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Finding patches is a crucial step in a pavement performance survey. The study develops a machine learning and image processing algorithm-established automatic method for identifying asphalt pavement patches. The GrayLevel Co-Occurrence Matrix and image texture-based features derived from color channel statistics are used as input parameters to describe the condition of the pavement. For feature extraction, image processing methods like the projective integral of images, steerable filters, and an improved image thresholding method were used. Support Vector Machine is employed for categorizing the differentiating patched regions from non-patch ones. The suggested combination of image texture analytical methods has been trained using an IA data set created from 400 image samples. The feature set, which combines the characteristics of cracked objects with those generated from the projective integral, can produce the desired result. To make the model's execution simpler, a patch recognition program was created and implemented in MATLAB. As a result, the recently created method has the potential to be an instrument for traffic management organizations through the assessment of pavement performance. The results of the experiments indicate that the newly developed method may achieve excellent accuracy prediction results with a classifier performance rate of about 96%. Such a method could enable transportation authorities to assess the state of asphalt pavement. [ABSTRACT FROM AUTHOR]

Concerns regarding microplastic (MP) contamination in aquatic ecosystems and its impact on seafood require a better understanding of human dietary MP exposure including extensive monitoring. While conventional techniques for MP analysis like infrared or Raman microspectroscopy provide detailed particle information, they are limited by low sample throughput, particularly when dealing with high particle numbers in seafood due to matrix-related residues. Consequently, more rapid techniques need to be developed to meet the requirements of large-scale monitoring. This study focused on semi-automated fluorescence imaging analysis after Nile red staining for rapid MP screening in seafood. By implementing RGB-based fluorescence threshold values, the need for high operator expertise to prevent misclassification was addressed. Food-relevant MP was identified with over 95% probability and differentiated from natural polymers with a 1% error rate. Comparison with laser direct infrared imaging (LDIR), a state-of-the-art method for rapid MP analysis, showed similar particle counts, indicating plausible results. However, highly variable recovery rates attributed to inhomogeneous particle spiking experiments highlight the need for future development of certified reference material including sample preparation. The proposed method demonstrated suitability of high throughput analysis for seafood samples, requiring 0.02–0.06 h/cm2 filter surface compared to 4.5–14.7 h/cm with LDIR analysis. Overall, the method holds promise as a screening tool for more accurate yet resource-intensive MP analysis methods such as spectroscopic or thermoanalytical techniques. [ABSTRACT FROM AUTHOR]

Increasing the sensitivity of image sensors is a major challenge for current imaging technology. Researchers are tackling it because highly sensitive sensors enable objects to be recognized even in dark environments, which is critical for today's smartphones, wearable devices, and automobiles. Unfortunately, conventional image-sensor architectures use light-absorptive color filters on every pixel, which fundamentally limits the detected light power per pixel. Recent advances in optical metasurfaces have led to the creation of pixelated light-transmissive color splitters with the potential to enhance sensor sensitivity. These metasurfaces can be used instead of color filters to distinguish primary colors, and unlike color filters, they can direct almost all of the incident light to the photodetectors, thereby maximizing the detectable light power. This review focuses on such metasurface-based color splitters enabling high-sensitivity color-image sensors. Their underlying principles are introduced with a focus on dispersion engineering. Then, their capabilities as optical elements are assessed on the basis of our recent findings. Finally, it is discussed how they can be used to create high-sensitivity color-image sensors. [ABSTRACT FROM AUTHOR]

With the increasing popularity of machine learning (ML) in image processing, privacy concerns have emerged as a significant issue in deploying and using ML services. However, current privacy protection approaches often require computationally expensive training from scratch or extensive fine-tuning of models, posing significant barriers to the development of privacy-conscious models, particularly for smaller organizations seeking to comply with data privacy laws. In this paper, we address the privacy challenges in computer vision by investigating the effectiveness of two recent fine-tuning methods, Model Reprogramming and Low-Rank Adaptation. We adapt these techniques to provide attribute protection for pre-trained models, minimizing computational overhead and training time. Specifically, we modify the models to produce privacy-preserving latent representations of images that cannot be used to identify unintended attributes. We integrate these methods into an adversarial min–max framework, allowing us to conceal sensitive information from feature outputs without extensive modifications to the pre-trained model, but rather focusing on a small set of new parameters. We demonstrate the effectiveness of our methods by conducting experiments on the CelebA dataset, achieving state-of-the-art performance while significantly reducing computational complexity and cost. Our research provides a valuable contribution to the field of computer vision and privacy, offering practical solutions to enhance the privacy of machine learning services without compromising efficiency. [ABSTRACT FROM AUTHOR]

Rock strength parameters such as uniaxial compressive strength and modulus of elasticity are crucial parameters in designing rock engineering structures. Owing to the importance of the aforementioned parameters, in this paper, image processing technique is coupled with artificial neural network (ANN) method for assessing the uniaxial compressive strength and modulus of elasticity of sandstones. For this reason, 102 core sandstone samples were prepared. Subsequently petrographic analyses and imaging operation for 102 images were performed. Principal component analysis was then conducted for feature reduction purposes. At last, an ANN model, which received its input data from image processing technique, was constructed for assessing the UCS and E of sandstone samples. Overall, the best performance of the network was obtained when 10 hidden nodes were used. The correlation coefficient (R) values of 0.9722 and 0.97062 for UCS and E, respectively, suggest the feasibility of the proposed model. [ABSTRACT FROM AUTHOR]

The segmentation of pathological image is an indispensable content in the cancerous diagnosis and grading, which is provided to doctors for the location and quantitative analysis of pathologically altered tissue. However, pathological whole slide image (WSI) generally has gigapixel size and huge region-level objective to be segmented. Extracting patches from WSI can address the limitation of computer memory, but the integrity of target is hence affected. Moreover, supervised learning methods require manually annotated labels for training, which is laborious and time-consuming. Thus, we studied a novel weakly supervised learning (WSL)-based end-to-end framework for semantic segmentation of cancerous area in WSI. The proposed framework is based on the block-level segmentation of convolutional neural network (CNN), while CNN is required to integrate the global average pooling layer and single fully connected layer as WSL-CNN. Class activation map and dense conditional random field (DenseCRF) are adapted to realize pixel-level segmentation of the cancerous area in patch, which is incorporated into the classification process of WSL-CNN. The hierarchically double use of DenseCRF effectively improves the precision of semantic segmentation. A region-based annotation method and a flexible method of constructing training dataset are proposed to reduce the workload of annotation. Experiments show that the block-level segmentation of CNNs has better performance than the pixel-level segmentation of fully convolutional networks, ResNet50 is the best one that achieves F1 score of 0.87426, Jaccard score of 0.78079, Recall of 0.94251 and Precision of 0.82182. The proposed framework can effectively refine the block-level prediction as semantic segmentation without pixel-level label. The precision of all tested CNNs get improved in the experiments, with WSL-ResNet50 achieving F1 score of 0.90630, Jaccard score of 0.83230, Recall of 0.92051 and Precision of 0.89789. We propose a complete end-to-end framework, including the specific structure of neural network, the construction of training dataset, the prediction method using neural network and the post-processing. CNN-like architectures can be widely transplanted into this framework to realize semantic segmentation, solving the problem of insufficient label of large-scale medical image to a certain extent. [ABSTRACT FROM AUTHOR]

