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Smart machine tools must be able to recognise the materials they interact with, in order to independently decide on what actions to be taken whenever needed. The purpose of this research is to offer a generic method to automate material identification and classification task utilising image processing and machine learning techniques so as to improve the cognitive capabilities of machine tools. A dataset of four material surfaces; Copper, Aluminium, Stainless Steel, and Bronze is generated and the RGB data are extracted. These colour channels are utilised as input features to train the machine learning algorithms. Convolutional Neural Network (CNN) and other classification methods like Support Vector Machine (SVM), Decision Trees, and k-Nearest Neighbour are also used to classify material images based on the generated dataset. A unique generalised technique, based on CNN, has been proposed to accurately recognise and categorise round surfaced materials during machining. The accuracy achieved by the classifier has reached 100% during training and testing. Test images are used to confirm the proposed methodology's capability to distinguish between materials based on various illumination environments and camera positions. [ABSTRACT FROM AUTHOR]

Curved ceramic materials possess corrosion-resistant, heat-resistant, and exceptional hardness, rendering them indispensable in many critical fields, including automotive, aerospace, biomedical, architectural, and artistic applications. The detection of defects in these materials is of paramount importance; but traditional inspection methods fall short due to their unique structural characteristics. Presently, the assessment of curved ceramics predominantly relies on manual inspection, which is fraught with subjective errors and inefficiencies. This paper provides a comprehensive overview of the advancements in machine vision technology as applied to defect detection across various types of curved ceramic materials, including industrial, daily-use, and artistic ceramics. The paper highlights the key factors influencing the development of surface defect detection technology in curved ceramics. It summarises the advantages and disadvantages of traditional machine vision and deep learning approaches for defect detection in curved ceramics and analyzes their respective application scopes. Concluding with an examination of the technical challenges inherent in defect detection, the paper also outlines a prospective trajectory for the advancement of inspection technology in curved ceramic materials. [ABSTRACT FROM AUTHOR]

The quality of weld seams is a core factor affecting the performance of welded components, significantly impacting the safety and reliability of weldment structures. It holds considerable importance in quality control and cost management. Therefore, weld seam inspection is an indispensable part of industrial manufacturing and a key topic in non-destructive testing. This paper comprehensively summarises the main research results in weld seam feature extraction and defect identification based on systematic and detailed literature research. Firstly, the paper compares current mainstream non-destructive testing technologies, such as ultrasonic testing, radiographic testing, and infrared thermography, regarding detection principles, performance accuracy, and efficiency. It summarises their applicable scopes, as well as their advantages and disadvantages, and briefly introduces image processing-based methods for weld seam feature extraction. Secondly, a discussion and analysis of weld defect recognition are carried out, focusing on intelligent recognition methods based on deep learning, such as convolutional neural networks, generative adversarial networks, YOLO, etc. Finally, the outlook and future research trends are presented from various perspectives, such as real-time, adaptability, intelligence, etc. to provide a theoretical basis and technical support for welding quality control and structural safety assessment. [ABSTRACT FROM AUTHOR]

In the image analysis of the hydrophobic flocculation process of coal particles, the recognition accuracy of floc characteristic parameters is affected by bubbles, floc superposition, and floc morphology. An image processing method was studied and designed based on the PyCharm Community platform for clarity recognition and floc segmentation of coal particle floc characteristics. Firstly, the critical value of image clarity was determined to be 1.31 by using the Laplace convolution accounting method after the average gray level correction, and the fuzzy flocs whose clarity was less than the critical value were eliminated. Then, by comparing the segmentation effect of the threshold, edge, and region segmentation algorithm on the flocs, the histogram bimodal method was determined to be the best image processing segmentation method for fine coal floc collection. The fractal theory was used to verify the effect of image processing. The results showed that the surface fractal dimension of the floc image after deblurring and segmentation was maintained near 2.05, indicating that the processed image can obtain more stable and reliable floc characteristic parameters. [ABSTRACT FROM AUTHOR]

Public safety is a critical concern, typically addressed through security checks at entrances of public places, involving trained officers or X-ray scanning machines to detect prohibited items. However, many places like hospitals, schools, and event centres lack such resources, risking security breaches. Even with X-ray scanners or manual checks, gaps can be exploited by individuals with malicious intent, posing significant security risks. Additionally, traditional methods, relying on manual inspections and conventional image processing techniques, are often inefficient and prone to high error rates. To mitigate these risks, we propose a real-time detection model – EnhanceNet using a customized Scale-Enhanced Pooling Network (SEP-Net) integrated into the YOLOv4. The innovative SEP-Net enhances feature representation and localization accuracy, significantly contributing to the model’s efficacy in detecting prohibited items. We annotated a custom dataset of nine classes and evaluated our models using different input sizes (608 and 416). The 608 input size achieved a mean Average Precision (mAP) of 74.10% with a detection speed of 22.3 Frames per Second (FPS). The 416 input size showed superior performance, achieving a mAP of 76.75% and a detection speed of 27.1 FPS. These demonstrate that our models are accurate and efficient, making them suitable for real-time applications. [ABSTRACT FROM AUTHOR]

Stripe noise is a prevalent issue in infrared imaging systems, characterized by its distinctive directional features, which often appear as vertical lines across the image. This type of noise can significantly degrade the quality of the captured images, making it crucial to address and mitigate its effects. This paper presents an effective strategy to tackle this problem by transforming it from a 2D image issue into a 1D signal problem, enabling efficient resolution of stripes in infrared images. By understanding the characteristics of stripe noise, the proposed algorithm effectively solves the problem by first computing the column average of the noisy image, extracting stripe components from this one-dimensional signal, and effectively removing the stripes without blurring image details. This approach has been tested on numerous images with varying noise levels, demonstrating exceptional denoising performance compared to state-of-the-art methods. The results show marked improvements in visual quality, especially around edges and smooth areas, without requiring complex algorithms or iterative processes. [ABSTRACT FROM AUTHOR]

