonstrates the advancements of our model. [ABSTRACT FROM AUTHOR]

The corrugated board is a versatile and durable material that is widely used in the packaging industry. Its unique structure provides strength and cushioning, while its recyclability and bio-degradability make it an environmentally friendly option. The strength of the corrugated board depends on many factors, including the type of individual papers on flat and corrugated layers, the geometry of the flute, temperature, humidity, etc. This paper presents a new approach to the analysis of the geometric features of corrugated boards. The experimental set used in the work and the created software are characterized by high reliability and precision of measurement thanks to the use of an identification procedure based on image analysis and a genetic algorithm. In the applied procedure, the thickness of each layer, corrugated cardboard thickness, flute height and center line are calculated. In most cases, the proposed algorithm successfully approximated these parameters. [ABSTRACT FROM AUTHOR]

With the development of society and the improvement of people's living standards, ecotourism based on forest ecological environment has become an urgent need of urban and rural people. Forest leisure tourism is different from traditional sightseeing tourism. The differences between them are not only reflected in consumption purpose, consumption behavior, consumption grade and consumption form, but also reflected in the differences between tourism products, service system and service quality demand. Therefore, in order to develop forest leisure tourism, we must first fully understand the objective status quo of the leisure tourism market and the real needs of the market, and correctly judge the basic characteristics of the current forest leisure tourism market. At present, the competition of tourism market is upgraded from the competition of tourism price and tourism product to the competition of tourism brand. However, the resource utilization efficiency of forest park tourism is insufficient, and the internal management and external marketing are difficult to adapt to the changes of the market, thus the image and attraction of forest park tourism need to be improved urgently. Therefore, based on remote sensing image analysis and neural computing model, this paper constructs a forest ecotourism evaluation scheme. We designed the novel cloud-based mobile edge computing model to construct the efficient scenario for the prediction. The experimental results show that the model proposed in this paper can effectively evaluate the development plan of forest eco-tourism. [ABSTRACT FROM AUTHOR]

The study of ultrasonic image deblurring is of high research value and practical significance, which can provide significantly useful medical pathological information for doctors and patients. Ultrasonic images are prone to speckle noise or blur. Real-time deblurring of ultrasonic images is a common problem in medical circles in China and globally. In this paper, based on the causes of ultrasound image degradation and its degradation model, the problem of ultrasound image deblurring is studied in depth. A new regularization constraint model is given, and the algorithm of ultrasound image deblurring with double constraints is effective. Through the experiment, the image quality evaluation index is used to evaluate the experimental results. The improved algorithm proposed in this paper improves the peak signal-to-noise ratio by 7%, the structural similarity value by 6%, the visual information fidelity by 14%, the feature similarity value by 4%, the resolution gain by 4%, and the new blind image quality evaluation value by 22%. The key information of the image can be kept faithfully. Compared with the traditional methods, the proposed method enhances the identification of the focus area in ultrasound images, provides a fast, efficient and complete deblurring method for medical image analysis, and faithfully retains and restores the required information in ultrasound images. [ABSTRACT FROM AUTHOR]

In a colonoscopy, accurate computer-aided polyp detection and segmentation can help endoscopists to remove abnormal tissue. This reduces the chance of polyps developing into cancer, which is of great importance. In this paper, we propose a neural network (parallel residual atrous pyramid network or PRAPNet) based on a parallel residual atrous pyramid module for the segmentation of intestinal polyp detection. We made full use of the global contextual information of the different regions by the proposed parallel residual atrous pyramid module. The experimental results showed that our proposed global prior module could effectively achieve better segmentation results in the intestinal polyp segmentation task compared with the previously published results. The mean intersection over union and dice coefficient of the model in the Kvasir-SEG dataset were 90.4% and 94.2%, respectively. The experimental results outperformed the scores achieved by the seven classical segmentation network models (U-Net, U-Net++, ResUNet++, praNet, CaraNet, SFFormer-L, TransFuse-L). [ABSTRACT FROM AUTHOR]