The article explores advanced image processing techniques for pigment discrimination in rock art paintings, emphasizing color separation using RGB (red, green, blue) and LHCUv (Luminance, Hue, Chroma) imagery. It highlights the use of dimensionality reduction methods such as Principal Components Analisys PCA and Independent Component Analysis (ICA), with a focus on Gaussian Mixture Models (GMM) for probabilistic classification of image elements. This approach, applied to the Chomache archaeological site on the northernmost coast of the Atacama Desert in Chile, reveals previously undetected motifs and details, offering a nuanced perspective in rock art documentation and analysis. This proposal reinforces the value of rock art panel not only as a finished product but as a process. [ABSTRACT FROM AUTHOR]

Cancer Stem Cells presumably drive tumor growth and resistance to conventional cancer treatments. From a previous computational model, we inferred that these cells are not uniformly distributed in the bulk of a tumorsphere. To confirm this result, we cultivated tumorspheres enriched in stem cells, and performed immunofluorescent detection of the stemness marker SOX2 using confocal microscopy. In this article, we present an image processing method that reconstructs the amount and location of the Cancer Stem Cells in the spheroids. Its advantage is the use of a statistical criterion to classify the cells in Stem and Differentiated, instead of setting an arbitrary threshold. Moreover, the analysis of the experimental images presented in this work agrees with the results from our computational models, thus enforcing the notion that the distribution of Cancer Stem Cells in a tumorsphere is non-homogeneous. Additionally, the method presented here provides a useful tool for analyzing any image in which different kinds of cells are stained with different markers. [ABSTRACT FROM AUTHOR]

Tools for implementing a systematic quality management are necessary for the use of material extrusion as an additive manufacturing process for products with high quality requirements. Well-defined quality classes are crucial for ensuring that the requirements for a product can be communicated transparently and that the existing properties can be evaluated. Furthermore, there is a lack of capable measurement equipment for the acquisition of process data during the production process. To address these challenges, the present paper introduces an image processing system that determines quality indicators for individual layers in terms of imperfect surface percentages and the number of imperfections. The central element of the hardware is an adaptive darkfield illumination, which leads to high-contrast images. In addition, five types of layer subareas are identified in a segmentation step. Unsupervised machine learning methods are then used to detect imperfections in each layer subarea. In the segmentation, the current layer can be distinguished from irrelevant image background regions with an F-measure of 0.981. For the layer-wise measurement of the quality indicators, relative measurement errors with standard deviations of 25 to 76.1% are found. After evaluating the capabilities of the image processing system, a proposal for limits of quality classes is derived by monitoring several material extrusion processes. For this purpose, three quality classes for each of the five layer subareas are deduced from the process scatter measured by the image processing system. The results are an important contribution to the industrialization of material extrusion in safety–critical areas such as medical technology or the aerospace industry. [ABSTRACT FROM AUTHOR]

Objective: To investigate the potential applicability of AI-assisted compressed sensing (ACS) in knee MRI to enhance and optimize the scanning process. Methods: Volunteers and patients with sports-related injuries underwent prospective MRI scans with a range of acceleration techniques. The volunteers were subjected to varied ACS acceleration levels to ascertain the most effective level. Patients underwent scans at the determined optimal 3D-ACS acceleration level, and 3D compressed sensing (CS) and 2D parallel acquisition technology (PAT) scans were performed. The resultant 3D-ACS images underwent 3.5 mm/2.0 mm multiplanar reconstruction (MPR). Experienced radiologists evaluated and compared the quality of images obtained by 3D-ACS-MRI and 3D-CS-MRI, 3.5 mm/2.0 mm MPR and 2D-PAT-MRI, diagnosed diseases, and compared the results with the arthroscopic findings. The diagnostic agreement was evaluated using Cohen's kappa correlation coefficient, and both absolute and relative evaluation methods were utilized for objective assessment. Results: The study involved 15 volunteers and 53 patients. An acceleration factor of 10.69 × was identified as optimal. The quality evaluation showed that 3D-ACS provided poorer bone structure visualization, and improved cartilage visualization and less satisfactory axial images with 3.5 mm/2.0 mm MPR than 2D-PAT. In terms of objective evaluation, the relative evaluation yielded satisfactory results across different groups, while the absolute evaluation revealed significant variances in most features. Nevertheless, high levels of diagnostic agreement (κ: 0.81–0.94) and accuracy (0.83–0.98) were observed across all diagnoses. Conclusion: ACS technology presents significant potential as a replacement for traditional CS in 3D-MRI knee scans, allowing thinner MPRs and markedly faster scans without sacrificing diagnostic accuracy. Clinical relevance statement: 3D-ACS-MRI of the knee can be completed in the 160 s with good diagnostic consistency and image quality. 3D-MRI-MPR can replace 2D-MRI and reconstruct images with thinner slices, which helps to optimize the current MRI examination process and shorten scanning time. Key Points: • AI-assisted compressed sensing technology can reduce knee MRI scan time by over 50%. • 3D AI-assisted compressed sensing MRI and related multiplanar reconstruction can replace traditional accelerated MRI and yield thinner 2D multiplanar reconstructions. • Successful application of 3D AI-assisted compressed sensing MRI can help optimize the current knee MRI process. [ABSTRACT FROM AUTHOR]