AbstractIn this experimental study, we delve into the performance of an active mixer that utilizes liquid metal droplets. Our mixer configuration follows an innovative Y-type design, where two liquid metal-based pumping mechanisms operate within the minichannel inlet branches. By applying alternating current voltage to both sides of the liquid metal, we induce a surface tension gradient, propelling the fluid from the reservoirs toward the minichannel. Notably, the two entering liquids are distinctly colored, allowing us to quantify mixing using a Mixing Index (MI) definition and image processing techniques. Our investigation centers on four key variables governing mixer performance: applied voltage, liquid metal droplet diameter, input angle between the branches, and minichannel width. Given the experimental nature of our research, we employ a central composite design method within the framework of design of experiments (DoE). From our obtained experimental results, we establish correlations between MI and the other variables. Our examination of the four mentioned parameters revealed that liquid metal droplet diameter exerts the most significant influence on the mixing index. Following this, in descending order of impact, we find applied voltage, input angle, and channel width. In the optimal dimensions of our mixer, we achieve a remarkable maximum MI of 0.867. [ABSTRACT FROM AUTHOR]

In this paper, a digital bio-dosimeter (DBD) with a square window size of 7 × 7 pixels is proposed for the quantitative detection of the deformed biological cells irradiated by X-rays with different energies and γ-rays from 60Co. The basic idea of the DBD is to compute the energy of the bio-dosimeter window size by summing up the entire gray levels. Peripheral blood films (PBFs) from male albino rats irradiated by ionizing radiations are prepared and photographed using an Automatic Image Contour Analysis system with high magnification to produce a 2D image. The surf function in MATLAB is used to convert the 2D image to a 3D shaded surface to resample the date more finely. The proposed DBD shows that the computed energies on the boundaries of the normal RBCs are nearly the same, while the computed energies on the boundaries of the deformed cells are significantly larger. [ABSTRACT FROM AUTHOR]

Image encryption is one of the most widely used techniques for securing images in trusted and unrestricted public media. However, the drawback is weak security in chaotic encryption algorithms, small key space and hardware implementation complexities. Various appropriate encryption algorithms have been carried out for secure image transmission; it becomes a critical issue to protect image integrity, confidentiality and authenticity. To overcome these issues, this article examines several existing memristor-based image encryption algorithms with various chaotic maps. As a result, this research aims to deliver comprehensive literature on image encryption techniques through memristor to help the researchers. Finally, this survey provides all vital literature on the current image encryption techniques with their benefits, drawbacks, developments, and future directions. We also give a general guideline about cryptography. We conclude that all methods are helpful for real-time image encryption. A comparison has been made between many image processing approaches (conventional and memristor) based on metrics such as the number of pixels change rate (NPCR) with the value of 0.9986, histogram, entropy (7.9993), unified average varying intensity (UACI) achieves the value as 0.4996, energy consumption as 0.32pJ and time complexity as 0.9s. The experimental results of various approaches, key sensitivity and statistical analysis revealed that the survey of memristor-based image encryption schemes provides an effective way to secure image transmission in real-time application. [ABSTRACT FROM AUTHOR]

Reliance on visual inspection for Maize leaf disease identification proves unreliable, often resulting in inappropriate pesticide application and associated health hazards. Food security requires precise and automated disease detection methods to save time and prevent crop losses. Although several studies have used deep and machine learning to detect plant leaf diseases from different perspectives, most of them require numerous training parameters or have low classification accuracy. Furthermore, a model that was developed for one region of the world might not be appropriate for another due to distinctions in morphology and other aspects. In this context, an application for mobile phones was developed that recognizes and classifies maize leaf diseases using a CNN-based pretrained VGG16 architecture. The model can detect northern corn leaf blight, common rust, and gray leaf spots in maize leaves in tropical climates. A total of 3024 images were used to generate the underlying model, including publicly available and field-collected images. The established model uses fewer training parameters to attain a training accuracy of 95.16% and a testing accuracy of 93%. The model provides farmers with an early warning system for early detection of plant diseases, enabling them to take preventive measures before significant production deficits occur. [ABSTRACT FROM AUTHOR]

Advancements in satellite technology coupled with image processing, statistical tools and Artificial Intelligence (AI) algorithms, enable efficient, non-invasive, real-time monitoring and analysis of landfills. Landfills pose a severe environmental threat to the local ecosystem. Metropolitan cities in most developing countries usually have multiple landfills. Landfill Land surface temperature (LST) monitoring can help keep track of the changes in landfills, especially concerning waste management, land use, hotspot movement, fire prediction and the stability of the landfill. Monitoring these waste disposal sites through remote sensing data can provide real-time monitoring and analysis, supporting quick interventions when necessary. This research utilizes LANDSAT 8 satellite images for Mumbai, India. Using geospatial analysis, image processing and data processing, LST is derived for 576 LST data points. While landfill LST has a positive trend over time, but there are cyclical, seasonal and changes due to disruptive events. With landfill LST consistently remaining the highest over the years, in comparison to built-up, green and sea LSTs. A statistical analysis of the data reveals the overall significance of the model in ascertaining the increase in temperature due to the presence of landfills. Despite challenges such as large data libraries and variable environmental factors, this research emphasizes and contributes to satellite imagery's evolving potential for monitoring and assessing LSTs. [ABSTRACT FROM AUTHOR]

Hyperspectral images can be widely used in many fields due to their high information richness. However, costly and complex imaging spectrometers limit its growth. Hyperspectral reconstruction aims to obtain the corresponding hyperspectral image from the multispectral image, to reduce the acquisition cost of hyperspectral images. At present, the related works mainly use methods based on deep learning, and they have achieved good results. However, how to fully extract the global spectral and spatial features of hyperspectral images is still not well solved. To address these issues, we propose an Attention and Transformer Complementary Fusion Network (ATCFNet), which is composed of three modules: Multi-angle Input Image Processing (MIIP), Deep Feature Extraction (DFE) and Hyperspectral Reconstruction (HR) modules. Within the DFE module, an improved Transformer module and a novel Multi-scale Spatial Attention (MSA) module are proposed to extract the global spectral relationship and the spatial features of the hyperspectral images, respectively. Moreover, the MIIP module is proposed to extract the features of input multispectral images more effectively and comprehensively. To verify these, we compare the proposed ATCFNet with other excellent reconstruction methods on three hyperspectral image datasets. The results show that our method achieves the best results in these datasets. Code is publicly available at . [ABSTRACT FROM AUTHOR]

