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SUMMARY: To address complex subsurface structures, a high-resolution velocity model must be constructed. Conventionally, algorithms such as full waveform inversion (FWI) have been used to derive accurate high-resolution velocity structures, but obstacles such as high computational costs remain. Therefore, we propose a high-resolution U-NET (HR U-NET) machine learning model to derive a high-resolution velocity model from a low-resolution velocity model. The low-resolution velocity model and migration data obtained through the corresponding velocity information were used as input data for training. In addition, we tried to improve the accuracy of the high-resolution velocity model by using prior information containing accurate velocity values. A prior model generated through geophysical logging data and a weight model including the reliability information of the prior model were also utilized. Therefore, the HR U-NET model was trained using the low-resolution velocity model, the migration data, the prior model and the weight model. Numerical experiments conducted using synthetic and field data demonstrated that the proposed model could accurately construct a high-resolution velocity model and verified that the prior model and weight model play important roles in the training process. Additionally, we confirmed that the proposed method derived almost similar results using only 8.2 percent of the computational cost of the conventional inversion method. In other words, there is an advantage that it is possible to predict high-resolution velocity information more efficiently in terms of computational cost. [ABSTRACT FROM AUTHOR]

The quality of cranial ultrasound has improved over time, with advancing technology leading to higher resolution, faster image processing, digital display, and back-up. However, some brain lesions may remain difficult to characterize: since higher frequencies result in greater spatial resolution, the use of additional transducers may overcome some of these limitations. The very high-frequency transducers (18-5 MHz) are currently employed for small parts and lung ultrasound. Here we report the first case series comparing the very high-frequency probes (18-5 MHz) with standard micro-convex probes (8-5 MHz) for cranial ultrasound in preterm infants. In this case series, we compared cranial ultrasound images obtained with a micro-convex transducer (8-5 MHz) and those obtained with a very high-frequency (18-5 MHz) linear array transducer in 13 preterm infants ≤ 32 weeks gestation (9 with cerebral abnormalities and 4 with normal findings). Ultrasound examinations using the very high-frequency linear transducer and the standard medium-frequency micro-convex transducer were performed simultaneously. We also compared ultrasound findings with brain MRI images obtained at term corrected age. Ultrasound images obtained with the very high-frequency (18-5 MHz) transducer showed high quality and accuracy. Notably, despite their higher frequency and expected limited penetration capacity, brain size is small enough in preterm infants, so that brain structures are close to the transducer, allowing for complete evaluation. Conclusion: We propose the routine use of very high-frequency linear probes as a complementary scanning modality for cranial ultrasound in preterm infants ≤ 32 weeks gestation. What is Known: • Brain lesions in preterm infants may remain insufficiently defined through conventional cranial ultrasound scan. • Higher frequency probes offer better spatial resolution but have a narrower filed of exploration and limited penetration capacity. What is New: • Very high-frequency probes were compared with standard medium-frequency probes for cranial ultrasound in infants ≤ 32 weeks' gestation. • Thanks to the smaller skull size of preterm infants, the new very high-frequency transducers allowed a complete and accurate evaluation. [ABSTRACT FROM AUTHOR]

We present a novel deep learning approach to a unique image processing application: high-speed (>1000 fps) video footage of lightning. High-speed cameras enable us to observe lightning with microsecond resolution, characterizing key processes previously analyzed manually. We evaluate different semantic segmentation networks (DeepLab3+, SegNet, FCN8s, U-Net, and AlexNet) and provide a detailed explanation of the image processing methods for this unique imagery. Our system architecture includes an input image processing stage, a segmentation network stage, and a sequence classification stage. The ground-truth data consists of high-speed videos of lightning filmed in South Africa, totaling 48,381 labeled frames. DeepLab3+ performed the best (93–95% accuracy), followed by SegNet (92–95% accuracy) and FCN8s (89–90% accuracy). AlexNet and U-Net achieved below 80% accuracy. Full sequence classification was 48.1% and stroke classification was 74.1%, due to the linear dependence on the segmentation. We recommend utilizing exposure metadata to improve noise misclassifications and extending CNNs to use tapped gates with temporal memory. This work introduces a novel deep learning application to lightning imagery and is one of the first studies on high-speed video footage using deep learning. [ABSTRACT FROM AUTHOR]

The superpixel usually serves as a region-level feature in various image processing tasks, and is known for segmentation accuracy, spatial compactness and running efficiency. However, since these properties are intrinsically incompatible, there is still a compromise within the overall performance of existing superpixel algorithms. In this work, the property constraint in superpixels is relaxed by in-depth understanding of the image content, and a novel two-stage superpixel generation framework is proposed to produce content-aware superpixels. In the global processing stage, a diffusion-based online average clustering framework is introduced to efficiently aggregate image pixels into multiple superpixel candidates according to color and spatial information. During this process, a centroid relocation strategy is established to dynamically guide the region updating. According to the area feature in manifold space, several superpixel centroids are then split or merged to optimize the regional representation of image content. Subsequently, local updating is adopted on pixels in those superpixel regions to further improve the performance. As a result, the dynamic centroid relocating strategy offers online averaging clustering the property of content awareness through coarse-to-fine label updating. Extensive experiments verify that the produced superpixels achieve desirable and comprehensive performance on boundary adherence, visual satisfactory and time consumption. The quantitative results are on par with existing state-of-the-art algorithms in terms with several common property metrics. [ABSTRACT FROM AUTHOR]

This paper demonstrates how the processing of Brillouin gain spectra (BGS) by two-dimensional correlation methods improves the accuracy of Brillouin frequency shift (BFS) extraction in distributed fiber optic sensor systems based on the BOTDA/BOTDR (Brillouin optical time domain analysis/reflectometry) principles. First, the spectra corresponding to different spatial coordinates of the fiber sensor are resampled. Subsequently, the resampled spectra are aligned by the position of the maximum by shifting in frequency relative to each other. The spectra aligned by the position of the maximum are then averaged, which effectively increases the signal-to-noise ratio (SNR). Finally, the Lorentzian curve fitting (LCF) method is applied to the spectrum with improved characteristics, including a reduced scanning step and an increased SNR. Simulations and experiments have demonstrated that the method is particularly efficacious when the signal-to-noise ratio does not exceed 8 dB and the frequency scanning step is coarser than 4 MHz. This is particularly relevant when designing high-speed sensors, as well as when using non-standard laser sources, such as a self-scanning frequency laser, for distributed fiber-optic sensing. [ABSTRACT FROM AUTHOR]