This study proposes a novel approach for detecting and recognizing asphalt pavement cracks. Image processing techniques including discrete Fourier transform (DFT), steerable filter (SF), and integral projection (IP) are combined to extract features from pavement images. The new method employs DFT as a low-pass filter to enhance the original digital image. Accordingly, a crack salient map is constructed by the utilization of SF. Based on such salient map, IP of the image is established and its statistical properties are computed to characterize the condition of a pavement image. A soft computing model that integrates the artificial neural network, Levenberg–Marquardt (LM) backpropagation training, and Differential Flower Pollination (DFP) algorithm is put forward for the task of pavement recognition. The integration of LM and DFP aims at combining the global search capability of metaheuristic and the local search efficiency of the steepest descent-based backpropagation approach. During the optimization process of DFP, LM algorithm is performed with a randomly selected member of the population to boost the convergence rate and enhance the searching effectiveness. This new training framework of ANN is, therefore, named as ANN with Random Boosted Differential Flower Pollination (RBDFP-ANN). A data set of pavement images including four class labels (no crack, longitudinal crack, transverse crack, and alligator crack) has been collected to train and verify the RBDFP-ANN. Experimental results supported by statistical test confirm that the proposed model achieves a superior prediction accuracy rate (89.25%) over other benchmark methods. Accordingly, the RBDFP-ANN model is very potential to be used by transportation agencies in the task of asphalt pavement inspection. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate novel, automated biomarkers, pigment epithelial detachment composition indices (PEDCI) in eyes with neovascular age-related macular degeneration (nAMD) undergoing anti-vascular endothelial growth factor (anti-VEGF) therapy through 24 months. Methods: Retrospective analysis of 37 eyes (34 patients) with PED associated with nAMD receiving as-needed anti-VEGF treatment was performed. Best-corrected visual acuity (BCVA) and optical coherence tomography images were acquired at a treatment-naïve baseline and 3-, 6-, 12-, 18-, and 24-month visits. Previously validated automated imaging biomarkers, PEDCI-S (serous), PEDCI-N (neovascular), and PEDCI-F (fibrous) within PEDs were measured. ANOVA analysis and Spearman correlation were performed. Results: Mean BCVA (in logMAR) was 0.60 ± 0.47, 0.45 ± 0.41, 0.49 ± 0.49, 0.61 ± 0.54, 0.59 ± 0.56, and 0.67 ± 0.57 at baseline, 3, 6, 12, 18, and 24 months respectively. Overall, BCVA showed minimal worsening of 0.07 ± 0.54 logMAR (p = 0.07). 13.38 ± 3.77 anti–VEGF injections were given through 24 months. PEDCI-F showed an increase of 0.116, 0.122, 0.036, and 0.006 at months 3, 6, 12, and 18 respectively and a decrease of 0.004 at month 24 (p = 0.03); PEDCI-S showed a decrease of 0.064, 0.130, 0.091, 0.092, and 0.095 at months 3, 6, 12, 18, and 24 respectively (p = 0.16); PEDCI-N showed a decrease of 0.052 at month 3 and an increase of 0.008, 0.055, 0.086, and 0.099 at months 6, 12, 18, and 24 respectively (p = 0.06). BCVA was negatively correlated with PEDCI-F (r = -0.28, p < 0.01), and positively correlated with PEDCI-N (r = 0.28, p < 0.01) and PEDCI-S (r = 0.15, p = 0.03). Conclusion: Longitudinal analysis of PEDCI supports their utility as biomarkers that characterize treatment related effects by quantifying the relative composition of PEDs. [ABSTRACT FROM AUTHOR]

The achievements of high driving performance and error minimization of autonomous vehicles (AVs) in harsh weather are the biggest challenges for the society of autonomous research area. AVs are mainly driven by the sensor fusion technology of light detection and ranging (LiDAR), radio detection and ranging (RADAR), and camera sensors. In harsh weather such as rain, storm, law lighting, snowfall, and vapor, the detection performances of all the sensors are obstructed. The camera imaging for object detection systems is highly affected by different types of noise in adverse weather conditions and its performance is very anxious for error-free AV driving. This article proposes the prediction-based adaptive fusion alignment (AFA) algorithm of the robust path and object tracking systems with the deep convolutional neural networking (D-CNN) model for detection accuracy improvement, calculative error reduction, and overall driving error minimization of AVs in harsh weather conditions. RADAR and LiDAR are not deep learning (DL) based yet. The D-CNN model of DL algorithms for camera image processing and the segmentation process of object classification is used for actual object detection and localization. The AV-simulated driving accuracy in harsh weather is significantly increased with the proposed AFA and D-CNN algorithms. [ABSTRACT FROM AUTHOR]

Morphable polishing tools are capable of finishing diamond turned surfaces with roughness in the nanometre range. The material removal rate of morphable tools polishing is challenging to calculate due to the labyrinth and dimple textures. This paper introduces a multi-scale theoretical model for predicting the material removal rate of morphable polishing tools. The model includes the polishing velocity, polishing pressure, material removal by an abrasive, and number of effective abrasives. First, polishing velocity is obtained by tool kinematics analysis. Second, polishing pressure is obtained based on image processing. Theoretical polishing pressure is calculated by polishing pressure equations. Morphable tool texture images are binarized and conducted with Boolean product of the theoretical polishing pressure images to get the morphable tool polishing pressure. The morphable tool polishing pressure and 12-image averaged pressure are conducted with Boolean product of the polishing velocity to obtain the instant and average material removal rate. Then, the polishing pressure and material removal rate model are validated through pressure measurement and polishing tests, respectively. It is found that the polishing spot is ellipse shape with long axis (10.6 mm) and short axis (9.6 mm). The material removal rate for smooth tools along the short axis is almost constant while it steadily increased along the negative long axis. For labyrinth tools, the material removal rate along the short axis and the long axis is a trapezoidal shape and scalene triangular shape. For dimple tools, the material removal rate along the long axis and the short axis are similar to isosceles triangular shape. The model results agree well with the experimental data and quantify how morphable tool texture influences the polishing pressure component and influences the material removal rate, which makes the polishing process more predictive. The image processing–based method can be further applied to obtain polishing pressure and material removal rate of different complicated polishing tools. [ABSTRACT FROM AUTHOR]

This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis. [ABSTRACT FROM AUTHOR]

The image classification task of porcelain fragments is of great significance for the digital preservation of cultural heritage. However, common issues are encountered in the image processing of porcelain fragments, including the low computation speed, decreased accuracy due to the uneven distribution of sample categories, and model instability. This study proposes a novel Capsule Network model, referred to as LBCapsNet, which is suitable for the extraction of features from images of porcelain artifacts fragments. A bottleneck-like channel transformation module denoted by ChannelTrans, which resides between the convolutional layer and the PrimaryCaps layer, was first designed. This module is used to reduce the computational complexity and enhance the processing speed when dealing with intricate porcelain images. The MF-R loss function was then proposed by incorporating focal loss into the original loss function. This allows to address the issue of imbalanced distribution of ceramic shard samples and reduce the classification errors, which leads to faster convergence with smoother trend. Finally, an adaptive dynamic routing mechanism is designed with a dynamic learning rate to enhance the overall stability of the classification process. The experimental results obtained on public datasets, such as MNIST, Fashion- MNIST, CIFAR10, FMD and DTD as well as porcelain fragments dataset, demonstrate that LBCapsNet achieves high classification accuracy with faster and more stable computation compared with existing methods. Furthermore, the ability of LBCapsNet to process special textures can provide technical support for the digital preservation and restoration of cultural heritage. [ABSTRACT FROM AUTHOR]