In this paper, we propose an inertial proximal splitting method for solving the non-convex optimization problem, and the new method employs the idea of inertial proximal point to improve the computational efficiency. Based on the assumptions that the sequence generated by the new method is bounded and the auxiliary function satisfies the Kurdyka–Łojasiewicz property, the global convergence analysis with a more relaxed parameter range is proved for the proposed method. Moreover, some numerical results on SCAD, image processing and robust PCA non-convex problems are tested to demonstrate the effectiveness and superiority of the proposed method. [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 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]

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]

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]

Roads are one of the most critical infrastructures, which should be maintained at a high quality of service. For this purpose, road pavement should be assessed cost-effectively. In the past, image processing methods were used to analyze pavement conditions. In recent years, machine learning methods have been employed, while now deep learning methods are applied. Deep learning has outperformed other methods regarding the accuracy and speed of pavement distress evaluation. In this research, a deep learning algorithm called YOLOv5 is deployed to detect pavement block cracking and estimate its severity using images taken from the right of way via a road surface profiler. Two models are successfully trained and tested, one to detect block cracking and the other to predict its severity with a sufficient level of accuracy of 84.5% and 76.6%, respectively. It is concluded that the model not only can detect block cracking but also predict its severity. [ABSTRACT FROM AUTHOR]

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AbstractThe deflection modeling during the insertion of bevel-tipped flexible needles into soft tissues is crucial for robot-assisted flexible needle insertion into specific target locations within the human body during percutaneous biopsy surgery. This paper proposes a mechanical model based on cutting force identification to predict the deflection of flexible needles in soft tissues. Unlike other models, this method does not require measuring Young’s modulus (E) and Poisson’s ratio (ν) of tissues, which require complex hardware to obtain. In the model, the needle puncture process is discretized into a series of uniform-depth puncture steps. The needle is simplified as a cantilever beam supported by a series of virtual springs, and the influence of tissue stiffness on needle deformation is represented by the spring stiffness coefficient of the virtual spring. By theoretical modeling and experimental parameter identification of cutting force, the spring stiffness coefficients are obtained, thereby modeling the deflection of the needle. To verify the accuracy of the proposed model, the predicted model results were compared with the deflection of the puncture experiment in polyvinyl alcohol (PVA) gel samples, and the average maximum error range predicted by the model was between 0.606 ± 0.167 mm and 1.005 ± 0.174 mm, which showed that the model can successfully predict the deflection of the needle. This work will contribute to the design of automatic control strategies for needles. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) combined with image processing has shown significant improvements through new techniques such as Machine Learning (ML) models. This paper introduces the key methods and algorithms used for Drone image processing. We discuss the benefits and limitations of using ML models instead of classical techniques. Our goal is to classify, categorize and describe the methods that are used in realistic settings of diverse domains of applications. We conducted a systematic literature review where systems presented in the papers were analysed based on their domain, task, technology, and efficiency. By extensively reviewing the existing literature, we successfully identified key themes and trends that emerged across the various research questions. The overall findings of the research emphasise the potential of AI and drone imagery in numerous fields. However, the review also uncovered several challenges that necessitate attention, such as issues related to data quality and the requirement for more advanced AI algorithms. The paper outlines significant innovations in the field and offers recommendations for future research directions. By highlighting cross-disciplinary insights, it delves into methodological approaches, exploring commonalities in AI algorithms and UAVs technologies. [ABSTRACT FROM AUTHOR]

To enhance the safety of autonomous vehicles in mixed traffic and connected environment, it is crucial to recognize the lane-changing intentions (LCIs) of human-driven vehicles for autonomous vehicles. This paper presents a novel method for LCI recognition, which extracts features from the driving state and relative motion of the target vehicle and its neighbors. The method applies short-time Fourier transform, Gramian angular summation field, and Gramian angular difference field to the time-series data, and generates three grayscale images, which are merged into one information fusion image (IFI) by image processing techniques. The IFIs are then classified into three categories: lane keeping, lane-changing left, and lane-changing right, using the Vision Transformer model with transfer learning to speed up convergence and reduce training cost. The experimental results demonstrate that the proposed method outperforms the traditional methods, achieving an accuracy of 95.65% for recognizing LCI 3s before the lane change point. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing (HU) has been a vital technique in hyperspectral image processing. Over the past years, many algorithms have been developed based on the nonnegative matrix factorization (NMF) framework. However, the conventional methods based on the NMF need prior information of the number of endmembers, which is usually estimated by a separate procedure. Therefore, the precision of estimating the number of endmembers will impact the subsequent unmixing effect. Thus, collaborating between the two processes is desired to avoid the propagation of errors. This paper proposes a new NMF-based method, which can estimate the number of endmembers and extract the endmembers and the abundance maps. Firstly, we introduce the weighted group sparsity regularization to adaptively eliminate the abundances corresponding to the redundant endmembers, while the abundances of the true endmembers are preserved. Then the number of endmembers can be determined by the reconstruction error function and the ${l_2}$ l 2 -norm of the abundance vectors. Secondly, since the simplex determined by the endmembers has the minimum volume among all simplexes that circumscribe the data points, we impose the total variational regularization to force the endmembers to be close to each other to minimize the simplex volume. And because the total variational regularization avoids the use of noisy pixels, it can decrease the influence of noise and improve the robustness of the solution. For the proposed model, we design an effective algorithm to solve it. Also, the experimental results based on the synthetic data and real data demonstrate that the proposed method can identify the endmembers number well and achieve robust unmixing results. [ABSTRACT FROM AUTHOR]