Uyghur text detection and recognition in images with simple backgrounds is still a challenging task for Uyghur image content analysis. In this paper, we propose a new effective Uyghur text detection method based on channel-enhanced MSERs and the CNN classification model. In order to extract more complete text components, a new text candidate region extraction algorithm is put forward, which is based on the channel-enhanced MSERs according to the characteristics of Uyghur text. In order to effectively prune the non-text regions, we design a CNN classification network according to the LeNet-5, which gains the description characteristics automatically and avoids the tedious and low efficiency artificial characteristic extraction work. For Uyghur text recognition in images, we improved the traditional CRNN network, and to verify its effectiveness, the networks trained on a synthetic dataset and evaluated on the text recognition datasets. The experimental results indicated that the Uyghur text detection method in this paper is robust and applicable, and the recognition result by improvedCRNN was better than the original CRNN network. [ABSTRACT FROM AUTHOR]

The paper considers and analyzes the main scientific results of the 15th International Conference on Pattern Recognition and Information Processing (PRIP-2021), Minsk, Republic of Belarus, September 2021. The history of this series of conferences is outlined and its significant role in the development of the theory and practice of image analysis, pattern recognition, and artificial intelligence is indicated. A list of articles in the special issue prepared on the basis of reports selected by the PRIP-2021 program committee is provided. [ABSTRACT FROM AUTHOR]

Mobile target tracking remains a significant issue in smart cities. Due to complex changes in time and space of targets, real-time tracking remains a challenging problem. As a result, this paper proposes a real-time tracking approach for moving objects by combining the advantages of YOLOv7 and SORT algorithms. First, we use the YOLOv7 algorithm for object detection, which has the characteristics of high accuracy and efficiency. Then, we apply the SORT algorithm to the target tracking stage, which estimates and updates the target state through Kalman filtering. The collaborative function of the two parts is expected to achieve high-quality tracking of moving targets. Besides, this paper also demonstrates experiments and analysis on image datasets. The experimental results show that the proposed algorithm has achieved good performance in real-time tracking of moving targets. Compared with traditional methods, it can more accurately predict the position and trajectory of targets and has better real-time performance. In addition, the proposed algorithm is equally effective for target tracking in complex scenes, such as multi-target tracking and target occlusion. Future research can further optimize the performance of algorithms to cope with more complex scenarios and problems. [ABSTRACT FROM AUTHOR]

Asbestos has been used extensively in several applications. Once it is known as a dangerous mineral, its usage has been prohibited and its identification and remediation play a very important role from the health safety point of view. Nowadays, deep learning techniques are used in many applications, especially for image analysis. They can be used to significantly reduce the time and cost of traditional detection methods. In this paper, taking advantage of asbestos spectral signature, a deep neural network is introduced in order to implement a complete methodology to identify asbestos roofings starting from hyperspectral images in a regional context. The novelty of the proposed approach is a dynamic mixing of models with different features, in order to accommodate classifications on widespread areas of both urban and rural territories. Indeed, the dataset used during the experiments described in this paper is a large one, consisting of many wide hyperspectral images with a geometric resolution of 1 m and with 186 bands, covering an entire region of approximately 8,000 km2${\rm km}^2$. This is in contrast to other works in the literature where the analyzed areas are limited in size and uniform for physical features. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is often affected by other high-power electromagnetic devices during ground observation, which causes unintentional radio frequency interference (RFI) with the acquired echo, bringing adverse effects into data processing and image interpretation. When faced with the task of screening massive SAR data, there is an urgent need for the global perception and detection of interference. The existing RFI detection method usually only uses a single type of data for detection, ignoring the information association between the data at all levels of the real SAR product, resulting in some computational redundancy. Meanwhile, current deep learning-based algorithms are often unable to locate the range of RFI coverage in the azimuth direction. Therefore, a novel RFI processing framework from quick-looks to single-look complex (SLC) data and then to raw echo is proposed. We take the data of Sentinel-1 terrain observation with progressive scan (TOPS) mode as an example. By combining the statistics-assisted network with the sliding-window algorithm and the error-tolerant training strategy, it is possible to accurately detect and locate RFI in the quick looks of an SLC product. Then, through the analysis of the TOPSAR imaging principle, the position of the RFI in the SLC image is preliminarily confirmed. The possible distribution of the RFI in the corresponding raw echo is further inferred, which is one of the first attempts to use spaceborne SAR data to elucidate the RFI location mapping relationship between image data and raw echo. Compared with directly detecting all of the SLC data, the time for the proposed framework to determine the RFI distribution in the SLC data can be shortened by 53.526%. All the research in this paper is conducted on Sentinel-1 real data, which verify the feasibility and effectiveness of the proposed framework for radio frequency signals monitoring in advanced spaceborne SAR systems. [ABSTRACT FROM AUTHOR]