Drug-coated balloon (DCB) angioplasty is one of the potential approaches to alleviating in-stent restenosis and treating peripheral artery disease. An in-silico model has been developed for sirolimus drug eluted from an inflated balloon in a patient-specific arterial cross-section consisting of fibrous tissue, fibrofatty tissue, dense calcium, necrotic core, and healthy tissue. The convection-diffusion-reaction equation represents the transport of drug, while drug binding, both specific and non-specific, can be modelled as a reaction process. The Brinkman equations describe the interstitial flow in porous tissue. An image processing technique is leveraged for reconstructing the computational domain. The Marker and Cell, and Immersed Boundary Methods are used to solve the set of governing equations. The no-flux interface condition and convection do amplify the tissue content, and the regions of dense calcium and necrotic core limited to or extremely close to the interface pose a clinical threat to DCB therapy. Simulations predict the effects of the positioning and clustering of plaque components in the domain. This study demands extensive intravascular ultrasound-derived virtual histology (VH-IVUS) imaging to understand the plaque morphology and determine the relative positions of different plaque compositions about the lumen-tissue interface, which have a significant impact on arterial pharmacokinetics. [ABSTRACT FROM AUTHOR]

This study aims to enhance the flexibility of robots in the manufacturing process to meet the growing demand for flexible manufacturing systems by constructing a dynamic compensation system. The system enables the end effector of the robot to track the trajectory contour on the horizontal plane, even if there is a deviation between the teaching trajectory and the target trajectory contour. The proposed system employs an innovative mechanism design and algorithm that enables dynamic compensation of the robot's absolute position in the XY-plane, even when the end effector is obstructed. The system uses an XY-stage and two industrial cameras, with each axis being separately controlled. To detect and compensate for errors in the end effector and target trajectory, the two cameras are aligned with the X- and Y-direction linear stage, respectively. The cameras are fixed with respect to the linear stage, and the end effector is placed on the stage work surface. Unlike traditional stereo cameras, the cameras in this system do not require calibration for their positional relationship. To verify the system's effectiveness, this study set a group of teaching and target trajectory contours with a maximum deviation of over than 8 mm. Under the compensation of the system, the maximum error between the end effector and the target trajectory contour was 22.8 pixels (1.34 mm), with an average absolute error of 5.4 pixels (0.32 mm). Compared to the maximum error of 8.45 mm before compensation, the error has been reduced by 84 %. [ABSTRACT FROM AUTHOR]

Approximation approaches have been developed for very large-scale integration architecture to limit power consumption and increase effective throughput. The full adder (FA) and block-based designs are the approximate adder designs widely used in the existing techniques to accelerate the additions by partitioning a long carry propagation chain. However, these techniques require more power and area than the standard FA-based techniques. To overcome this issue, this paper presents an error-reduced carry prediction approximate full adder (ERCPAA) technique to achieve faster additions and performance by adding a constant truncation mechanism with an error reduction strategy. Deep learning and neural network technology had made great progress in recent years for field programmable gate array implementation and real-time object detection. It is essential to maintain high accuracy rates in image processing applications, as any errors introduced during the processing can significantly impact the quality of the output image and impact the hardware implementation. Therefore, we propose an ERCPAA that utilizes a lightweight convolutional neural network (ERCPAA–lightweight CNN) to fit well in a fixed-point CNN accelerator architecture. A lightweight CNN that is both thin (with fewer feature maps per layer) and deep (four layers) with just one fully connected hidden layer can enable faster training while achieving higher accuracy. In addition, in a few positions of the higher-order bits of the incorrect part, a full adder (FA) cell is used to simplify the traditional one-bit FA cell, creating an approximate summation and carry. The proposed method is verified by randomly selecting specific images from the Fruit 360, ImageNet, and the Caltech 256 Image Dataset. The proposed model offers an normalized mean error distance, error rate, and mean relative error distance values of 0.154, 6.55%, and 4.6E-4. The recognition accuracy of the proposed model in the ImageNet database is 96% with an execution time of 1420 µs. [ABSTRACT FROM AUTHOR]

Edge detection in images is a vital application of image processing in fields such as object detection and identification of lesion regions in medical images. This problem is more complex in the domain of color images due to the combination of color layer information and the need to achieve a unified edge boundary across these layers, which increases the complexity of the problem. In this paper, a simple and effective method for edge detection in color images is proposed using a combination of support vector machine (SVM) and the social spider optimization (SSO) algorithm. In the proposed method, the input color image is first converted to a grayscale image, and an initial estimation of the image edges is performed based on it. To this end, the proposed method utilizes an SVM with a Radial Basis Function (RBF) kernel, in which the model's hyperparameters are tuned using the SSO algorithm. After the formation of initial image edges, the resulting edges are compared with pairwise combinations of color layers, and an attempt is made to improve the edge localization using the SSO algorithm. In this step, the optimization algorithm's task is to refine the image edges in a way that maximizes the compatibility with pairwise combinations of color layers. This process leads to the formation of prominent image edges and reduces the adverse effects of noise on the final result. The performance of the proposed method in edge detection of various color images has been evaluated and compared with similar previous strategies. According to the obtained results, the proposed method can successfully identify image edges more accurately, as the edges identified by the proposed method have an average accuracy of 93.11% for the BSDS500 database, which is an increase of at least 0.74% compared to other methods. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD), a neurodegenerative disease that mostly affects the elderly, slowly impairs memory, cognition, and daily tasks. AD has long been one of the most debilitating chronic neurological disorders, affecting mostly people over 65. In this study, we investigated the use of Vision Transformer (ViT) for Magnetic Resonance Image processing in the context of AD diagnosis. ViT was utilized to extract features from MRIs, map them to a feature sequence, perform sequence modeling to maintain interdependencies, and classify features using a time series transformer. The proposed model was evaluated using ADNI T1-weighted MRIs for binary and multiclass classification. Two data collections, Complete 1Yr 1.5T and Complete 3Yr 3T, from the ADNI database were used for training and testing. A random split approach was used, allocating 60% for training and 20% for testing and validation, resulting in sample sizes of (211, 70, 70) and (1378, 458, 458), respectively. The performance of our proposed model was compared to various deep learning models, including CNN with BiL-STM and ViT with Bi-LSTM. The suggested technique diagnoses AD with high accuracy (99.048% for binary and 99.014% for multiclass classification), precision, recall, and F-score. Our proposed method offers researchers an approach to more efficient early clinical diagnosis and interventions. [ABSTRACT FROM AUTHOR]

As photovoltaic (PV) panels are installed outdoors, they are exposed to harsh environments that can degrade their performance. PV cells can be coated with a protective material to protect them from the environment. However, the coated area has relatively small temperature differences, obtaining a sufficient database for training is difficult, and detection in low-resolution thermal images is complicated. This paper proposes a method for detecting the relative temperature difference on PV panels and a method for accumulating detection results within consecutive thermal images. To verify the performance of the proposed method, we installed PV panels coated with three different materials. Subsequently, 60 infrared (IR) thermal and visible images were acquired using an IR thermal imaging camera mounted on the drone. When more than 16 out of 60 results were accumulated, the highest performance was achieved with an F1 score of 0.7178. This case study demonstrated that even low-resolution thermal images can be acquired continuously to detect areas with small temperature differences without applying machine learning, which requires a large database. [ABSTRACT FROM AUTHOR]