Energy-efficient designs are the need of the hour for signal and image processing applications that are error-tolerant. The paper proposes a new 4:1 approximate arithmetic-based compressor developed by analyzing the probability-based tactics that have a higher degree of precision when implemented. Approximate $ n \times n $ n×n multiplier is designed using the proposed varying probability-based compressors. To validate the effectiveness of the new compressor, a $ 16 \times 16 $ 16×16 multiplier is designed. The $ n \times n $ n×n bit multiplier will have $ 2n $ 2n columns of partial products(PP), in which the least significant n−1 columns are designed using the novel approximate 4:1 compressor and most n + 1 columns are designed with the exact compressor. The results of the 16-bit multiplier are superior to the performance of the exact multiplier in terms of power, area, power-delay-product (PDP), and area-delay-product (ADP) typically by $ 39\% $ 39%, $ 20\% $ 20%, $ 44\% $ 44%, and $ 26\% $ 26% respectively for Design 1 and $ 14\% $ 14%, $ 24\% $ 24%, $ 30\% $ 30%, and $ 21\% $ 21%, respectively for Design 2. The Peak Signal to Noise Ratio(PSNR), Mean Square Error(MSE), and Structural Similarity Index Measure(SSIM) for the output images processed by new approximate multipliers are acceptable when compared with precision and energy. [ABSTRACT FROM AUTHOR]

Biometric devices have been indicated to be vulnerable to various threats. In the present study, a novel liveness identification technique is introduced according to the noise analysis of fingerprint images. It has been shown that the textural patterns of real fingertips are different from those of spoof fingertips. In this work, Real images were successfully distinguished from fake ones with an accuracy of approximately 99.50%. The database consists of 200 fingerprints, half of which is the real database (i.e. 100 fingerprints), and the other half is the fake database. Textural features are analyzed using pyramidal mean and PDFB analysis techniques that decompose images into directional sub-bands at several scales. The Shannon entropy, approximate entropy, sample entropy, permutation entropy, fuzzy entropy, multiscale entropy, Log energy entropy, and Sure entropy were estimated for all these sub-bands and were arranged into an array. In the classification stage, an ensemble classifier based on a multiobjective genetic algorithm was employed. The results obtained revealed that the suggested technique could successfully differentiate live fingertips from fake ones. The best accuracy was obtained for the proposed ensemble technique, with an average accuracy of 99.52% compared to other classifiers. After the ensemble method, quadratic SVM achieved the best classification performance with a mean accuracy of 97.34% in discriminating real live fingerprints from fake fingerprints. In comparison to other prior techniques, the suggested method produced good classification performance in real-time fingerprint identification while remaining straightforward. Therefore, the system can be a good option for use in real biometric systems. [ABSTRACT FROM AUTHOR]

Fresh Oudemansiella raphanipies are rich in protein and prone to deterioration during natural storage. Hot air drying is typically used to extend shelf life. However, some O. raphanipies may not dry during the actual drying process because of different placements, which can adversely affect the entire product. To detect dryness, we introduced ORD-Net, a model based on RGB images captured using a smartphone camera. Incorporating residual hybrid dilated convolution and efficient channel attention modules, ORD-Net outperforms mainstream algorithms, achieving high accuracy, precision, recall, F1-score, and a Matthews coefficient of 85.3, 82.2, 91.2, 86.5%, and 0.709, respectively. Additionally, experiments on various CPUs confirm its adaptability for industrial use, with AMD Ryzen CPUs performing optimally with an average processing time of 0.67 s for a single target. This study proposes a convenient and cost-effective identification method that can effectively determine the drying status of O. raphanipies, thus laying the foundation for the high-throughput identification of the drying conditions of postharvest O. raphanipies. The data and methods used in this study are available at . [ABSTRACT FROM AUTHOR]

Video processing is the most emergent area of image processing in Research and Development. This paper, we present some advanced technology interrelated to video and image processing, which will provide a super-resolution high-quality most prominent visible light video for better human visualization and background motions or gesticulations of the human brain. There are two approaches for increasing the current resolution level of an image. The first one is the improvement of spatial resolution by reducing the size of pixels through the techniques of sensor manufacturing by about 40 $ {\mathcal{M}}{\textrm{m}^2} $ M m 2 for a 0.35 $ {\mathcal{M}}\textrm{m} $ M m CMOS process and the second approach is the enhancement of chip size that also enhances the capacitance. In video processing, we mainly focus on object images, i.e. any form of signal processing in which the input is an image and the output is a photograph or a frame of video. So, it will be better to converge over techniques of digital image processing. In the proposed algorithm (MHI Simulink model), edge extraction is performed to find the precious area of MRI and CT-Scan images. Using FPGA, edge detection methods have been implemented over scanned images to get better accuracy. The accuracy of the proposed method has been compared with Prewitt and Sobel's edge detection techniques. The proposed method has given better accuracy than Prewitt and Sobel methods. Finally, this paper shows future directions for researchers to enhance the characteristics of digital image and video processing. [ABSTRACT FROM AUTHOR]

The characterisation of edges in medical images is critical for disease diagnosis. However, existing systems are still deficient in this task. Traditionally, integer-based derivative operators are employed due to their efficiency in time complexity but lack the ability to track nonlocal and non-singular edge maps. This study proposes a new mask based on Atangana-Beleanu fractional operator. This operator has the same complexity as the state-of-the-art integer-order derivative mask known as the Canny edge detector but has the added advantage to characterise more efficiently nonlocal and nonsingular edge maps. Performance evaluation of the proposed mask reveals an enhanced performance in the context of robustness to noise and quality edge extraction, a significant contribution to literature. The metric for the study is the signal-to-noise ratio, and the structural similarity index and appropriate mask observed is a mask of dimension greater than five. [ABSTRACT FROM AUTHOR]