This paper describes the design, implementation, and usage of a Python package called Hyperspectral Python (HypPy). Proprietary software for processing hyperspectral images is expensive, and tools developed using these packages cannot be freely distributed. The idea of HypPy is to be able to process hyperspectral images using free and open-source software. HypPy was developed using Python and relies on the array-processing capabilities of packages like NumPy and SciPy. HypPy was designed with practical imaging spectrometry in mind and has implemented a number of novel ideas. To name a few of these ideas, HypPy has BandMath and SpectralMath tools for processing images and spectra using Python statements, can process spectral libraries as if they were images, and can address bands by wavelength rather than band number. We expect HypPy to be beneficial for research, education, and projects using hyperspectral data because it is flexible and versatile. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low detection accuracy, slow detection speed and poor stability of traditional coal gangue detection based on image processing, a new YOLOv5s-EB (You Only Look Once Version-5s) network model is constructed for coal gangue target detection and location. Replace the PANet (Path Aggregation Network) layer of the original network with the BiFPN (Bidirectional Feature Pyramid Network) feature fusion network to enhance the global information extraction strength. And combined with the attention mechanism module of ECANet (Efficient Channel Attention Networks). 490 sets of multispectral data were obtained through the multispectral data acquisition system, and 980 original samples of coal and gangue were obtained, the original samples are preprocessed by histogram equalization to establish the coal gangue data set, they are trained in the original YOLOv5s, YOLOv5s-ECA, YOLOv5s-BiFPN, YOLOv5s-EB, YOLOv4 and YOLOv7 neural networks respectively. According to the results, the accuracy rate P of YOLOv5s-EB neural network model reaches 98.5%, and the recall rate R reaches 98.4%, the mAP reached 87.55%. Moreover, YOLOv5s-EB network model is relatively small, only 12.5MB. At the same time, the YOLOv5s-EB model obtains the coordinate information of the upper left and lower right corners of the target detection frame during the identification and detection process, and locates the coal gangue target for the convenience of subsequent sorting. [ABSTRACT FROM AUTHOR]

The Rashid-1 rover, being the main component of the Emirates Lunar Mission (ELM) developed by the Mohammed Bin Rashid Space Center (MBRSC), was launched in late 2022 on-board the Hakuto lunar lander. The scientific objectives of the rover were to investigate the regolith formations of our natural satellite and analyze the geology of its landing area. To perform its investigations, Rashid-1 carried two wide-angle cameras (CAM-1 and CAM-2) and a narrow-angle microscope camera (CAM-M) equipped with Bayer color filter arrays. The mission was however unsuccessful due to a lander failure during lunar descent which caused the loss of the payload. Despite this drawback, significant insights into exploration rover imaging have been gained through the extensive work done prior to launch to achieve the highest image quality from the on-board cameras, and the resulting CASPIP software package will be used for upcoming rover missions. This paper presents the CAM-1 and CAM-2 instruments and the planned advanced image products. In the first part, we provide a detailed presentation of the calibration of the instruments including their optical parameters, characterization of the detector in term of flat-field, optical ghosts, dark current, spectral sensitivities and distortion. The second part of the paper deals with colorimetry, focusing on the colorimetric characterization of the instrument as well as a description of the correction applied to obtain images expressed in the standard color space CIE XYZ and displayed properly in sRGB. Finally, the third part describes advanced image products developed to assist experts (geologists, navigation engineers). It includes a tool for distance visualization directly inside an image, and the creation of 3D images through stereoscopic reconstruction. [ABSTRACT FROM AUTHOR]

The thickness of the bonding interface changes after the structure has been subjected to tensile load, thereby affecting the strength of the bonding structure. It is necessary to monitor the evolution of the bonding interface thickness under tensile load. Through terahertz time-domain spectroscopy imaging and scale-invariant feature transform image processing, the thickness variation of three interfaces of 150 silicon-based adhesive structures under tensile load were analyzed. It was determined that variation at the edge was larger than that in the middle area. The research results also provide data support for the bonding structure health monitoring systems. [ABSTRACT FROM AUTHOR]

Poisson image deblurring, which aims to restore the latent image from its blurred and noisy observation, has drawn significant attention in image processing. Due to its ill-posed nature, enhancing image quality often involves incorporating a well-defined prior to effectively regularize the ill-posed inverse problem. Building upon the framelet system, we propose a frame-based nonconvex regularization method for Poisson image deblurring. The method is formulated by combining a data-fitting term with the difference of two norms, namely ℓ 1 and ℓ 2 , on the latent image. We solve the optimization problem by combining the difference of convex functions algorithm (DCA) with the alternating direction method of multipliers (ADMM), establishing its convergence. We further employ a simulated annealing procedure and show that proposed algorithm almost certainly converges to a global minimum. Two different approaches are employed to handle the frame-based ℓ 1 norm within the ADMM framework. In particular, the frame-based nonconvex regularization method is also considered for the blind Poisson problem. An effective recovery model and its algorithm are presented. Experimental results demonstrate the effectiveness of our proposed models compared with other models in terms of quantitative metrics and visual quality. • Frame-based nonconvex regularization model is proposed to eliminate Poisson noise and blur from degraded image. • An efficient algorithm with convergence guarantees is proposed. • We study an accelerated algorithm and explore the blind Poisson problem. • Experimental results demonstrate the effectiveness of our proposed models compared with other models. [ABSTRACT FROM AUTHOR]

The quality of images captured digitally or transmitted over networks is distorted by noise during the process. The current methods of image restoration can be ineffective in dealing with intricate noise patterns or may be slow or imprecise. This paper fills this gap by presenting a new second-order continuous-time dynamical system for denoising of images in image restoration. The approach used in this work poses the problem as a convex quadratic program that can, thus, be solved for optimality. The existence and uniqueness of a global solution are theoretically demonstrated, and the condition for the global strong convergence of the system's trajectory is provided. The method presented in this paper is shown to be useful in a number of experiments on image restoration. As for the performance, it is higher than that of other known algorithms, with an average SNR equal to 34.78 and a Structural Similarity Index Measure (SSIM) of 0.959 for the reconstructed images. Such improvements demonstrate the effectiveness of the second-order dynamical system approach in actual image restoration applications. [ABSTRACT FROM AUTHOR]

Recently, the fixed-point theorem for fuzzy contractive mappings has been investigated within the framework of intuitionistic fuzzy metric-like spaces. This interesting topic was explored through the utilization of G-Cauchy sequences as defined by Grabiec. The aim of this study is to enhance the aforementioned results in a few aspects. Initially, the proof of the fixed-point theorem is simplified and condensed, allowing for potential generalization to papers focusing on similar fixed-point analyses. Furthermore, instead of G-Cauchy sequences, the classical Cauchy sequences proposed by George and Veeramani are examined, incorporating an additional condition on the fuzzy metric. Within this context, a solution to an old unresolved question posed by Gregory and Sapena is provided. The findings are reinforced by relevant examples. Finally, the introduced fuzzy metrics are applied to the field of image processing. [ABSTRACT FROM AUTHOR]