Image segmentation is a core research in the image processing and computer vision. In this paper, we suggest a Bi-cubic spline phase transition potential and elaborate a Bi-Cubic spline phase transition potential development. In the image segmentation, we develop the new approach to apply the novel computational fluid dynamics in the boundary with subgrid. The numerical subgrid Bi-cubic method with Bi-Cubic spline for minimizing the piecewise constant energy functional is very efficient, robust and fast in the image segmentation with a multispecies multiphase segmentation models. The subgrid Bi-cubic spline is applied on the boundary with subgrid and the regular grid is applied on the non-boundary. The model generates a multispecies multiphase distribution with Bi-Cubic spline and we can extract the image segments with multispecies multiphase. Finally, we analyze the models and show the numerical results. Numerical results are presented with OCR (Optical Character Recognition) and the medical image. [ABSTRACT FROM AUTHOR]

Introduction: Traction force microscopy (TFM) is a widely used technique to measure cell contractility on compliant substrates that mimic the stiffness of human tissues. For every step in a TFM workflow, users make choices which impact the quantitative results, yet many times the rationales and consequences for making these decisions are unclear. We have found few papers which show the complete experimental and mathematical steps of TFM, thus obfuscating the full effects of these decisions on the final output. Methods: Therefore, we present this "Field Guide" with the goal to explain the mathematical basis of common TFM methods to practitioners in an accessible way. We specifically focus on how errors propagate in TFM workflows given specific experimental design and analytical choices. Results: We cover important assumptions and considerations in TFM substrate manufacturing, substrate mechanical properties, imaging techniques, image processing methods, approaches and parameters used in calculating traction stress, and data-reporting strategies. Conclusions: By presenting a conceptual review and analysis of TFM-focused research articles published over the last two decades, we provide researchers in the field with a better understanding of their options to make more informed choices when creating TFM workflows depending on the type of cell being studied. With this review, we aim to empower experimentalists to quantify cell contractility with confidence. [ABSTRACT FROM AUTHOR]

In a flow channel, generally, microorganisms derived from bacteria contained in water first attach to a surface, form colonies and then become a pollutant known as biofilm. It is important to control the generation and growth of this pollutant, because it has the disadvantage of causing insanitary conditions inside tubes employed in medical and food processing, resulting in various infections. In the present study, we estimate the process of formation of biofilm inside a glass tube by means of binarization and fractal dimension of light scattering patterns obtained under illumination of a white light source and a laser diode. Experiments are conducted for glass tubes filled with bacteria-containing water without flow and with flow to confirm the feasibility of the present method for monitoring biofilm adhering to their inner surfaces. [ABSTRACT FROM AUTHOR]

With multiple merits compared with thermal spray, cold spray technology is widely applied for various application fields. As part of an intelligent cold spray repair system, an approach for three-dimensional (3D) damage region contour extraction is proposed, which is without nominal Computer-Aided Design models and prior knowledge of damaged workpieces. Moreover, due to partially image processing based, the computation load of the proposed method is lower caused by partially avoiding the 3D point cloud processing. According to the results, it is capable to handle the uneven, sparse and unorganized point cloud to precisely extract 3D contour of damage region. The effectiveness of the proposed approach is highlight. Furthermore, the proposed methods on 3D cloud point to grayscale conversion, two-dimensional (2D) edge detection on images, 3D contour reconstruction from 2D edge are also promising for point cloud processing in other application fields. [ABSTRACT FROM AUTHOR]

The convolutional neural network-based deep generative model (DGM) is a powerful tool for handling image datasets that opens up strategies for fast synthesis of optimum designs at unseen boundary conditions. Existing DGMs for design synthesis are typically based on O (10000) training data, which limits the engineering applications. This paper explores the feasibility of improving DGM data efficiency with O (100) training data through prior constraints. A two-stage data-efficient deep generative model (DE-DGM) is proposed which leverages the first-stage design synthesis from probabilistic proper orthogonal decomposition and the second-stage enhancement from encoder-decoder convolutional neural network. Four topology optimization cases have been adopted, including compliance minimization, heat conduction, airplane bearing bracket design, and three-dimensional machine tool column structure design. The proposed DE-DGMs could be trained with 100–200 data and synthesize the main features of the design at unseen boundary conditions. The overall computation cost of warm-start topology optimization leveraging DE-DGM predictions reduces to 36 %–58 % of the standard cases. [ABSTRACT FROM AUTHOR]

Radiological image analysis using machine learning has been extensively applied to enhance biopsy diagnosis accuracy and assist radiologists with precise cures. With improvements in the medical industry and its technology, computer-aided diagnosis (CAD) systems have been essential in detecting early cancer signs in patients that could not be observed physically, exclusive of introducing errors. CAD is a detection system that combines artificially intelligent techniques with image processing applications thru computer vision. Several manual procedures are reported in state of the art for cancer diagnosis. Still, they are costly, time-consuming and diagnose cancer in late stages such as CT scans, radiography, and MRI scan. In this research, numerous state-of-the-art approaches on multi-organs detection using clinical practices are evaluated, such as cancer, neurological, psychiatric, cardiovascular and abdominal imaging. Additionally, numerous sound approaches are clustered together and their results are assessed and compared on benchmark datasets. Standard metrics such as accuracy, sensitivity, specificity and false-positive rate are employed to check the validity of the current models reported in the literature. Finally, existing issues are highlighted and possible directions for future work are also suggested. [ABSTRACT FROM AUTHOR]

Image super-resolution (SR) is a classic problem of image processing. This paper proposes a self-learning interpolation method (SLIM) based on a single image by combining grid feature mapping with binary decision tree, which is not only transparent as the interpolation-based methods, but also achieves comparable performance as the learning-based methods. Firstly, it downsamples the given image I LR to obtain its low–low-resolution image I LLR , which is used to obtain sample data for the self-learning interpolation algorithm for enlarging I LLR to get I LR . Secondly, it provides a structural feature classification method to divide all of the samples into several groups, such that each class of I LLR is mapped to a matrix of coefficients for calculating the values of the pixels of I LR . The image I LR is approximated by executing the decision tree to refine the corresponding mapping matrix. Finally, the resulting high-resolution image I HR is obtained from the given image I LR by using the mapping matrixes. Experimental results show that SLIM achieves more smooth edges and better details on subjective vision than prevailing SR methods, and it is a transparent one but achieves comparable performances on PSNR and SSIM with the learning-based methods, while it outperforms the interpolation-based methods. It means that SLIM is both transparent and efficient and has much better subjective vision than other SR methods. [ABSTRACT FROM AUTHOR]