Thyroid carcinoma ranks as the 9th most prevalent global cancer, accounting for 586,202 cases and 43,636 deaths in 2020. Computerized image analysis, utilizing artificial intelligence algorithms, emerges as a potential tool for tumor evaluation. This study aims to assess and compare chromatin textural characteristics and nuclear dimensions in follicular neoplasms through gray-level co-occurrence matrix (GLCM), fractal, and morphometric analysis. A retrospective cross-sectional study involving 115 thyroid malignancies, specifically 49 papillary thyroid carcinomas with follicular morphology, was conducted from July 2021 to July 2023. Ethical approval was obtained, and histopathological examination, along with image analysis, was performed using ImageJ software. A statistically significant difference was observed in contrast (2.426 (1.774–3.412) vs 2.664 (1.963–3.610), p =.002), correlation (1.202 (1.071–1.298) vs 0.892 (0.833–0.946), p =.01), and ASM (0.071 (0.090–0.131) vs 0.044 (0.019–0.102), p =.036) between NIFTP and IFVPTC. However, morphometric parameters did not yield statistically significant differences among histological variants Computerized image analysis, though promising in subtype discrimination, requires further refinement and integration with traditional diagnostic parameters. The study suggests potential applications in scenarios where conventional histopathological assessment faces limitations due to limited tissue availability. Despite limitations such as a small sample size and a retrospective design, the findings contribute to understanding thyroid carcinoma characteristics and underscore the need for comprehensive evaluations integrating various diagnostic modalities. [ABSTRACT FROM AUTHOR]

Extracting offshore aquaculture areas from high-resolution remote sensing images is significant for rational planning and dynamic management of aquaculture. However, the complex and changeable characteristics of aquaculture areas will lead to difficulty during high-resolution image processing. In this paper, an improved U-Net deep learning model is proposed to extract offshore aquaculture areas from high-resolution images. This model is improved by the pyramid pooling module (PPM) and up-sampling structure based on the original U-Net model. PPM and the up-sampling structure can better mine semantic and position information in high-resolution images, reduce the interference and adhesion of information in images, and improve the extraction accuracy of unclear aquaculture areas. Based on Gaofen-2 remote sensing image data of Rongcheng Bay in the eastern waters of Weihai City, Shandong Province, the extraction of offshore aquaculture areas is studied. The results show that the F1 score and MIoU of the proposed network model are 93.63% and 87.93%, respectively. Compared with several commonly used deep learning models, the improved U-Net model has a better extraction effect. [ABSTRACT FROM AUTHOR]

One of the most challenging scenarios for drivers of open-pit mines is driving in dense fog. As a result, drivers cannot see the road, which causes significant vehicle colloidal accidents. To overcome the problem, an intelligent vision enhancement system has been developed by real-time processing and integrating data from the vehicle-to-everything (V2X) module, global navigation satellite system (GNSS), radar, and infrared cameras to enhance the visibility range of the operative road to the driver and mitigate the chances of accidents. Final images after image processing and object detection are displayed using a dashboard screen in front of the driver for adaptive control and safe operation. Performance analysis of the visual computing algorithm has been carried out in a mine based on image quality parameters. Field-tested images' average contrast value, peak signal-to-noise ratio, structural similarity index, visual information fidelity, and universal quality index were 0.87, 19.36, 0.39, 0.82, and 0.56, respectively, and the calculated accurateness of the convolution neural network (CNN)-based object detection model was 90%. These values indicate the output of the image processing algorithm provided better-enhanced images in the foggy condition. With the help of image processing algorithms, the camera view is able to provide a clear vision of more than 30 m in dense fog. Further, using V2X technology, the augmented visibility range has been increased up to 200 m. The 3D augmented reality (AR) view and 2D navigation view provide other surrounding vehicle locations. Untagged objects like rocks or bunds are also detected and displayed using radar. HIGHLIGHTS: The system fuses IR cameras, V2X, GNSS, radar, & visual computing for safe driving Image processing algorithm fuses CLAHE and DCP methods for clear image output CNN-based object detection algorithm integrated with image processing software Provides multi-stage safety using camera, 2D navigation, & 3D virtual reality views Intelligent system inbuilt with software and advanced analytics for fleet management [ABSTRACT FROM AUTHOR]

In recent years, with the rapid advancements in deep learning, image processing has progressively attracted the attention of medical researchers to achieve accurate diagnoses of numerous human diseases in a non-invasive manner. Image processing systems are among the most helpful and crucial disease identification technologies. Liver cancer is the world's fifth most prevalent and leading cause of cancer deaths. Diagnoses and treatment are essential because of the high mortality rate and recurrence after treatment. Medical image analysis is extremely beneficial in the healthcare industry. In the last several years, creating and developing computer-assisted imaging systems has been increasingly vital to aid physicians or doctors in strengthening their diagnoses. This research presents a deep model to identify cancerous regions in the liver aims using Computerized Tomography (CT) scans from the Kaggle dataset. Basic preprocessing is applied to CT images from the dataset acquired from the Kaggle repository. The performance of the proposed model is evaluated using different performance metrics, and an accuracy score of 94.3% was achieved in tumour identification. [ABSTRACT FROM AUTHOR]

Recently, huge concerns have been raised in diagnosing chest diseases, especially after the COVID-19 pandemic. Regular diagnosis processes of chest diseases sometimes fail to distinguish between Corona and Viral Pneumonia diseases through Polymerase Chain Reaction (PCR) tests which are a time-engrossing process that needs convoluted manual procedures. Artificial Intelligence (AI) techniques have achieved high performance in aiding medical diagnostic processes. The innovation of this work lies in using a new diagnostic technique to distinguish between COVID-19 and Viral Pneumonia diseases using advanced AI technologies. This is done by extracting novel features from chest X-ray images based on Wavelet analysis, Scale Invariant Feature Transformation (SIFT), and the Mel Frequency Cepstral Coefficient (MFCC). Support vector machines (SVM) and artificial neural networks (ANN) were utilized to build classification algorithms using 1200 chest X-ray mages for each case. Using Wavelet features, the results of evaluating the SVM and ANN models were 97% accurate, and with SIFT features, they were closer to 99%. The proposed models were very effective at identifying COVID-19 and Viral Pneumonitis, so physicians can determine the best treatment course for patients with the support of this high accuracy. Moreover, this model can be used in hospitals and emergency rooms when a massive number of patients are waiting, as it is faster and more accurate than the regular diagnosis processes as each step takes few seconds on average to complete. [ABSTRACT FROM AUTHOR]