Cauliflower is a popular winter crop in Bangladesh. However, cauliflower plants are vulnerable to several diseases that can reduce the cauliflowers’ productivity and degrade their quality. The manual monitoring of these diseases takes a lot of effort and time. Therefore, automatic classification of the diseased cauliflower through computer vision techniques is essential. This study has retrieved ten different statistical and gray-level co-occurrence matrix (GLCM)-based features from the cauliflower image dataset by implementing a variety of image processing techniques. Afterwards, the SelectKBest method with the analysis of variance f-value (ANOVA F-value) has been used to identify the most important attributes for classification of the diseased cauliflower. Based on the ANOVA F-value, the top N (5≤N ≤9) most dominant attributes is used to train and test five machine learning (ML) models for classification of diseased cauliflower. Finally, different performance metrics have been used for evaluating the effectiveness of the employed ML models. The bagging classifier achieved the highest accuracy of 82.35%. Moreover, this model has outperformed other ML classifiers in terms of other performance metrics also. [ABSTRACT FROM AUTHOR]

AbstractThis study presents a new method for simultaneous determination of fenoterol, procaterol, clenbuterol, terbutaline and isoprenaline drugs using thin-layer chromatography assisted by image analysis (HPTLC-IA) combined with sensitive detection modes. Separation of the selected drugs was obtained using HPTLC Silica gel 60 F254 plates and mixture of isopropyl alcohol, ethyl acetate and ammonia (20:25:1.5 v/v/v) as mobile phase. UV detection mode combined with the use of 2,2-diphenyl-1-picrylhydrazyl (DPPH•) and 2,2'-azino-bis (3-ethylbenzothiazoline)-6-sulfonic acid (ABTS+•) radicals were applied for the first time for sensible detection of the separated drugs. Grey, green, red and blue scales respectively were selected for image analysis and accurate quantification of spot area. According to the obtained result, the use of DPPH• and ABTS+• reagents revealed a higher sensitivity in detection and accurate quantification of the analyzed drugs. Lower limit of detection (LOD = 0.07 ± 0.03 µg/spot; 0.09 ± 0.03 µg/spot and 0.10 ± 0.04 µg/spot) was obtained for fenoterol, procaterol and terbutaline using the ABTS+• radical and red scale selection for image processing. With the exception of isoprenaline, the proposed HPTLC-IA method showed a good separation of fenoterol, procaterol, clenbuterol and terbutaline drugs from other compounds in urine samples. Good recovery was obtained for these drugs in spiked urine samples. [ABSTRACT FROM AUTHOR]

The implementation of tumor grading tasks with image processing and machine learning techniques has progressed immensely over the past several years. Multispectral imaging enabled us to capture the sample as a set of image bands corresponding to different wavelengths in the visible and infrared spectrums. The higher dimensional image data can be well exploited to deliver a range of discriminative features to support the tumor grading application. This paper compares the classification accuracy of RGB and multispectral images, using a case study on colorectal tumor grading with the QU-Al Ahli Dataset (dataset I). Rotation-invariant local phase quantization (LPQ) features with an SVM classifier resulted in 80% accuracy for the RGB images compared to 86% accuracy with the multispectral images in dataset I. However, the higher dimensionality elevates the processing time. We propose a band-selection strategy using mutual information between image bands. This process eliminates redundant bands and increases classification accuracy. The results show that our band-selection method provides better results than normal RGB and multispectral methods. The band-selection algorithm was also tested on another colorectal tumor dataset, the Texas University Dataset (dataset II), to further validate the results. The proposed method demonstrates an accuracy of more than 94% with 10 bands, compared to using the whole set of 16 multispectral bands. Our research emphasizes the advantages of multispectral imaging over the RGB imaging approach and proposes a band-selection method to address the higher computational demands of multispectral imaging. [ABSTRACT FROM AUTHOR]

This article presents a novel approach to achieve precise trajectory tracking control for a line-following quadcopter by employing a multilayer type-2 fuzzy Petri nets controller (MT2PNC). The MT2PNC dynamically adapts its parameters based on tracking errors, allowing for real-time adjustments to the quadcopter's tilt angles and flight direction. The effectiveness of the controller is thoroughly evaluated through both simulations and experimental studies. In the experimental study, a camera is integrated into the quadcopter to capture line images, which are then processed using sophisticated image processing algorithms to extract essential line information. This extracted data is subsequently fed into the MT2PNC, enabling the quadcopter to precisely follow the reference line. The simulation and experimental results conclusively demonstrate the superior control efficacy of the MT2PNC, showcasing its remarkable ability to accurately track the quadcopter's trajectory. The proposed control method exhibits great promise for line-following and trajectory-tracking applications, and its practical implementation holds substantial potential. [ABSTRACT FROM AUTHOR]

Recently, the success of Transformer in natural language processing and image processing inspires researchers to apply Transformer in point cloud processing. However, existing point cloud Transformer methods have problems with massive parameters, heavy computation, and lack of local features due to the use of global self-attention. To solve these problems, this paper presents a novel point-voxel dual stream Transformer (PVDST) network, which combines the voxel-based convolution and point-based local attention, extracting the local and contextual features of point clouds simultaneously. To reduce the parameters and computation of self-attention and make the contextual features contain more position information, we design the local-aware attention module with explicit position encoding and neighbor embedding, which conducts the attention calculation locally. Based on our local-aware attention module and the cross-attention mechanism, we design a unique way to adaptively fuse the local and contextual features. Extensive experiments on shape classification, object part segmentation, and semantic segmentation tasks demonstrate that PVDST achieves competitive performance compared with other methods. [ABSTRACT FROM AUTHOR]

Observation datasets acquired by the Hyper Suprime-Cam (HSC) on the Subaru Telescope for NASA's New Horizons mission target search were analyzed through a method devised by JAXA. The method makes use of Field Programmable Gate arrays and was originally used to detect fast-moving objects such as space debris or near-Earth asteroids. Here we present an application of the method to detect slow-moving Kuiper Belt Objects (KBOs) in the New Horizons target search observations. A cadence that takes continuous images of one HSC field of view for half a night fits the method well. The observations for the New Horizons Kuiper Belt Extended Mission (NH/KEM) using HSC began in 2020 May, and are ongoing. Here we show our result of the analysis of the dataset acquired from 2020 May through 2021 June that have already passed the proprietary period and are open to the public. We detected 84 KBO candidates in the 2020 June and 2021 June datasets, when the observation field was close to opposition. [ABSTRACT FROM AUTHOR]