Non-traumatic thoracic aorta emergencies are acute conditions associated with substantial morbidity and mortality. In the emergency setting, timely detection of aortic injury through radiological imaging is crucial for prompt treatment planning and favorable patient outcomes. 3D cinematic rendering (CR), a novel rendering algorithm for computed tomography (CT) image processing, allows for life-like visualization of spatial details and contours of highly complex anatomic structures such as the thoracic aorta and its vessels, generating a photorealistic view that not just adds to diagnostic confidence, but is especially useful for non-radiologists, including surgeons and emergency medicine physicians. In this pictorial review, we demonstrate the utility of CR in the setting of non-traumatic thoracic aorta emergencies through 10 cases that were processed at a standalone 3D CR station at the time of presentation, including its role in diagnosis and management. [ABSTRACT FROM AUTHOR]

Auto-Encoder based Deep Subspace Clustering (DSC) has been widely applied in computer vision, motion segmentation and image processing. However, existing DSC methods suffer from two limitations: (1) they ignore the rich useful relational information and the connectivity within each subspace due to the reconstruction loss; (2) they design convolutional networks individually according to specific datasets. To address the above problems and improve the performance of DSC, we propose a novel algorithm called Self-Supervised deep Subspace Clustering with Entropy-norm(S 3 CE) in this paper. Firstly, S 3 CE introduces self-supervised contrastive learning to pre-train the encoder instead of requiring a decoder. Besides, the trained encoder is used as a feature extractor to segment subspace by combining self-expression layer and entropy-norm constraint. This not only preserves the local structure of data, but also improves the connectivity between data points. Extensive experimental results demonstrate the superior performance of S 3 CE in comparison to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Deep Neural Networks (DNN) have made significant advances in various fields including speech recognition and image processing. Typically, modern DNNs are both compute and memory intensive, therefore their deployment in low-end devices is a challenging task. A well-known technique to address this problem is Low-Rank Factorization (LRF), where a weight tensor is approximated by one or more lower-rank tensors, reducing both the memory size and the number of executed tensor operations. However, the employment of LRF is a multi-parametric optimization process involving a huge design space where different design points represent different solutions trading-off the number of FLOPs, the memory size, and the prediction accuracy of the DNN models. As a result, extracting an efficient solution is a complex and time-consuming process. In this work, a new methodology is presented that formulates the LRF problem as a (FLOPs vs. memory vs. prediction accuracy) Design Space Exploration (DSE) problem. Then, the DSE space is drastically pruned by removing inefficient solutions. Our experimental results prove that the design space can be efficiently pruned, therefore extract only a limited set of solutions with improved accuracy, memory, and FLOPs compared to the original (non-factorized) model. Our methodology has been developed as a stand-alone, parameterized module integrated into T3F library of TensorFlow 2.X. [ABSTRACT FROM AUTHOR]

Fused filament fabrication (FFF) is a widely used additive manufacturing process for producing functional components and prototypes. The FFF process involves depositing melted material layer-by-layer to build up 3D physical parts. The quality of the final product depends on several factors, including the component density and tensile strength, which are typically determined through destructive testing methods. X-ray microtomography (XCT) can be used to investigate the pore sizes and distribution. These approaches are time-consuming, costly, and wasteful, making it unsuitable for high-volume manufacturing. In this paper, a new method for non-destructive determination of component density and estimation of the tensile strength in FFF processes is proposed. This method involves the use of gradual error detection by sensors and convolutional neural networks. To validate this approach, a series of experiments has been conducted. Component density and tensile strength of the printed specimens with varying extrusion factor were measured using traditional destructive testing methods and XCT. The cumulative error detection method was used to predict the same properties without destroying the specimens. The predicted values were then compared with the measured values, and it was observed that the method accurately predicted the component density and tensile strength of the tested parts. This approach has several advantages over traditional destructive testing methods. The method is faster, cheaper, and more environmentally friendly since it does not require the destruction of the product. Moreover, it facilitates the testing of each individual part instead of assuming the same properties for components from one series. Additionally, it can provide real-time feedback on the quality of the product during the manufacturing process, allowing for adjustments to be made as needed. The advancement of this approach points toward a future trend in non-destructive testing methodologies, potentially revolutionizing quality assurance processes not only for consumer goods but various industries such as electronics or automotive industry. Moreover, its broader applications extend beyond FFF to encompass other additive manufacturing techniques such as selective laser sintering (SLS), or electron beam melting (EBM). A comparison between the old destructive testing methods and this innovative non-destructive approach underscores the possible fundamental change toward more efficient and sustainable manufacturing practices. This approach has the potential to significantly reduce the time and cost associated with traditional destructive testing methods while ensuring the quality of FFF-manufactured products. [ABSTRACT FROM AUTHOR]

Considering the trend of scaling down conventional technologies toward next-generation devices and the importance of new practical and efficient procedures in increasing the efficiency of digital computing circuits, this paper presents three full adder circuits. Suggested circuits, named Design-1 to Design-3, are proposed based on FinFET technology with channel length of 11 nm. In order to optimize energy consumption and area occupation, they have been implemented based on approximate computing with 4, 10, and 12 transistors, respectively. The main characteristics of the presented circuits lie in their utilization of the gate diffusion input technique, which has resulted in the generation of only three errors in their output. Diverse objectives for error generation have been incorporated into these circuits, positioning them as highly accurate competitors when compared to the most state-of-the-art designs. Also, due to the use of low number of transistors, which has led to low internal nodes, these circuits are in a favorable position in terms of power consumption and speed. To evaluate these circuits, extensive simulations are conducted considering various implementations, including single-bit configurations and integration within 4-bit ripple carry adders with 2 and 4 approximate bits. Based on the figure of merits used consist of diverse design parameters, Design-1 with the least number of transistors has a difference of about 85% improvement compared to the nearest competitor, while design-2 and design-3 circuits are also placed in desirable rankings. [ABSTRACT FROM AUTHOR]

Algae are often cultivated in closed system of photobioreactors. In the cultivation, algae cells grow to adhere on the wall surface of a reactor, which results in inhibiting photosynthesis of culture solution. This short paper visualizes the microalgae adhesion on the oil coated wall of a cultivation reactor. A remarkable finding is the fact that the coating of silicone oil could protect the wall surface from the microalgae adhesion. In particular, the coating of silicone oil with the kinematic viscosity larger than 20 [cSt] can be found to reduce the adhesion area on the reactor wall to less than half that on an uncoated reactor. [ABSTRACT FROM AUTHOR]