Axial estimation is an important task for detecting non-cooperative space targets in orbit, with inverse synthetic aperture radar (ISAR) imaging serving as a fundamental approach to facilitate this process. However, most of the existing axial estimation methods usually rely on manually extracting and matching features of key corner points or linear structures in the images, which may result in a degradation in estimation accuracy. To address these issues, this paper proposes an axial estimation method for spaceborne targets via ISAR image sequences based on a regression network. Firstly, taking the ALOS satellite as an example, its Computer-Aided Design (CAD) model is constructed through a prior analysis of its structural features. Subsequently, target echoes are generated using electromagnetic simulation software, followed by imaging processing, analysis of imaging characteristics, and the determination of axial labels. Finally, in contrast to traditional classification approaches, this study introduces a straightforward yet effective regression network specifically designed for ISAR image sequences. This network transforms the classification loss into a loss function constrained by the minimum mean square error, which can be utilized to adaptively perform the feature extraction and estimation of axial parameters. The effectiveness of the proposed method is validated through both electromagnetic simulations and experimental data. [ABSTRACT FROM AUTHOR]

The challenge of fracture segmentation remains a significant obstacle in imaging logging interpretation within the current oil and gas exploration and development field. However, existing image segmentation algorithms still encounter issues related to accuracy, speed, and robustness, as well as a tendency to misdetect or overlook small fractures when applied to logging image fracture segmentation tasks. To address these challenges comprehensively, this paper proposes an end-to-end fracture segmentation algorithm named SWSDS-Net. This algorithm is built upon the UNet architecture and incorporates the SimAM with slicing (SWS) attention mechanism along with the deformable strip convolution (DSCN) module. The SWS introduces a fully 3D attention mechanism that effectively learns the weights of each neuron in the feature map, enabling better capture of fracture features while ensuring fair attention and enhancement for both large and small objects. Additionally, the deformable properties of DSCN allow for adaptive sampling based on fracture shapes, effectively tackling challenges posed by varying fracture shapes and enhancing segmentation robustness. Experimental results demonstrate that SWSDS-Net achieves optimal performance across all evaluation metrics in this task, delivering superior visual results in fracture segmentation while successfully overcoming limitations present in existing algorithms such as complex shapes, noise interference, and low-quality images. Moreover, serving as a lightweight network solution enables SWSDS-Net's deployment on mobile devices at remote sites—an advancement that lays a solid foundation for interpreting logging data and promotes deep learning technology application within traditional industrial scenarios. [ABSTRACT FROM AUTHOR]