Accurately estimating the absolute distance and height of objects in open areas is quite challenging, especially when based solely on single images. In this paper, we tackle these issues and propose a new method that blends traditional computer vision techniques with advanced neural network-based solutions. Our approach combines object detection and segmentation, monocular depth estimation, and homography-based mapping to provide precise and efficient measurements of absolute height and distance. This solution is implemented on an edge device, allowing for real-time data processing using both visual and thermal data sources. Experimental tests on a height estimation dataset we created show an accuracy of 98.86%, confirming the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Architectural parameters of skeletal muscle such as pennation angle provide valuable information on muscle function, since they can be related to the muscle force generating capacity, fiber packing, and contraction velocity. In this paper, we introduce a 3D ultrasound-based workflow for determining 3D fascicle orientations of skeletal muscles. We used a custom-designed automated motor driven 3D ultrasound scanning system for obtaining 3D ultrasound images. From these, we applied a custom-developed multiscale-vessel enhancement filter-based fascicle detection algorithm and determined muscle volume and pennation angle. We conducted trials on a phantom and on the human tibialis anterior (TA) muscle of 10 healthy subjects in plantarflexion (157 ± 7 ∘ ), neutral position (109 ± 7 ∘ , corresponding to neutral standing), and one resting position in between (145 ± 6 ∘ ). The results of the phantom trials showed a high accuracy with a mean absolute error of 0.92 ± 0.59 ∘ . TA pennation angles were significantly different between all positions for the deep muscle compartment; for the superficial compartment, angles are significantly increased for neutral position compared to plantarflexion and resting position. Pennation angles were also significantly different between superficial and deep compartment. The results of constant muscle volumes across the 3 ankle joint angles indicate the suitability of the method for capturing 3D muscle geometry. Absolute pennation angles in our study were slightly lower than recent literature. Decreased pennation angles during plantarflexion are consistent with previous studies. The presented method demonstrates the possibility of determining 3D fascicle orientations of the TA muscle in vivo. [ABSTRACT FROM AUTHOR]

Objectives: To prospectively investigate whether fully automated artificial intelligence (FAAI)-based coronary CT angiography (CCTA) image processing is non-inferior to semi-automated mode in efficiency, diagnostic ability, and risk stratification of coronary artery disease (CAD). Materials and methods: Adults with indications for CCTA were prospectively and consecutively enrolled at two hospitals and randomly assigned to either FAAI-based or semi-automated image processing using equipment workstations. Outcome measures were workflow efficiency, diagnostic accuracy for obstructive CAD (≥ 50% stenosis), and cardiovascular events at 2-year follow-up. The endpoints included major adverse cardiovascular events, hospitalization for unstable angina, and recurrence of cardiac symptoms. The non-inferiority margin was 3 percentage difference in diagnostic accuracy and C-index. Results: In total, 1801 subjects (62.7 ± 11.1 years) were included, of whom 893 and 908 were assigned to the FAAI-based and semi-automated modes, respectively. Image processing times were 121.0 ± 18.6 and 433.5 ± 68.4 s, respectively (p <0.001). Scan-to-report release times were 6.4 ± 2.7 and 10.5 ± 3.8 h, respectively (p < 0.001). Of all subjects, 152 and 159 in the FAAI-based and semi-automated modes, respectively, subsequently underwent invasive coronary angiography. The diagnostic accuracies for obstructive CAD were 94.7% (89.9–97.7%) and 94.3% (89.5–97.4%), respectively (difference 0.4%). Of all subjects, 779 and 784 in the FAAI-based and semi-automated modes were followed for 589 ± 182 days, respectively, and the C-statistic for cardiovascular events were 0.75 (0.67 to 0.83) and 0.74 (0.66 to 0.82) (difference 1%). Conclusions: FAAI-based CCTA image processing significantly improves workflow efficiency than semi-automated mode, and is non-inferior in diagnosing obstructive CAD and risk stratification for cardiovascular events. Clinical relevance statement: Conventional coronary CT angiography image processing is semi-automated. This observation shows that fully automated artificial intelligence-based image processing greatly improves efficiency, and maintains high diagnostic accuracy and the effectiveness in stratifying patients for cardiovascular events. Key Points: • Coronary CT angiography (CCTA) relies heavily on high-quality and fast image processing. • Full-automation CCTA image processing is clinically non-inferior to the semi-automated mode. • Full automation can facilitate the application of CCTA in early detection of coronary artery disease. [ABSTRACT FROM AUTHOR]

Countermeasures for chaff jamming have drawn great attention in the field of radar target detection and tracking. Current approaches for chaff jamming recognition and suppression exhibit limitations in practical effect, generalisation ability, and hybrid jamming handling. To address the above problems, the authors first transform the traditional 1D signal processing problem into a 2D semantic segmentation task and then solve it from the perspective of the dataset construction and algorithm design. For the dataset construction, the authors use both measured and simulated data to synthesise a more realistic labelled dataset (semi‐realistic dataset), which is also with good diversity due to its adjustable chaff interference background. For the algorithm design, the authors propose a Pre‐Decluttering Dual‐Stage UNet (D2UNet) to recognise and suppress chaff jamming in two stages successively, where the former provides prior attention masks for the latter. To further improve the performance of D2UNet, the authors also design a multi‐stage loss function to achieve progressive training. Extensive experimental results demonstrate that D2UNet delivers remarkable recognition accuracy (99.305%) and suppression performance (41.326 dB peak signal‐to‐jamming ratio, 0.9952 structure similarity index measure) on the semi‐realistic dataset. Its practical effect is further verified on measured data. [ABSTRACT FROM AUTHOR]

Different varieties of the same fruit tree can be classified as a fine granularity. Most them, only a single organ of fruit cannot fully extract the overall feature of the plant. It is also difficult to further improve the accuracy of recognition. Multiorgan features can be expected to recognize the fruit tree in current research. However, the inter-organ features can be interfered with each other in the multi-organ feature recognition. It is still challenging to accurately locate and distinguish different organ features. In this study, an organ segmentation (RSA-YOLOv8s) was proposed for the fruit and leaf using YOLOv8s target detection. The three parts of the model were improved, namely Backbone, Neck, and Head. A cross-stage local residual (Residual CSPLayer 2Conv, RC2) module was also designed in the Backbone part. The sensory field of each network layer was used to construct the hierarchical class residual links in each network layer. The dense and small target features were fully extracted after that. The Scale Spatial Pyramid Pooling (SSPP) module was designed in the Neck part. The spatial and scale information of the network were integrated to introduce 3D convolution. The higher-order and multi-scale features were extracted to detect the multi-scale targets. Furthermore, the Asymmetric decoupling detection head (ADDH) module was designed in the Head part. The regression of the model was realized to classify the features using an asymmetric structure. As such, the classification was focused on the center content, while the regression was the edge information. A total of 950 images of 17 fruit tree varieties were selected from the PlantCLEF2022 public dataset. The experimental results show that the precision rate, recall rate, F1 value and average precision of the RSA-YOLOv8s model were 83.2%, 87.9%, 85.5%, and 90.2%, respectively, in the recognition of fruit and leaf organ, which were 5.6, 6.6, 6.1 and 6.7 percentage points higher than those of the original, respectively; By contrast, the precision rate, recall rate, F1 value and mean precision were 88.2%, 91.7%, 89.9% and 94.3%, respectively, in the recognition of single organ, which were 6.4, 6.6, 6.5 and 7.2 percentage points higher than those of the original, respectively; The precision rate, recall rate, F1 value and the mean precision were 78.2%, 84.1%, 81.0% and 86.1%, respectively, in the recognition of leaf single organ, which were 4.8, 6.6, 5.6 and 6.2 percentage points higher than those of the original, respectively. In addition, the improved model was also applied into the 20 date varieties with a total of 3881 images. A recognition accuracy of 99.1% was achieved, which was 6.6% higher than that of the original model. The finding can provide a strong reference for the organ segmentation and variety recognition using multi-organ features, particularly for common fruit trees. [ABSTRACT FROM AUTHOR]