Modern pavement management systems depend mainly on pavement condition assessment to plan rehabilitation strategies. Manual inspection is performed by trained inspectors to assess pavement damages conventionally. This can be cost-intensive, time-consuming, and a source of risk for inspectors. An image-based inspection using a smartphone is adopted to overcome such problems. This paper proposes an automatic crack detection and mapping program for rigid pavement, which can automate the visual inspection process. The program uses Global Navigation Satellite System (GNSS) data recorded by smartphones and various image processing techniques to detect crack lengths and areas in images. The performance of the program was evaluated by a field study. A crack quantification process was performed to compare the manually measured values and crack lengths obtained from the program. The results show that the program can detect other types of distress, such as pop-outs and punch-outs. This method can achieve satisfactory performance compared to the effort and costs spent. [ABSTRACT FROM AUTHOR]

Medjool date thinning automation is essential for reducing Medjool production labor and improving fruit quality. Thinning automation requires motion planning based on feature extraction from a segmented fruit bunch and its components. Previous research with focused bunch images attained high success in bunch component segmentation but less success in establishing correct association between the two components (a rachis and spikelets) that form one bunch. The current study presents an algorithm for improved component segmentation and association in the presence of occlusions based on integrating deep neural networks, traditional methods building on bunch geometry, and active vision. Following segmentation with Mask-R-CNN, segmented component images are converted to binary images with a Savitzky–Golay filter and an adapted Otsu threshold. Bunch orientation is calculated based on lines found in the binary image with the Hough transform. The orientation is used for associating a rachis with spikelets. If a suitable rachis is not found, bunch orientation is used for selecting a better viewpoint. The method was tested with two databases of bunches in an orchard, one with focused and one with non-focused images. In all images, the spikelets were correctly identified [intersection over union (IoU) 0.5: F1 0.9]. The average orientation errors were 18.15° (SD 12.77°) and 16.44° (SD 11.07°), respectively, for the focused and non-focused databases. For correct rachis selection, precision was very high when incorporating orientation, and when additionally incorporating active vision recall (and therefore F1) was high (IoU 0.5: orientation: precision 0.94, recall 0.44, F1 0.60; addition of active vision: precision 0.96, recall 0.61, F1 0.74). The developed method leads to highly accurate identification of fruit bunches and their spikelets and rachis, making it suitable for integration with a thinning automation system. [ABSTRACT FROM AUTHOR]

Optical tracking sensor technology has been widely used in the field of motion. However, there are still some challenges in the recognition of badminton players, and more accurate methods are needed to capture the dynamic characteristics of badminton players. The aim of this study is to design an optical tracking sensor system based on image feature extraction for badminton player motion recognition. In this paper, a high resolution camera is used to collect the image sequence of badminton match. Then through image processing and computer vision technology, the key image features are extracted from the image sequence. Then, machine learning algorithm is used to classify and recognize the extracted features to achieve accurate recognition of badminton players' movements. The experimental results show that the optical tracking sensor system can effectively extract the movement features of badminton players and identify their movements accurately. Compared with the traditional method, the system in this paper has higher precision and real-time performance, and can meet the needs of practical applications. [ABSTRACT FROM AUTHOR]

The aim of this study is to propose an image processing device based on load balancing algorithm, which can realize efficient and accurate data processing in physical fitness detection. The characteristics of optical sensor image processing equipment and the demand of physical fitness detection are studied and analyzed. Then, according to the principle and strategy of load balancing, a load balancing algorithm suitable for iot optical sensor image processing equipment is designed. The algorithm can automatically adjust the task allocation according to the load of the device and ensure the load balance of each device. Finally, the experiment is carried out in the actual scene, and the comparison with the traditional method is analyzed. The experimental results show that the iot optical sensor image processing equipment based on load balancing algorithm has excellent performance in the physical fitness detection. It can effectively reduce the load pressure of the equipment and improve the speed and accuracy of data processing. The application potential of the equipment is huge, and it can play an important role in sports training, health monitoring and other fields. [ABSTRACT FROM AUTHOR]

This study aims to explore the application of computer simulation method based on light image processing in the monitoring of metabolic regulation data in exercise training. The study collected light image data during exercise training, including several parameters such as skin reflexes, heart rate response and skin temperature. Then, using computer simulation technology, the data were processed and analyzed to obtain metabolic regulation data during exercise. Finally, according to the analysis results, the training effect is evaluated and the training plan is adjusted. The results of the data analysis show that by monitoring the metabolic regulation data, the effect of exercise training can be more accurately evaluated and help adjust the training plan to achieve better training results. This method has the advantages of non-invasive, real-time and accurate, and is expected to be widely used in the field of sports training monitoring and regulation, and further improve the training effect and the formulation of individual training plans. [ABSTRACT FROM AUTHOR]

This research introduces quantum adaptive machine learning (QAML) as an innovative solution to enhance the processing efficiency of machine learning in medical image classification, focusing on the diagnosis of brain tumors. QAML leverages quantum algorithms to address challenges associated with large and high-dimensional medical images, offering advantages such as accelerated processing rates, reduced model complexity with heightened accuracy, enhanced precision in handling intricate data relationships, and increased resistance to noise—critical in medical image analysis. The study details the implementation of QAML through a hybrid quantum–classical neural network, employing parameterized quantum circuits for image processing. Comparative experiments with traditional machine learning models demonstrate QAML's faster convergence and comparable accuracy. The research also explores the impact of adaptive optimization strategies on QAML performance, indicating promising results. Overall, QAML, with its quantum convolutional and pooling layers, emerges as a promising and efficient solution for medical image classification, marking significant progress in the integration of quantum-enhanced machine learning in healthcare applications. [ABSTRACT FROM AUTHOR]

Over the past few decades, the field of sports training has used traditional sensor technology to collect motion data, but sensor technology is limited by its installation location and number, and cannot provide accurate and comprehensive data acquisition. The traditional data acquisition system cannot realize real-time data acquisition and analysis because of the delay in data transmission and processing. Therefore, the goal of this study is to develop a real-time data acquisition system based on optical imaging technology, through the use of optical imaging technology, real-time acquisition of the image of the movement during the movement process, and extract the movement data. By connecting the camera device to the embedded processor, the data can be transmitted and processed quickly. Through the image processing algorithm, the key motion data can be extracted from the images of the movers. This data will be stored and analyzed by an embedded processor. The experimental results show that the optical imaging technology developed in this study has good performance in the acquisition of sports training data. [ABSTRACT FROM AUTHOR]