Xerostomia, commonly known as dry mouth, is characterized by reduced salivary secretion, which can lead to various oral health issues and discomfort. In this paper, we propose a novel, non-invasive method for predicting xerostomia through the analysis of tongue images. To predict salivary gland secretion from tongue images, we collected images from patients who visited the hospital with complaints of dry mouth and measured their saliva secretion. Features were extracted from these tongue images, and correlation analysis was performed using machine learning techniques to assess the relationship between the extracted features and measured saliva secretion. We obtained tongue images and saliva secretion measurements from 176 patients. Images were cropped to 100 × 100 pixels, resulting in 462 features. The dataset was divided into training and test sets, consisting of 160 and 16 samples, respectively. The correlation coefficients for the training and test datasets were 0.9496 and 0.9415, respectively, while the correlation coefficient for the entire dataset was 0.9482. The estimated linear equation was y = 0.9244x + 2.1664. This study aimed to predict salivary gland secretion based on tongue images. By extracting features from color images and employing a neural network machine learning model, we estimated salivary gland secretion. With a sufficiently large dataset of tongue images, further advancements in regression analysis using deep learning techniques could enhance the accuracy of these predictions. [ABSTRACT FROM AUTHOR]

This paper presents an efficient weed detection method based on the latent diffusion transformer, aimed at enhancing the accuracy and applicability of agricultural image analysis. The experimental results demonstrate that the proposed model achieves a precision of 0.92, a recall of 0.89, an accuracy of 0.91, a mean average precision (mAP) of 0.91, and an F1 score of 0.90, indicating its outstanding performance in complex scenarios. Additionally, ablation experiments reveal that the latent-space-based diffusion subnetwork outperforms traditional models, such as the the residual diffusion network, which has a precision of only 0.75. By combining latent space feature extraction with self-attention mechanisms, the constructed lightweight model can respond quickly on mobile devices, showcasing the significant potential of deep learning technologies in agricultural applications. Future research will focus on data diversity and model interpretability to further enhance the model's adaptability and user trust. [ABSTRACT FROM AUTHOR]

POSIT is a numerical system consists of sign, regime, exponential, and fraction bits to overcome some limitations of the IEEE‐754 floating point (FP) representation. This work proposes a technique to hide critical information in the least significant bits (LSBs) of the POSIT FP representation by exploiting approximate computing (AC). The proposed technique, called information hiding using POSIT and LSB (IHUPL), explores the opportunities offered by both the POSIT representation and the AC to hide information with a minimum loss in accuracy and other performance metrics. IHUPL offers two options for hiding information: either by embedding one digit of each character into each pixel or by embedding all the digits of the character at the same time into each pixel of the alpha‐red, green, blue, or black/white image. Experimental results are evaluated for benchmark images and showed that IHUPL enhanced the accuracy of embedding data into LSB of POSIT FP by an average of 5%, the image quality improvement rates of IHUPL are 19% and 16% for options 1 and 2, respectively. Besides the encoding method using IHUPL, the paper outlines an extraction decoding technique that saves the original replacement bits in a key‐image to recover the hidden security message. [ABSTRACT FROM AUTHOR]

Explainable Artificial Intelligence (XAI) is an emerging machine learning field that has been successful in medical image analysis. Interpretable approaches are able to "unbox" the black-box decisions made by AI systems, aiding medical doctors to justify their diagnostics better. In this paper, we analyze the performance of three different XAI strategies for medical image analysis in ophthalmology. We consider a multimodal deep learning model that combines optical coherence tomography (OCT) and infrared reflectance (IR) imaging for the diagnosis of age-related macular degeneration (AMD). The classification model is able to achieve an accuracy of 0.94, performing better than other unimodal alternatives. We analyze the XAI methods in terms of their ability to identify retinal damage and ease of interpretation, concluding that grad-CAM and guided grad-CAM can be combined to have both a coarse visual justification and a fine-grained analysis of the retinal layers. We provide important insights and recommendations for practitioners on how to design automated and explainable screening tests based on the combination of two image sources. [ABSTRACT FROM AUTHOR]