Automatic picking tea buds is ever increasing in the continuous expansion of famous brand tea market at present. However, manual picking cannot fully meet the needs of short-term concentrated harvesting in the large-scale tea production. Mechanical mass picking can also share the very short window period of famous brand tea. Therefore, it is the urgent demand to realize the intelligent and accurate picking of famous tea. Among them, visual identification has been confined to the small target of tea buds and the complex background under the natural environment in the field. Fortunately, Yolov5s-segment network model is suitable to identify tea buds, due to the easy deployment, simple integrated structure and real-time detection. In this study, a novel model was proposed to extract the contour and then locate the picking point of tea bud using improved Yolov5s-segment. The rapid recognition and accurate location of picking points were realized for the tea buds in large field scenes. Firstly, P2 micro-target detection layer was imported into the Neck and Head of the original network. The small targets detection of P3, P4 and P5 layers was achieved in the improved Yolov5s-segment network. Secondly, the contour features of tea bud were extracted in the natural lighting environment. CBAM (convolutional block attention module) was added into the end of Backbone of Yolov5s-segment network for the anti-interference of the improved model. Finally, the position coordinates of the bud stem were extracted, according to the bud contour. The picking points were located accurately for the single bud, one bud with one leaf, and one bud with two leaves. Furthermore, the micro target detection layer and CBAM attention mechanism were gradually added into the original Yolov5s-segment model. In ablation test, the CBAM attention mechanism was replaced with SE, ECA and SimAM. After that, the optimal model was compared with Yolov8s-segment and Yolact. Finally, the positioning test was carried out using the optimal model. The results showed that the best performance was achieved in the model with the micro-target detection layer and CBAM attention mechanism. Compared with the original Yolov5s-segment model, the accuracy, recall rate, F1 score, mean average precision mAP50 and mAP50-95 were improved by 7.0, 8.9, 8.1, 8.3 and 7.3 percentage points, respectively. In the comparison test, the mAP50 of the improved model increased by 4.2 and 9.5 percentage points, respectively, compared with the Yolov8s-segment and Yolact. The positioning test showed that the pixel coordinates of picking points were accurately obtained in the 0° and 45° shooting angles of camera under different states, indicating the high positioning accuracy. The improved model can be used to accurately extract the outline of single bud, one bud with one leaf and one bud with two leaves in large field scenes. The precise location of picking points can be realized at the same time. The findings can provide a theoretical basis for intelligent and rapid picking of famous tea. [ABSTRACT FROM AUTHOR]

Edge-aware image decomposition is an essential topic in the field of multimedia signal processing. In this paper, we propose a novel non-convex penalty function, which we name the generalized Welsch function. We show that the proposed penalty function is more than a generalization of most existing penalty functions for edge-aware regularization, thus, it better facilitates edge-awareness. We embed the proposed penalty function into a novel optimization model for edge-aware image decomposition. To solve the optimization model with non-convex penalty function, we propose an efficient algorithm based on the additive quadratic minimization and Fourier domain optimization. We have experimented with the proposed method in a variety of tasks, including image smoothing, detail enhancement, HDR tone mapping, and JPEG compression artifact removal. Experiment results show that our method outperforms the state-of-the-art image decomposition methods. Furthermore, our method is highly efficient, it is able to render real-time processing of 720P color images on a modern GPU. [ABSTRACT FROM AUTHOR]

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

In order to construct a numerical simulation model that can reflect the bedding structure of real shale samples, CT scanning and digital image processing were utilized to analyze the intricate internal structure of the shale. By integrating this information with RFPA3D finite element software, a 3D non-uniform numerical model that can accurately represent the shale bedding structure was built and simulated for direct tensile tests. The experimental findings demonstrated that shale possesses noticeable anisotropic properties with regard to tensile strength. Two distinct modes of damage were observed in shale specimens subjected to uniaxial tensile loading: tensile damage along the bedding plane and damage occurring perpendicular to the bedding plane. The spatial distribution characteristics of the acoustic emission points better reflect the damage pattern of the specimen, and the acoustic emission counting evolution characteristics are closely related to the damage process of the specimen. These research findings shed light on the fracture damage mechanism of shale formations, thereby contributing to a better understanding of the engineering stability evaluation for rock bodies. Furthermore, they offer a novel perspective for investigating the evolution of damage in rock particles. [ABSTRACT FROM AUTHOR]

Based on an analysis of the information processing mechanism in the primary visual cortex of biological vision, this study proposes an integration method of bar-combination of shifted filter responses (B-COSFIRE) filter with the differential evolution (DE) algorithm for enhancing the precision of events extraction. First, the B-COSFIRE filter incorporates trainable and unsupervised features, utilizing the two-dimensional expression of the difference-of-Gaussians (DoG) model to simulate the receptive field model. By capitalizing on the blur and shift properties of the DoG response, the proposed approach enhances the continuous effective signal while attenuating discontinuous noise signal, thereby demonstrating superior noise robustness compared to conventional methods. Second, the selectivity of proposed filter is not predefined during the implementation but automatically determined based on the given prototype pattern during the configuration process, resulting in a universal solution adaptable to various target patterns. Lastly, we employ the DE algorithm to optimize the feature selection process, enabling the extraction of a minimum feature subset that maximizes the performance of events characterization. The B-COSFIRE method is widely used in the field of image processing. When applying it to seismic exploration, the seismic data used by this algorithm is in 'sgy' format, providing richer information than traditional image data. The proposed model can effectively detect the event in seismic data with significant data volume and substantial noise interference. The B-COSFIRE filter method outperforms conventional edge detection techniques by accurately capturing seismic events of varying widths, aligning with the principles observed in biological vision mechanisms. The extracted events exhibit enhanced continuity and accuracy compared to existing approaches. [ABSTRACT FROM AUTHOR]