The heat exchange tubes in the steam generator are susceptible to vibration caused by fluid flow, which can lead to damage to both the tubes and their support structures due to collisions. To enhance the predictive accuracy and cost effectiveness of fluid-elastic instability mitigation, multiple models have been created to circumvent its occurrence. In this research, a model has been developed to predict fluid-elastic instability in tube arrays by integrating Hassan's time-domain-solving model with a parameter acquisition method using computational fluid dynamics (CFD) simulations. By utilizing CFD methods, a comprehensive set of tube-in-channel model parameters were acquired. This method eliminates the requirement of empirical parameters obtained through experiments. The acquired parameters were integrated into the time-domain, tube-in-channel model. This model predicts fluid-elastic stability for a single flexible tube or a bundle of seven tubes within a rigid tube array, accounting for fluid forces in the lift direction. The stability map accurately represents the stiffness effect of flow-induced vibration, agreeing with experimental results and highlighting that the model may effectively utilize parameters obtained from CFD simulations. The combination of the time-domain-solving model and the CFD-based parameter acquisition method has been shown to produce a reliable model. [ABSTRACT FROM AUTHOR]

Operational modal analysis (OMA) is required to maintain large-scale and necessary civil infrastructures. The non-contact method of OMA using digital image correlation and point tracking algorithms requires a speckle pattern placed on the structure. Alternatively, advanced computer vision methods like optical flow and phase-based video motion magnification (PBVMM) techniques are used to measure modal parameters. Despite the importance of PBVMM, the users should know the range of frequencies in which the natural structure frequency lies. A methodology based on an unsupervised machine learning technique is developed to extract the modal parameters blindly from its recorded digital video. The proposed methodology uses complex steerable pyramids and an unsupervised machine learning technique, also known as principal component analysis, and analytical mode decomposition with a random decrement technique to blindly extract the modal parameters of a structure. This study validated the proposed methodology using a multi-degree of freedom (DOF) numerical model. The results are compared with theoretical and estimated values and are in good agreement. Furthermore, it is implemented on a laboratory benchmark SDOF, MDOF, and real-time videos of the London Millennium and Tacoma Narrows bridges for blindly extracting the modal frequencies and damping ratios. [ABSTRACT FROM AUTHOR]

Image denoising is a crucial algorithm in image processing that aims to enhance image quality. Deep learning-based image denoising methods can be categorized into supervised and unsupervised approaches. Supervised learning requires pairs of noisy and noise-free training data, which is impractical in real-world scenarios. Unsupervised learning uses pairs of noisy images for training, but it may yield lower accuracy. Additionally, deep learning-based methods often require a large amount of training data. To overcome these challenges, this research proposes a self-validation Noise2Noise (SV-N2N) framework that generates validation sets using only noisy images without requiring noise-free pairs. The proposed SV-N2N method effectively reduces noise, comparable to supervised and unsupervised methods, without requiring a noise-free ground truth, which is efficient for solving real-world scenarios. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI), is a field of science and engineering that deals with intelligent behaviour which has the potential of improved access and the cost of healthcare applications.Polycystic Ovarian Syndrome (PCOS) is characterised by a protracted menstrual cycle and frequent excess androgen levels, which often affect many women of reproductive age. There are now no trustworthy objective tests that might fully confirm the diagnosis and comprehension of PCOS. Effective image processing steps have been utilized in this work for the automation of PCOS diagnosis and the classification algorithms used are DarkNet-19, AlexNet, SqueezeNet, and SVM. As a result of the classifier's accelerated PCOS diagnosis and improved performance analysis, there will be fewer instances of potentially deadly consequences that can arise from a delayed diagnosis. The system produced metrics demonstrating improved performance, such as accuracy to provide a comprehensive ultrasound picture diagnosis using the classifier DarkNet-19 with 99%. [ABSTRACT FROM AUTHOR]

Quarry fines are secondary materials that can be reused for road construction, commonly after improving their properties through stabilising. To better understand and characterise these recycled materials, we created an image-based, two-dimensional representation of the particles that compose the material. This computational representation enables (1) the quantification of the (area-based) gradation from images, (2) a detailed particle-by-particle characterisation and (3) potentially, random rearrangement of the particles into reconstructed specimens for further computational analyses. The images of cement-stabilised quarry fines were obtained using a scanning electron microscope. The particles were isolated and characterised using image processes and a computational approach (MATLAB code). The techniques and code presented herein can be applied as well to other granular-based materials. The MATLAB code and associated files are available as an open resource in GitHub (). [ABSTRACT FROM AUTHOR]

AbstractDespite the fact that most police departments in the U.S. serve jurisdictions with fewer than 10,000 residents, policing practices in small towns are understudied. This is due in part to data limitations and technological barriers that exist in the small-town context. In this article we focus on one small town police department in New England with a history of misconduct, and develop a comprehensive data science pipeline that addresses the stages from design and collection to reporting. We present the reader with specific tools in the open-source Python ecosystem for replicating this pipeline. Once these data are processed, we perform two statistical analyses in an attempt to better understand the provisions of service by the small-town police department of focus. First, we perform ecological inference to estimate the rate at which residents are placing calls for service. Second, we model wait times using a negative binomal regression model to account for overdispersion in the data. We discuss data and model limitations arising through the pipeline creation and analysis process. [ABSTRACT FROM AUTHOR]