The Irpinia Fault, also known as the Monte Marzano Fault System, located in the Southern Apennines (Italy), is one of the most seismically active structures in the Mediterranean. It is the source of the 1980, Ms 6.9, multi-segment rupture earthquake that caused significant damage and nearly 3,000 casualties. Paleoseismological surveys indicate that this structure has generated at least four Mw ~ 7 surface-rupturing earthquakes in the past 2 ka. This paper presents a comprehensive, high-resolution geophysical investigation focused on the southernmost fault segment of the Monte Marzano Fault System, i.e., the Pantano-Ripa Rossa Fault, outcropping within the Pantano di San Gregorio Magno intramontane basin. The project, named TEst Site IRpinia fAult (TESIRA), was supported by the University of Napoli Federico II to study the near-surface structure of this intra-basin fault splay that repeatedly ruptured co-seismically in the past thousands of years. Our imaging approach included 2D and 3D electrical and seismic surveys, gravimetry, 3D FullWaver electrical tomography, drone-borne GPR and magnetic surveys, and CO2 soil flux assessment across the surface rupture. This multidisciplinary investigation improved our understanding of the basin shallow structure, providing an image of a rather complex subsurface fault and basin geometry. Seismic data suggest that fault activity at the Pantano segment of MMFS is characterized by a near-surface cumulative displacement greater than previous estimations, calling into question earlier assumptions about the timing of its activation. Despite some challenges with our drone-mounted survey equipment, the integrated dataset provides a comprehensive and reliable image of the subsurface structure. This work demonstrates the utility of developing an integrated approach at high-resolution geophysical imaging and interpretation of fault zones with weak morphological expressions. [ABSTRACT FROM AUTHOR]

Objectives: With advancements in computer vision technology, accurately identifying and segmenting various components in food images has become essential for food nutrition analysis and promoting healthier diet management. However, most existing image segmentation models rely solely on a single image input, which often struggles to capture subtle distinguishing features in food images with minimal visual differences, ultimately impacting segmentation accuracy. This paper addressed the limitations of single-modality approaches in segmentation tasks by incorporating text information to provide richer contextual data for the model. Additionally, it leveraged self-distillation techniques to guide the model in effectively segmenting food images. Methods: This paper proposed a multi-modal self-distillation segmentation model guided by ingredient information to improve food image segmentation. The model leveraged the comparative language pre-training model (CLIP) to capture ingredient information and fused it with image knowledge. By combining the strengths of the diffusion model in dense prediction, the model achieved accurate segmentation of food images. Results: When evaluated on the benchmark dataset FoodSeg103, the model achieved an mIoU of 47.93%, surpassing the current best-performing FoodSAM model by 1.51%. On the UEC-FoodPIX Complete benchmark dataset, the mIoU reached 75.13%, outperforming the FoodSAM model by 8.99%. Conclusions: The proposed multi-modal self-distillation network demonstrated strong performance in food image segmentation, showcasing the effective role of ingredient information in guiding segmentation tasks. This approach significantly improves segmentation accuracy and presents a promising solution for food image analysis. [ABSTRACT FROM AUTHOR]