When a camera lens is directly faced with a strong light source, image flare commonly occurs, significantly reducing the clarity and texture of the photo and interfering with image processing tasks that rely on visual sensors, such as image segmentation and feature extraction. A novel flare removal network, the Sparse-UFormer neural network, has been developed. The network integrates two core components onto the UFormer architecture: the mixed-scale feed-forward network (MSFN) and top-k sparse attention (TKSA), creating the sparse-transformer module. The MSFN module captures rich multi-scale information, enabling the more effective addressing of flare interference in images. The TKSA module, designed with a sparsity strategy, focuses on key features within the image, thereby significantly enhancing the precision and efficiency of flare removal. Furthermore, in the design of the loss function, besides the conventional flare, background, and reconstruction losses, a structural similarity index loss has been incorporated to ensure the preservation of image details and structure while removing the flare. Ensuring the minimal loss of image information is a fundamental premise for effective image restoration. The proposed method has been demonstrated to achieve state-of-the-art performance on the Flare7K++ test dataset and in challenging real-world scenarios, proving its effectiveness in removing flare artefacts from images. [ABSTRACT FROM AUTHOR]

Partial discharge in oil paper insulation is an important reason for insulation breakdown. The study of partial discharge in transformer oil is of great significance for ensuring the reliability of transformers. In this paper, a comprehensive observation platform of partial discharge with needle plate electrode and oil-paper insulation is built, and the original shape image set of partial discharge is collected and established. Through multi-channel convolution processing of the true color image, gray level equivalent distribution of 3D section, segmentation stretching of threshold interval and binary neighborhood search, a general technical method suitable for processing a series of low brightness discharge images with background interference is finally obtained. [ABSTRACT FROM AUTHOR]

This research proposes an application of the image processing device for the detection of knitted fabrics. The machine rolls up knitted fabrics and then the fabrics moves through a detection area, where the camera is connected to the image processing device. Next, an image captured after a defect exists is sent to be processed and analyzed by the NI myRIO device. An area pixel is computed based on Hue, Saturation, and Lightness (HSL) system and interpreted for defect selection. This procedure is performed on region of interest (ROI) to verify the defect. Three defects, examined in this research, consist of small holes, loose threads, and crook knitting needles. These knitted fabric defects are classified from the proposed weighted voting classifier, the results were found that the defects of three knitted fabrics can be accurately predicted. The proposed method can be available to the textile industry. [ABSTRACT FROM AUTHOR]

Advances in deep learning technology have enabled the generation of more realistic deepfakes, which not only endanger individuals' identities but also exploit vulnerabilities in face recognition systems. The majority of existing deepfake detection methods have primarily focused on RGB-based analysis, offering unreliable performance in terms of detection accuracy and time. To address the issue, a grayscale-based deepfake detection method has recently been proposed. This method significantly reduces detection time while providing comparable accuracy to RGB-based methods. However, despite its significant effectiveness, the "key components" that directly affect the performance of grayscale-based deepfake detection have not been systematically analyzed. In this paper, we target three key components: RGB-to-grayscale conversion method, brightness level in grayscale, and resolution level in grayscale. To analyze their impacts on the performance of grayscale-based deepfake detection, we conducted comprehensive evaluations, including component-wise analysis and comparative analysis using real-world datasets. For each key component, we quantitatively analyzed its characteristics' performance and identified differences between them. Moreover, we successfully verified the effectiveness of an optimal combination of the key components by comparing it with existing deepfake detection methods. [ABSTRACT FROM AUTHOR]

Measuring the grain size distribution (GSD) of unconsolidated particles is critical to understanding coastal spreading, riverbed dynamics, and sediment transport. The current study presents a novel gravel automatic sieving (GAS) method designed to improve the accuracy and reliability of particle size analyses. At the macroscopic, the method utilizes the convex hull property of gravel to define the maximum extent of the searched gravel, effectively reducing over and under-segmentation problems. At the microscopic, the accuracy of gravel segmentation is improved by analyzing the color space characteristics of gravel to identify the pixel patches of gravel accurately. To validate the effectiveness of the GAS method, the proposed method was tested in both the laboratory and the field. In the laboratory, four artificial samples were processed using the GAS method, and the results were compared with those obtained using the traditional sieving method. The results showed that the correlation coefficients between the GAS method and the traditional sieving method ranged from 94.3% to 97.8%, and the relative errors ranged from 5.8% to 20.9%, demonstrating the validity of the GAS method. In addition, the application of ImageJ software to manually identify the particle size method (ImageJ method) was also compared with the mechanical sieving method, and the correlation coefficient between the two methods was greater than 98.2%, and the relative error was less than 10.9%, so the ImageJ method can be used as a standardized method to measure the other methods. In the field, sixteen images taken in four different regions and at different times were analyzed using the ImageJ method as a benchmark. The performance of the automatic with image filtering (AIF), BASEGRAIN, and the GAS methods also were compared. The results show that the relative errors range from 28.1% to 94.6% for the BASEGRAIN, 16.8% to 1003.6% for the AIF method, and only 5.6% to 30.7% for the GAS method. As a result, the GAS method demonstrates higher accuracy and stability in complex environments. • The convex hull hypothesis determines the position and maximum size of the gravel. • Proposed search rules for pixel patches based on the shape characteristics of the gravel. • A method for determining gravel pixel patches is proposed based on the macroscopic and microscopic characteristics of the gravel. [ABSTRACT FROM AUTHOR]

An intriguing area in the IP (image processing) is the recovery of noisy photographs from the noise caused by the salt and pepper. As the mistake rate rises and the image format varies, the issue with the current task does not go away. In this study, Salt and Pepper Denoising, Hybrid Balancing Composite motion Optimization with Adaptive Switching Modified Decision based Unsymmetric Trimmed Median Filter and Forensics-Based Investigation Algorithm is proposed (R-SPN-ASMD-UTMF-Hyb-BCO-FBIA). Initially, the input images are obtained from boat-typesrecognition dataset, cat-breeds-dataset, cars-image-dataset, butterfly-images 40-species dataset and birds-200 dataset. The images are pre-processed through an ASMD-UTMF filter. ASMD-UTMF does not expose any adoption of optimization systems to calculate the optimal parameters. Therefore, the proposed Hyb-BCO-FBIA is employed for optimizing the ASMD-UTMF weight parameters. The suggested system is implemented on MATLAB and the assessment metrics as Mean Square Error (MSE), Structural similarity index measurement (SSIM), Peak signal to noise ratio (PSNR), Normalized cross-correlation (NC), Image Enhancement Factor (IEF), Mean Square Error (MSE) are analysed. The proposed method attains higher PSNR, NC related with other SOTA (State-Of-The Art) methods. [ABSTRACT FROM AUTHOR]