Anaemia is a blood-related disorder that is sometimes a harbinger of a much more severe condition like cancer. WHO recommends context-specific diagnosing methods for anaemia in the case of in-feasibility of traditional diagnosing methods, including a haemoglobin colour scale method that is based on the colour of the blood. In this manifesto, we take it one step further and diagnose anaemia using images of lower palpebral conjunctiva captured in consumer-grade cameras. Data that include lower palpebral conjunctiva images, basic details, and haemoglobin values are collected from participants. Pixel parameters of collected conjunctiva images are extracted in various colour spaces and used as input features. An ensemble learning model was developed with K-Nearest Neighbour (KNN), Random Forest and Decision Tree algorithms which showed better classification performance in classifying anaemic and non-anaemic samples. This Ensemble Model-Based Anaemia Classifier (EMBAC) performed well with both training and test sets and produced 90.91% sensitivity, 89.06% specificity, 89.69% accuracy, and area under the Receiver Operating Characteristics(ROC) curve of 0.90 for the unseen test data. EMBAC also outperformed the conventional ensemble algorithms in terms of sensitivity and accuracy. EMBAC will classify the samples more correctly irrespective of the image acquisition device under any lighting conditions without any additional hardware. This research work attempts to lay the foundation for a smartphone application to detect anaemia in real-life conditions which will enable access to a mobile healthcare instrument to diagnose anaemia for the proletariat. [ABSTRACT FROM AUTHOR]

AbstractRecycling and recovery of domestic waste pose significant challenges in terms of accurate object positioning and ranging, areas where conventional methods, heavily reliant on monocular image processing, often fail due to limited distance data and reduced precision. This study introduces an innovative strategy for the localization and ranging of recyclable waste objects, leveraging the NanoDet-Plus model within a binocular stereo vision context. A dataset, the Binocular Recyclable Domestic Waste Dataset (BRDWD), was constructed, with transfer learning executed via the pretrained MULTI-TRASH monocular dataset. Procedures encompassed stereo camera calibration and rectification to address geometric distortions, while the integration of the Semi-Global Block Matching (SGBM) algorithm supplied disparity details for triangulation-based distance computations. Enhancements to the NanoDet-Plus model led to improved target accuracy, evidenced by a mean average precision at a threshold of 0.5 (mAP@0.5) reaching 91.88% and a detection speed of 15 frames per second (FPS). Assessments verified a ranging error below 5% within the 0.5 to 1.2-meter span, aligning well with the requirements of typical recyclable waste sorting and recycling situations. Additionally, a human-machine interface was developed utilizing Pyside6, enabling image uploads, real-time result visualization, and interactive process control, thereby furnishing pivotal advancements for the automation and intellectualization of waste categorization and recycling. [ABSTRACT FROM AUTHOR]

The increasing scale of education has decreased the teacher–student ratio, making it difficult to keep track of student's progress individually. Therefore, teachers are leveraging artificial intelligence tools such as smart tutoring digital tools like learning management systems, and automated solvers for various subjects. Mathematics is one of the most fundamental subjects in school education. This work deals with facilitating the automatic solving of geometry math word problems (MWPs) at the elementary school level. To solve such problems, the information from the text as well as the accompanying figure needs to be interpreted. The preliminary task in solving such problems is to first identify which geometric shape is given in the question. Most existing systems or geometry solvers give the shape as one of the inputs to their systems to solve it further. In this work, the focus is on detecting the shape automatedly and proposing a technique for the same using traditional image processing methods. In addition, further, the triangles and quadrilaterals are classified into fine-grained classifications by taking into account their geometrical properties. Performance metrics like precision, recall, accuracy, and F1-score are used to present the results of this approach. The testing is done on various datasets that are readily available. The results show that on Geometry3K, GeoQA, and GeoS datasets the proposed technique attains an accuracy of 77.8%, 93.38%, and 95.02% and an F1 score of 60%, 71%, and 91.97% respectively. Like all other techniques, our system also has a limitation in that it does not detect all inscribed shapes. [ABSTRACT FROM AUTHOR]

This paper presents the methodology and theoretical foundation of topology optimization (TO) Results Spaceframe Interpreter, an automatic TO results interpreter that generates a closely associated space frame consisting of welded structural tubing or rectangular bars. TO is a computational technique that uses a finite element (FE) formulation to identify the most weight-efficient structure within a design domain. Density-based TO results in structures that take organic forms and is usually a tedious and cumbersome process to generate a computer-aided-design (CAD) model to manufacture through conventional techniques. The optimal topology frequently resembles a space frame, which is well-known as being a rigid, lightweight structure. The methodology of the TO results interpreter leverages several techniques from volumetric image processing and has four primary processes. First, the results are obtained from commercial FE/TO software and mapped into a cubic grid of voxels. Second, junction locations are extracted and member connectivity that represents a frame is identified. Third, a sizing optimization is incorporated to determine appropriate sectional dimensions of the circular or rectangular space frame members. Fourth, the optimized space frame geometry is imported into a CAD design tool to automatically create a design model. The logic and are implemented within the frame extraction processes. The automated TO interpreter is designed to interact with commercial FE analysis and CAD systems. The interpreter is demonstrated on various spatial examples including aerospace and automotive applications. In each case, the welded space frame closely resembles the TO result, with nearly equivalent stiffness and mass. [ABSTRACT FROM AUTHOR]

This paper introduces a novel thresholding and morphology-based skull stripping method for different MRI modalities. The proposed method is designed in a way which is easy to use and generates satisfactory results with minimal parameter adjustments. The method is evaluated on three different benchmark datasets and compared with nine state-of-the-art skull stripping methods. The experimental results suggest that the proposed method generates comparable results to some of the best methods in literature. However, unlike many other methods, it works well on different types of MRI scans. Moreover, this method generates the skull mask along with the brain mask that can be used to study various skull pathologies. [ABSTRACT FROM AUTHOR]