Wushu has unique movement requirements and techniques, but the traditional methods of optimizing wushu have some problems, such as unstable effect and difficult quantitative evaluation. In recent years, light sensors have been widely used in the field of image processing and detection, but there is little research on the application of martial arts motion optimization and image defect detection. The purpose of this study is to optimize martial arts movements and detect image defects by using light sensors based on reinforcement learning (RL) algorithm. The system collected the data of martial arts movement and image defects. Based on RL algorithm, the optimization model of optical sensor is established. The model is trained to optimize martial arts movements and detect image defects. The experimental results show that the light sensor based on RL can effectively optimize martial arts movements and accurately detect image defects. The proposed method improves the accuracy and fluency of martial arts movements in terms of optimization, and achieves high precision and efficiency in image defect detection, providing a new method and idea for martial arts training and image processing. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing image fusion technology can combine multiple remote sensing images to display images more richly and accurately, and make them present more comprehensive and accurate image features. Because this technology has a high utilization rate of image processing, it has research significance. The objective of this study is to improve the accuracy and reliability of infrared image processing technology in motion recognition. In order to achieve this goal, this paper focuses on the research of high resolution image processing technology to improve the quality and clarity of infrared images. Image enhancement algorithm is used to reduce the noise and blur effect in infrared images, image recovery technology is used to improve the resolution and detail representation ability of infrared images, and finally image segmentation and feature extraction algorithm are used to extract the motion features in infrared images. [ABSTRACT FROM AUTHOR]

The existing basketball teaching system can only capture images through the camera, and can not get the subtle movements of the players in real time. The combination of wearable sensor technology and moving image processing technology has injected new vitality into the field of sports, improved the training efficiency and level of athletes, and realized the comprehensive monitoring and analysis of sports. The aim of this research is to develop a wearable sensor based on infrared optical imaging technology for real-time capture of basketball players' movements and image processing. By embedding infrared sensors into the sports equipment of basketball players, the key movement data of the players can be obtained in real time. The collected infrared image is analyzed and recognized by image processing algorithm, and the athlete's joint position and movement trajectory are extracted. Through experimental verification, the wearable sensor proposed in this paper can capture the movement of athletes with high precision and carry out effective image processing, which has a good application prospect in basketball teaching. The system can obtain the athletes' action data in real time, and analyze and evaluate it accurately through image processing, which provides a strong support for basketball teaching. [ABSTRACT FROM AUTHOR]

Table tennis players need to have tactical awareness, that is, to choose the most appropriate tactical strategy to deal with the opponent's situation and the game situation. But in practice, many athletes tend to neglect the training of tactical awareness, resulting in a disconnect between training and competition. In order to solve this problem, this paper introduces a spatial dynamic image planning method based on wearable optoelectronic devices, and applies it to the tactical simulation of table tennis match. By analyzing the characteristics and demands of table tennis competition, an optical sensing system is designed and integrated into wearable devices. The system uses optical sensors to capture the position and movement trajectory of ping-pong balls in real time, and carries out real-time image processing and analysis based on this information. In the process of tactical simulation, the system can predict the opponent's tactical intention according to the position and trajectory of the ping-pong ball, and give corresponding coping strategies. The experimental results show that the system achieves good results in both accuracy and real-time performance. The application of tactical optimization model in table tennis competition will help to improve the competitive level of athletes and promote the development of table tennis. [ABSTRACT FROM AUTHOR]

Optical processing technology can improve the accuracy and efficiency of image processing. Visual packaging art design simulation is to quickly generate realistic packaging effects in the early stage of packaging design, so as to make better design decisions. Traditional simulation methods often require a lot of time and human resources, and the results lack realism and detail. Machine vision technology based on optical processing can make full use of image processing algorithms and computer vision technology to achieve efficient and accurate packaging design simulation. In this paper, a machine vision technology framework based on image optical processing is proposed. In the image acquisition stage, high-resolution cameras are used to acquire real packaging images, and in the optical processing stage, algorithms are used to enhance, reduce noise and correct images to improve image quality and clarity. In the image analysis stage, machine learning algorithms are used to extract features and detect and identify packaging elements, providing data support for subsequent simulation rendering. The results show that the machine vision technology based on image optical processing can generate high quality and realistic simulation effect of packaging design. Compared with traditional methods, this technique has achieved significant improvements in terms of time cost and design quality. [ABSTRACT FROM AUTHOR]

Oil painting education not only helps to improve students' drawing skills and quality but also fosters creative thinking in the field of art. In order to keep up with the development of art diversity, it is essential to establish the concept of art diversity education and reform university oil painting education to meet the needs of modern society. This study aims to explore the potential value of image optical processing technology based on computer vision in the application of oil painting teaching image simulation. In order to improve students' ability of oil painting simulation, this paper presents an image optical processing technology based on computer vision. The study collected a large number of oil painting images and teaching samples. Then, through image processing algorithm, we extract and analyze the optical features in the oil painting works, and then design an optical simulation method to apply these features to the students' simulation works. By comparing the traditional methods and the techniques proposed in this paper, the image optical processing technology based on computer vision has potential practical value in the application of oil painting teaching image simulation, which can help students better understand and simulate the optical effects of traditional oil paintings. [ABSTRACT FROM AUTHOR]

Image processing is an important area to bring out the best in an image for human interpretation. This process has been widely used in various fields such as machine vision, remote sensing image analysis, medical diagnosis, image restoration, image pattern recognition, and video processing. During the acquisition and transmission of digital images, several random signals, known as noise, can affect image quality. To reduce or to remove efficiently the noise in the acquired or transmitted images, various image denoising techniques can be applied. The performance of denoising methods increases progressively when the noise parameters are taken into account as input parameters. Traditional denoising approaches adopt some assumptions to model noise, such noise is known as purely additive or multiplicative, pixel-independent, and channel-invariant. Usually, these assumptions limit the denoising effect due to inaccurate estimation of noise parameters in these algorithm models. However, the real noise model is signal-dependent and even device-dependent. In this paper, a new denoising method called signal-dependant noise-reducing anisotropic diffusion is developed, which is a version of the speckle reducing anisotropic diffusion (SRAD) filter. It differs from the standard SRAD filter approach by the insertion of a suitable noise parameters estimation framework. The new filter is designed to handle a variety of images corrupted by several common types of signal-dependent noises that are produced by charge-coupled device sensors. As well as it offers great potential for denoising with preserving textures and fine details. Extensive experiments demonstrate a significant increase in the image denoising performance in terms of SNR and RMSE. Qualitative (visual) results underline the efficacy of the proposed algorithm for filtering mixed signal-dependent noise. [ABSTRACT FROM AUTHOR]

The main objective of this study is to develop a method for the automated detection and classification of weeds and sugar beets. Precision agriculture is an essential area of research that aims to optimize farming practices and reduce the use of harmful chemicals. For this purpose, the Faster RCNN and Federating Learning (FL)-based ensemble models were utilized to classify a specific dataset. In the first stage of the study, feature extraction is performed from the images in the dataset and classified by machine learning algorithms. Then, classification is carried out with the help of FL based deep learning ensemble models. Within the scope of the study, grid search is used for hyperparameter optimization and the results are obtained by a tenfold cross-validation method. Among all tested algorithms, the FL-based ensemble model constructed using the ResNet50 model exhibited the highest accuracy rate of 99%. This system has the potential to significantly reduce the use of herbicides and other chemicals in agricultural practices, promoting a more sustainable form of agriculture. [ABSTRACT FROM AUTHOR]