Copyright of Afyon Kocatepe University Journal of Science & Engineering / Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi is the property of Afyon Kocatepe University, Faculty of Science & Literature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Image segmentation is one of the most important aspects of medical image processing. PET and PET/CT scanning are well-known imaging tools in medical image processing, which are used for figuring out the inner anatomy of the internal organs in a noninvasive manner. In this study, an unsupervised artificial neural network Hierarchical Self-Organizing Feature Map (HSOFM) is presented to isolate and extract abnormal regions in PET and PET/CT images. This method was implemented on three, images in which the tumor size decreases with time after receiving treatment, and then many mathematical models were tested to elect the proper one that more adequate to simulate the change behavior in order to predict the change in the tumor size in the coming time. The results indicate that this method was adequate to extract tumor areas with percentage the percent relative difference between the results of the proposed method and the radiologist work ranged from 0.00% to 33.766% for PET images and ranged from 5.809% to 34.459% for PET/CT scan images. The behavior of the abnormality size change with time was correctly simulated using exponential and linear mathematical models and the predicated tumor size values in the coming times were calculated the tumor size with over time for PET and PET/CT images. The resultant values of the coefficient of Determination (R2) for the exponential model were 0.93, while for the linear model equation was 0.89. This is evidence of the success of the regression model in predicting future results. [ABSTRACT FROM AUTHOR]

Dental caries occurs from the interaction between oral bacteria and sugars, generating acids that damage teeth over time. The importance of X-ray images for detecting oral problems is undeniable in dentistry. With technological advances, it is feasible to identify these lesions using techniques such as deep learning, machine learning, and image processing. Therefore, the survey and systematization of these methods are essential to determining the main computational approaches for identifying caries in X-ray images. In this systematic review, we investigated the primary computational methods used for classifying, detecting, and segmenting caries in X-ray images. Following the PRISMA methodology, we selected relevant studies and analyzed their methods, strengths, limitations, imaging modalities, evaluation metrics, datasets, and classification techniques. The review encompassed 42 studies retrieved from the Science Direct, IEEExplore, ACM Digital, and PubMed databases from the Computer Science and Health areas. The results indicate that 12% of the included articles utilized public datasets, with deep learning being the predominant approach, accounting for 69% of the studies. The majority of these studies (76%) focused on classifying dental caries, either in binary or multiclass classification. Panoramic imaging was the most commonly used radiographic modality, representing 29% of the cases studied. Overall, our systematic review provides a comprehensive overview of the computational methods employed in identifying caries in radiographic images and highlights trends, patterns, and challenges in this research field. [ABSTRACT FROM AUTHOR]

Acoustic measurements using sine sweeps are prone to background noise and non-stationary disturbances. Repeated measurements can be averaged to improve the resulting signal-to-noise ratio. However, averaging leads to poor rejection of non-stationary high-energy disturbances and, in the case of a time-variant environment, causes attenuation at high frequencies. This paper proposes a robust method to combine repeated sweep measurements using across-measurement median filtering in the time-frequency domain. The method, called Mosaic, successfully rejects non-stationary noise, suppresses background noise, and is more robust toward time variation than averaging. The proposed method allows high-quality measurement of impulse responses in a noisy environment. [ABSTRACT FROM AUTHOR]

Brain tumours are a major public health concern, and early and accurate detection is critical in treatment. Early and precise detection of brain tumors is paramount, yet current technologies often struggle to achieve the necessary level of accuracy due to inherent limitations in image processing and classification methodologies. While approaches like convolutional neural networks and optimization techniques have shown promise, they often fall short in capturing intricate patterns and textures or achieving sufficient sensitivity, emphasizing the need for more advanced and integrated solutions like the proposed BioSwarmNet model. The system includes a meticulously designed image processing pipeline that ensures data consistency and quality. BioSwarmNet, a novel combination of Fractional Order Differential Particle Swarm Optimisation (FODPSO) and Recurrent Neural Networks (RNNs), uses swarm intelligence and deep learning to revolutionize medical image classification. Using the well-known BRATS dataset, this study provides a promising avenue for improving diagnostic accuracy and efficiency in brain tumour detection, which has the potential to benefit both healthcare professionals and patients. Notably, the proposed system outperformed in key metrics such as 99.12% accuracy, 98.62% sensitivity, and 99.86% specificity. [ABSTRACT FROM AUTHOR]

In the field of 2-D image processing and computer vision, accurately detecting and segmenting objects in scenarios where they overlap or are obscured remains a challenge. This difficulty is worse in the analysis of shoeprints used in forensic investigations because they are embedded in noisy environments such as the ground and can be indistinct. Traditional convolutional neural networks (CNNs), despite their success in various image analysis tasks, struggle with accurately delineating overlapping objects due to the complexity of segmenting intertwined textures and boundaries against a background of noise. This study introduces and employs the YOLO (You Only Look Once) model enhanced by edge detection and image segmentation techniques to improve the detection of overlapping shoeprints. By focusing on the critical boundary information between shoeprint textures and the ground, our method demonstrates improvements in sensitivity and precision, achieving confidence levels above 85% for minimally overlapped images and maintaining above 70% for extensively overlapped instances. Heatmaps of convolution layers were generated to show how the network converges towards successful detection using these enhancements. This research may provide a potential methodology for addressing the broader challenge of detecting multiple overlapping objects against noisy backgrounds. [ABSTRACT FROM AUTHOR]

The development of diagnostic systems for rotating machines such as induction motors (IMs) is a task of utmost importance for the industrial sector. Reliable diagnostic systems allow for the accurate detection of different faults. Different methods based on the acquisition of thermal images (TIs) have emerged as diagnosis systems for the detection of IM faults to prevent the further generation of faults. However, these methods are based on artisanal feature selection, so obtaining high accuracy rates is usually challenging. For this reason, in this work, a new system for fault detection in IMs based on convolutional neural networks (CNNs) and thermal images (TIs) is presented. The system is based on the training of a CNN using TIs to select and extract the most salient features of each fault present in the IM. Subsequently, a classifier based on a decision tree (DT) algorithm is trained using the features learned by the CNN to infer the motor conditions. The results of this methodology show an improvement in the accuracy, precision, recall, and F1-score metrics for 11 different conditions. [ABSTRACT FROM AUTHOR]

In recent years, the prevalence of cancer has increased significantly around the world. Cancer is considered one of the most dangerous diseases in humans. Cancer screening devices, such as Magnetic Resonance Imaging (MRI), X-ray imaging, ultrasound imaging, and others, play an important role in its early detection. This study aims to facilitate cancer tumor detection on mobile phones with high accuracy in a short period of time using deep learning techniques. A brain tumor dataset was used, consisting of 4,489 images and 14 classified types, and experiments were carried out using ResNet 12, DenseNet, YOLOv8, and MobileNet to evaluate them in terms of accuracy, speed, and model size. ResNet12, DenseNet, YOLOv8, and MobileNet results indicated satisfactory accuracy ranging from 88% to 97.3%. YOLOv8 was the most suitable model, as its fastest inference time of 1.8 ms, preprocessing time of 0.1 ms, highest accuracy of 97.3%, and compact model size make it ideal for real-time mobile applications. [ABSTRACT FROM AUTHOR]