The objective of the study was to create a reliable method that could be used to evaluate the particle size distribution of samples and pre-mixes in real-world situations, particularly those consisting of typical formulation blends. The goal was to use this method to assess the uniformity of the samples and ensure that they met the required quality standards. The researchers aimed to create a method that could be easily incorporated into the manufacturing process, providing a practical and efficient solution. This study demonstrates the use of ImageJ software to analyze the particle size distribution (PSD) of powders. The technique produces qualitative data from microscopy images and quantitative data from analysis of parameters including average diameter, D10, D50, D90, and standard deviation. The method was tested with various treatments, showing differentiating outcomes in all cases. The alternate technique provides a rapid and cost-effective method for PSD analysis, surpassing the limitations of sieve analysis. Extensive testing of the method, using a variety of sample types, including typical formulation blends, was performed. The results suggest that the method can effectively assess the morphology of changing materials during batch manufacturing and characterize uniformity in blends. The methodology has the capability to identify attributes related to PSD that are typically required to be monitored during manufacturing. The technique allows for accurate and reliable quantification of the attributes through image capture technology. The technique has future potential and has important implications for material science, powder rheology, pharmaceutical formulation development, and continual process monitoring. A Novel Image Processing Technique for Weighted Particle Size Distribution Assessment [ABSTRACT FROM AUTHOR]

Business cluster identification is an essential topic for helping understand regional and global supply chains and establishing economic policies and logistics. This work aims to leverage the benefits of self-organizing maps (SOM), combined with traditional clustering algorithms and image processing techniques, to identify business clusters that are described by high-dimensionality feature vectors. It is advantageous over previous work because the algorithm is unsupervised and makes no assumptions about the number of clusters for a given feature set. The proposed algorithm was evaluated using recent datasets for US metropolitan cities from the Indiana Business Research Center (Innovation 2.0) and the Occupational Employment Statistics Survey. Data involving innovation metrics, education levels, economic well-being, connectivity, local GDP, and STEM are aggregated to demonstrate the effectiveness of the proposed neural network. The clustering results are compared to traditional approaches, including K-means clustering, both quantitatively and qualitatively. The unsupervised nature of the proposed SOM approach, and the acceptable computational complexity of the overall algorithm, suggests that self-organizing maps offer several advantages over traditional methods. In this work, we present a novel architecture coupling a SOM model with processing techniques for automatically identifying business clusters derived from high-dimensionality feature vectors, the first use case of SOMs in business cases affecting supply chains and other economic decisions. Preliminary results confirm the viability of architecture as an unsupervised approach for identifying business clusters. [ABSTRACT FROM AUTHOR]

Image preprocessing is important for noise exclusion in order to improve the accuracy of contour detection and image edge extraction. In this paper, a dual quantum spatial filter is proposed in order to reduce statistical noise, image blurring, impulsive noise, and retained details. Non-Maximum-Suppression and instead of manually operating threshold, adaptive thresholding operation is used in the quantum edge detection algorithm to detect more edge information with sharp edges. The circuit of the algorithm is realized through a series of operatives. We have compared PSNR, SSIM, precision rate, accuracy rate, recall rate, error rate, and computational complexity of the algorithm with operator based some existing classical and quantum edge extraction algorithms. The algorithm has the potential to get improved edge continuity, greater sharpness, and extract edge information having less proportion of noise. [ABSTRACT FROM AUTHOR]

This paper emphasizes on detection and classification of Fluoro-Deoxy-Glucose (FDG) radioactivity uptakes in fused Positron Emission Tomography / Computerized Tomography (PET/CT) images automatically. The deep learning technique using Convolutional Neural Network (CNN) is proposed to reduce the complexity in observation, to solve the problem of low accurateness and the time-consuming process of traditional classification methods. The CNN layers are designed and proposed for the FDG uptakes classification problem in fused PET/CT images. The proposed modified CNN model is trained using different optimizers such as Stochastic Gradient Descent Momentum (SGDM), Adaptive Moment Estimation (ADAM), and Root Mean Square propagation (RMSprop). The deep features extracted from the proposed CNN are classified using different classifiers such as K-Nearest Neighbor (KNN), Decision Trees (DT), Ensemble, Naive Bayes (NB), and multi-class Support Vector Machine (SVM) the results of which are compared. The multi-class SVM classifier trained using SGDM optimizer attains the maximum test accuracy of 98.18% and was found to be superior to pre-trained deep models such as AlexNet, ResNet, and GoogleNet. [ABSTRACT FROM AUTHOR]

Multiple kernel learning (MKL) is an efficient way to improve hyperspectral image classification with few training samples by integrating spectral and spatial features. Nonetheless, presenting a MKL algorithm to capture significant information and eliminate redundancy is indispensable. In this paper, a novel graph-based MKL method (GMKL) is introduced to enhance the performance of the MKL algorithm and achieve high discriminative features, resulting in maximum separability. To do so, an informative low-rank graph is determined via a new algorithm called block diagonal representation that takes into account both local and global data structures. Then, in lieu of a time-consuming search for finding the optimal combination of the basic kernels, unlike the prior works, an optimal projective direction is acquired using graph theory. Various basic kernels generated from superpixels and attribute profiles are projected to produce a discriminative combination of kernels. Eventually, the optimum kernel is given to the support vector machine classifier. Experiments have been conducted on two popular hyperspectral datasets (Indian Pines and Pavia University). For the datasets, the proposed algorithm gained 90.43% and 99.28% overall accuracy, respectively, using only 1% training samples, which is much better than the state-of-the-art MKL algorithm and deep learning-based approaches with lower computational time. [ABSTRACT FROM AUTHOR]

The aim of this study is the determination of the best fit ellipsoid to given points by quaternions. The problem of the fitting ellipsoid is frequently encountered in image processing, computer games, medicine, engineering and science applications, geodesy, etc. The ellipsoid fitting problem is the process of determining the ellipsoid that best fits a given set of points in 3D. In the fitting process, it is generally done over two models. The first of these is the algebraic method and the second one is orthogonal (geometric) method. In this study, we tried to solve the problem of algebraic and orthogonal ellipsoid fitting based on Euler angles for the first time over quaternions. The superiority of quaternions over Euler rotation angles is well known. In addition, the variance–covariance matrix of the parameters of the fitted ellipsoid will also be calculated. Numerical applications show that the proposed method can be used successfully. [ABSTRACT FROM AUTHOR]