Due to the diversity of image sources, content-based multi-source image fusion and retrieval have shown promising capabilities in computer vision tasks, and especially when applied in Computer-Aided Diagnosis (CAD) to automate and improve the accuracy of medical image analysis. The combination of computer vision and CAD systems has the potential to revolutionize healthcare by augmenting the expertise of clinicians, improving overall diagnostic accuracy and helping experts in the clinical decision-making process by classifying and retrieving similar annotated clinical images to a given query. In the context of multi-view mammography interpretation, the concept of multi-view feature fusion has recently been studied to improve retrieval performance while effectively guaranteeing the complementarity of both MLO and CC views. However, conventional multi-view feature fusion makes descriptors long and lacks to take into consideration the relationship between descriptors. To deal with this issue, we propose two hierarchical multi-view feature fusion methods, for multi-view mammogram retrieval, based on the Canonical Correlation Analysis (CCA), which is the most commonly used multivariate parametric test. In fact, we have adapted CCA to determine the relationship between two descriptors by processing latent correlation factors. Moreover, after extracting descriptors for each view, a comparative study of texture and shape fusion descriptors is proposed in order to identify the more discriminative features for multi-view mammogram retrieval. Then, a query-dependent distance metric preserving both visual resemblance and semantic similarity is carried out to dynamically determine the more appropriate distance measure for each query image. Extensive experiments on the challenging Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) have demonstrated the effectiveness of the proposed hierarchical multi-view feature fusion for mammogram retrieval, which outperforms the performance achieved either by conventional fused information or by single view information. To improve the transparency of our paper, the source code of the proposed method and the related dataset (including readme files) are publicly accessible through the following GitHub link: https://github.com/ABDERRAHIMMAR/Multi-View-Feature-Fusion-for-Mammogram-Retrieval. This open-access resource empowers researchers and practitioners to delve deeper into our methodology, fostering collaboration and advancements in the field of computer-aided diagnosis and medical image analysis. [ABSTRACT FROM AUTHOR]

This research study delves into the hydrodynamic frictional characteristics of fouled panels coated with different types of coatings, investigating how fouling coverage and surface roughness influence drag. The investigation incorporates data on the overall percentage coverage of fouling, as well as roughness measurements obtained through a 3D profilometer. Drag data collected from a flowcell simulation of real-world flow conditions complements these measurements. Notably, the determination of the level of fouling leverages the capabilities of CIE L*a*b as an image analysis method, focusing on the overall coverage rather than individual fouling species. The objective is to illustrate how fouled panels perform under varying flow and coating conditions compared to their clean counterparts. Furthermore, the paper proposes a roughness scaling approach that considers both the percentage coverage and measured areal roughness for each coating type, encompassing both fouled and unfouled areas. This approach provides valuable insights into the combined effects of fouling and surface roughness on hydrodynamic performance, enhancing our understanding of the intricate interplay between these factors. [ABSTRACT FROM AUTHOR]

The joint classification of hyperspectral imagery (HSI) and LiDAR data is an important task in the field of remote sensing image interpretation. Traditional classification methods, such as support vector machine (SVM) and random forest (RF), have difficulty capturing the complex spectral–spatial–elevation correlation information. Recently, important progress has been made in HSI-LiDAR classification using Convolutional Neural Networks (CNNs) and Transformers. However, due to the large spatial extent of remote sensing images, the vanilla Transformer and CNNs struggle to effectively capture global context. Moreover, the weak misalignment between multi-source data poses challenges for their effective fusion. In this paper, we introduce AFA–Mamba, an Adaptive Feature Alignment Network with a Global–Local Mamba design that achieves accurate land cover classification. It contains two main core designs: (1) We first propose a Global–Local Mamba encoder, which effectively models context through a 2D selective scanning mechanism while introducing local bias to enhance the spatial features of local objects. (2) We also propose an SSE Adaptive Alignment and Fusion (A2F) module to adaptively adjust the relative positions between multi-source features. This module establishes a guided subspace to accurately estimate feature-level offsets, enabling optimal fusion. As a result, our AFA–Mamba consistently outperforms state-of-the-art multi-source fusion classification approaches across multiple datasets. [ABSTRACT FROM AUTHOR]