Globally, lung cancer is the primary cause of cancer-related mortality. Higher chance of survival depends on the early diagnosis of lung nodules. Manual lung cancer screenings depends on the human factor. The variability in size, texture, and shape of lung nodules may pose a challenge for developing accurate automatic detection systems. This article proposes an ensemble approach to tackle the challenge of lung nodule detection. The goal was to improve prediction accuracy by exploring the performance of multiple transfer learning models instead of relying solely on deep learning models. An extensive dataset of CT scans was gathered to train the built deep learning models. This research paper is focused on the Convolutional Neural Networks' (CNNs') ability to automatically learn and adapt to discernible features in the lung images which is particularly beneficial for accurate classification, aiding in identifying true and false labels, and ultimately enhancing lung cancer diagnostic accuracy. This paper provides a comparative analysis of the performance of CNN, VGG-16, and VGG-19. Notably, the built transfer learning model VGG-16 achieved a remarkable accuracy of 95%, surpassing the baseline method. [ABSTRACT FROM AUTHOR]

Image processing is vital in modern technology, offering a diverse range of techniques for manipulating digital images to extract valuable information or enhance visual quality. Among these techniques, image inpainting stands out, involving the reconstruction or restoration of missing or damaged regions within the images. This study explores advances in image inpainting and presents a novel approach that integrates coarse-to-fine inpainting and attention-based inpainting techniques. The proposed method in this paper leverages deep learning methods to enhance the quality and efficiency of image inpainting, achieving robust and high-quality results that balance structural integrity and contextual coherence. A comprehensive evaluation and comparison with existing methods showed that the proposed approach has superior performance in maintaining structural integrity and contextual coherence within images. [ABSTRACT FROM AUTHOR]

This paper focuses on how to reduce cheating and minimize errors while automatically grading paper-based multiple-choice questions (MCQ) by making the whole process relatively fast, less expensive, more credible, and fairer especially when the number of examinees and number of questions are large. Credibility is obtained when techniques and best practices are introduced in the design process of MCQ. Fairness is obtained by personalizing evaluation through permutation of answers and questions. The distance introduced in personalization has led to the modification of the traditional automatic grading process where an application mapping the test number with its responses in the grading software is loaded automatically at each start of the grading process. On the extracted header fields, 2DFFT is applied as well as the reduction of computed coefficients to obtain the corresponding final local characteristic in the representation. The minimization of image processing errors is then obtained by training a support vector machine (SVM) for handwriting optical character recognition (OCR) using the Mixed National Institute of Standards and Technology (MNIST) dataset with 99.5% accuracy. The tests are carried out in several subjects at Fotso Victor University Institute of Technology (UIT) in Bandjoun and the ColTech of the University of Bamenda and teachers as well as students after investigation have confirmed that our method reduces cheating and improves the error rate during grading with fewer complaints. [ABSTRACT FROM AUTHOR]

The complex systematics of the genus Sedum, the difficulties of its classification and the ambiguity of the concrete identification of the taxa brought about the need to implement a measurement system adaptable to field conditions, so as to facilitate the accuracy of data collection, avoiding the etiolation of samples and, therefore, the deterioration of the morphological structures subject to analysis. Thus, our study describes a digitization of the classic method of making measurements using millimeter paper, thus facilitating the subsequent statistical processing of quantifiable values. Depending on the number of pixels in the photos taken and the pixel/millimeter ratio, a variable measurement scale can be created depending on the size of the analyzed taxomes. The method used adds to the classic taxonomy, which is based on the analysis of morphological characteristics to determine the species of these succulent plants. The applicability of our method is shown by means of the example of an analysis performed on the flowers of the native species of the genus Sedum in the territory of Romania. [ABSTRACT FROM AUTHOR]

The proliferation of battery-operated end-user electronic devices due to technological advancements, especially in medical image processing applications, demands low power consumption, high-speed operation, and efficient coding. The design of these devices is centered on the Application-Specific Integrated Circuits (ASIC), General Purpose Processors (GPP), and Field Programmable Gate Array (FPGA) frameworks. The need for low-power functional blocks arises from the growing demand for high-performance computational units that are part of high-speed processors operating at high clock frequencies. The operational speed of the processor is determined by the computational unit, which is the workhorse of high-speed processors. A novel approach to integrating Very Large-Scale Integration (VLSI) ASIC design and the concepts of low-power VLSI compatible with medical image compression was embraced in this research. The focus of this study was the design, development, and implementation of a Power Delay Product (PDP) optimized computational unit targeted for medical image compression using ASIC design flow. This stimulates the research community's quest to develop an ideal architecture, emphasizing on minimizing power consumption and enhancing device performance for medical image processing applications. The study uses area, delay, power, PDP, and Peak Signal-to-Noise Ratio (PSNR) as performance metrics. The research work takes inspiration from this and aims to enhance the efficiency of the computational unit through minor design modifications that significantly impact performance. This research proposes to explore the trade-off of high-performance adder and multiplier designs to design an ASIC-based computational unit using low-power techniques to enhance the efficiency in power and delay. The computational unit utilized for the digital image compression process was synthesized and implemented using gpdk 45 nm standard libraries with the Genus tool of Cadence. A reduced PDP of 46.87% was observed when the image compression was performed on a medical image, along with an improved PSNR of 5.89% for the reconstructed image. [ABSTRACT FROM AUTHOR]

In the contemporary era, dizziness is a prevalent ailment among patients. It can be caused by either vestibular neuritis or a stroke. Given the lack of diagnostic utility of instrumental methods in acute isolated vertigo, the differentiation of vestibular neuritis and stroke is primarily clinical. As a part of the initial differential diagnosis, the physician focuses on the characteristics of nystagmus and the results of the video head impulse test (vHIT). Instruments for accurate vHIT are costly and are often utilized exclusively in healthcare settings. The objective of this paper is to review contemporary methodologies for accurately detecting the position of pupil centers in both eyes of a patient and for precisely extracting their coordinates. Additionally, the paper describes methods for accurately determining the head rotation angle under diverse imaging and lighting conditions. Furthermore, the suitability of these methods for vHIT is being evaluated. We assume the maximum allowable error is 0.005 radians per frame to detect pupils' coordinates or 0.3 degrees per frame while detecting the head position. We found that for such conditions, the most suitable approaches for head posture detection are deep learning (including LSTM networks), search by template matching, linear regression of EMG sensor data, and optical fiber sensor usage. The most relevant approaches for pupil localization for our medical tasks are deep learning, geometric transformations, decision trees, and RASNAC. This study might assist in the identification of a number of approaches that can be employed in the future to construct a high-accuracy system for vHIT based on a smartphone or a home computer, with subsequent signal processing and initial diagnosis. [ABSTRACT FROM AUTHOR]