Semantic change detection (SCD) is a newly important topic in the field of remote sensing (RS) image interpretation since it provides semantic comprehension for bi-temporal RS images via predicting change regions and change types and has great significance for urban planning and ecological monitoring. With the availability of large scale bi-temporal RS datasets, various models based on deep learning (DL) have been widely applied in SCD. Since convolution operators in DL extracts two-dimensional feature matrices in the spatial dimension of images and stack feature matrices in the dimension termed the channel, feature maps of images are tri-dimensional. However, recent SCD models usually overlook the stereoscopic property of feature maps. Firstly, recent SCD models are usually limited in capturing spatial global features in the process of bi-temporal global feature extraction and overlook the global channel features. Meanwhile, recent SCD models only focus on spatial cross-temporal interaction in the process of change feature perception and ignore the channel interaction. Thus, to address above two challenges, a novel fine-grained feature perception network (FFPNet) is proposed in this paper, which employs the Omni Transformer (OiT) module to capture bi-temporal channel–spatial global features before utilizing the Omni Cross-Perception (OCP) module to achieve channel–spatial interaction between cross-temporal features. According to the experiments on the SECOND dataset and the LandsatSCD dataset, our FFPNet reaches competitive performance on both countryside and urban scenes compared with recent typical SCD models. [ABSTRACT FROM AUTHOR]

Body circumference fitting model is a key technology for personalization in smart manufacturing by the apparel industry. First, the paper collected front and side images plus manual measurement data of 122 young men. Second, through contour recognition and feature point detection of the collected images, the width and thickness of each body part plus the height were estimated. Then, a correlation analysis of these known factors was conducted. The paper proposed that weight was included in initial regression model for circumferences as an additional independent variable along with width and thickness. Finally, according body type classification, a multivariate regression model was established. After validation, the values predicted by the multivariate regression model were within ±1.5 cm of the manual values for 90.9% of the individuals on average. The paper provides theoretical support for body circumference fitting and may also be beneficial to the development of the smart manufacturing of garments. [ABSTRACT FROM AUTHOR]

Hot surfaces in industrial processes and automotive systems present a remarkable fire hazard. Lubricating oil is a widely used oil in these scenarios. Quantifying the ignition characteristics and flame behavior of lubricating oil on hot surfaces is critical for enhancing fire safety in energy-related applications. This paper utilizes a self-developed experimental platform for the hot surface ignition to systematically conduct combustion tests on lubricating oil with varying volumes at different surface temperatures. Through statistical analysis and image processing, the ignition temperature, flame height, flame propagation velocity, and flame temperature were examined to assess the fire risk of a hot surface ignition. The results demonstrate that the ignition and combustion process of lubricating oil on hot surfaces can be categorized into five stages. The ignition temperature decreases as the oil volume increases. The flame height and flame propagation velocity are positively correlated with the hot surface temperature. The maximum flame height increases with the increase in the oil volumes. When the flame height reaches the maximum value, the flame area is the largest, and the average flame temperature is 1540.30 °C, showing a greater fire risk. When the oil content is 0.2 mL, the flame propagation velocity is the fastest, reaching 3.81 m/s. Meanwhile, the flame is very close to the oil pipe, which may cause a secondary fire. Therefore, hot surface ignition of lubricating oil poses a direct threat to vehicle safety. [ABSTRACT FROM AUTHOR]

Super-resolution is a technique for generating a high-resolution image or video from a low-resolution counterpart by predicting natural and realistic texture information. It has various applications such as medical image analysis, surveillance, remote sensing, etc. However, traditional single-image super-resolution methods can lead to a blurry visual effect. Reference-based super-resolution methods have been proposed to recover detailed information accurately. In reference-based methods, a high-resolution image is also used as a reference in addition to the low-resolution input image. Reference-based methods aim at transferring high-resolution textures from the reference image to produce visually pleasing results. However, it requires texture alignment between low-resolution and reference images, which generally requires a lot of time and memory. This paper proposes a lightweight reference-based video super-resolution method using deformable convolution. The proposed method makes the reference-based super-resolution a technology that can be easily used even in environments with limited computational resources. To verify the effectiveness of the proposed method, we conducted experiments to compare the proposed method with baseline methods in two aspects: runtime and memory usage, in addition to accuracy. The experimental results showed that the proposed method restored a high-quality super-resolved image from a very low-resolution level in 0.0138 s using two NVIDIA RTX 2080 GPUs, much faster than the representative method. [ABSTRACT FROM AUTHOR]