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Under long-term synergy effect of multi-factors, especially the thermal stress, insulating paper will be degraded and its insulation performance will decline due to carbonization and degradation of cellulose. This paper presents an optical approach for aging feature extraction of the insulating paper, where one of the image processing methods called texture analysis is utilized. By conducting laboratory accelerated thermal aging tests, insulating paper samples with different aging conditions for both Nomex and Kraft, evaluated with the aging time, are prepared. After taking optical microscopic images of insulating paper samples belong to different aging groups, up to 14 texture features are extracted using the gray-level co-occurrence matrix (GLCM). With different feature selection methods applied, several of them are finally selected to represent the aging condition of insulating. Numerical tests with both supervised and unsupervised algorithms, as well as a linear regression method verifies the validity of these features in characterizing the aging condition of the insulating paper. [ABSTRACT FROM PUBLISHER]

We propose an efficient method to model paper tearing in the context of interactive modeling. The method uses geometrical information to automatically detect potential starting points of tears. We further introduce a new hybrid geometrical and physical-based method to compute the trajectory of tears while procedurally synthesizing high resolution details of the tearing path using a texture based approach. The results obtained are compared with real paper and with previous studies on the expected geometric paths of paper that tears. [ABSTRACT FROM AUTHOR]

In this paper, a Shearing Invariant Texture Descriptor (SITD) is proposed, which is a theoretically and computationally simple method based on the Rotation invariant Local Binary Pattern (Rot-LBP) descriptor. In real-world applications using flatbed scanners, such as paper texture fingerprinting, it's common for a sheet of paper to rotate during the image acquisition process. Because the rotation is usually not based on the paper's geometrical centre pivot, the produced image is deformed with irregular rotation resulting in shearing transforms. To tackle the shearing problem, the proposed SITD selects a few patterns from the conventional Rot-LBP to achieve either horizontal or vertical invariance. This paper presents the construction of the SITD operators and their performance in recognizing self-developed and standard image datasets, including real paper texture and Outex images, as well as those with distinctive shapes. The images were distorted with only a shearing transform. The self-developed images were distorted manually, while the standard images were distorted by software. The proposed description method achieved up to 100% correctly recognition rate in all the tested datasets based on the horizontal shear invariant operator. In addition to the accurate performance in all the conducted experiments, the operator significantly outperformed the Rot-LBP and another benchmark method, the Shearing Moment Invariant (SMI). The superiority of the descriptor in recognizing different types of patterns demonstrate its ability to be used in applications where the shearing transform is present. [ABSTRACT FROM AUTHOR]

We present a novel appearance model for paper. Based on our appearance measurements for matte and glossy paper, we find that paper exhibits a combination of subsurface scattering, specular reflection, retroreflection, and surface sheen. Classic microfacet and simple diffuse reflection models cannot simulate the double-sided appearance of a thin layer. Our novel BSDF model matches our measurements for paper and accounts for both reflection and transmission properties. At the core of the BSDF model is a method for converting a multi-layer subsurface scattering model (BSSRDF) into a BSDF, which allows us to retain physically-based absorption and scattering parameters obtained from the measurements. We also introduce a method for computing the amount of light available for subsurface scattering due to transmission through a rough dielectric surface. Our final model accounts for multiple scattering, single scattering, and surface reflection and is capable of rendering paper with varying levels of roughness and glossiness on both sides. [ABSTRACT FROM AUTHOR]

Splicing the shredded paper means the technology that, according the paper, which has been shredded to design a particular Algorithm to splice and recover the original paper. This paper introduced the algorithm of splicing the shredded paper based on the matching to texture feature. By means of this algorithm, we modeled for the problem of splicing the shredded paper and solved it. And, we used the algorithm to splice both pieces of English shredded paper and Chinese shredded paper. The recovered paper had proved the accuracy of the algorithm. [ABSTRACT FROM AUTHOR]

Introduction: Monitoring the leaf area index (LAI), which is directly related to the growth status of rice, helps to optimize and meet the crop's fertilizer requirements for achieving high quality, high yield, and environmental sustainability. The remote sensing technology of the unmanned aerial vehicle (UAV) has great potential in precisionmonitoring applications in agriculture due to its efficient, nondestructive, and rapid characteristics. The spectral information currently widely used is susceptible to the influence of factors such as soil background and canopy structure, leading to low accuracy in estimating the LAI in rice. Methods: In this paper, the RGB andmultispectral images of the critical periodwere acquired through rice field experiments. Based on the remote sensing images above, the spectral indices and texture information of the rice canopy were extracted. Furthermore, the texture information of various images at multiple scales was acquired through resampling, which was utilized to assess the estimation capacity of LAI. Results and discussion: The results showed that the spectral indices (SI) based on RGB and multispectral imagery saturated in the middle and late stages of rice, leading to low accuracy in estimating LAI.Moreover, multiscale texture analysis revealed that the texture of multispectral images derived from the 680 nm band is less affected by resolution, whereas the texture of RGB images is resolution dependent. The fusion of spectral and texture features using random forest and multiple stepwise regression algorithms revealed that the highest accuracy in estimating LAI can be achieved based on SI and texture features (0.48 m) from multispectral imagery. This approach yielded excellent prediction results for both high and low LAI values. With the gradual improvement of satellite image resolution, the results of this study are expected to enable accurate monitoring of rice LAI on a large scale. [ABSTRACT FROM AUTHOR]

The purpose of this article is to investigate techniques for classifying tumor grade from magnetic resonance imaging (MRI). This requires early diagnosis of the brain tumor and its grade. Magnetic resonance imaging may show a clear tumor in the brain, but doctors need to measure the tumor in order to treat more or to advance treatment. For this purpose, digital imaging techniques along with machine learning can help to quickly identify tumors and also treatments and types of surgery. These combined techniques in understanding medical images for researchers are an important tool to increase the accuracy of diagnosis. In this paper,methods used to classify brain tumors, including pre-processing, screening, tissue extraction, and statistical features of the tumor using two types of T1W and Flair brain MRI images and also the method of dimensionality reduction of extracted features and how to train them in classification are also explained. Determine the tumor area using three classification of Fuzzy Logic C-Means Clustering (FCM), Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM). In this paper, simulated and real MRI images are used. The results obtained from the proposed methods in this paper are compared with the reference results and the results show that the proposed approach can increase the reliability of brain tumor diagnosis. [ABSTRACT FROM AUTHOR]

We present stego-texture, a unique texture synthesis method that allows users to deliver personalized messages with beautiful, decorative textures. Our approach was inspired by the success of recent work generating marbling textures using mathematical functions. We were able to transform an input image or a text message into an intricate texture by combining the seven basic, reversible functions provided in the system. Later, the input image or message could be recovered by reversing the process of these functions. During the design process, the parameters of operations were automatically recorded, encrypted and invisibly embedded into the final pattern to create a stego-texture. In this way, the receiver could extract the hidden message from the stego-texture without the need for extra information from the sender. To ensure that the delivered message is unnoticeably covered by the texture, we propose a new technique for automatically creating a background that is harmonious with the message based on a set of visual perception cues. [ABSTRACT FROM AUTHOR]

Small intestinal stromal tumor (SIST) is a common gastrointestinal tumor. Currently, SIST diagnosis relies on clinical radiologists reviewing CT images from medical imaging sensors. However, this method is inefficient and greatly affected by subjective factors. The automatic detection method for stromal tumors based on computer vision technology can better solve these problems. However, in CT images, SIST have different shapes and sizes, blurred edge texture, and little difference from surrounding normal tissues, which to a large extent challenges the use of computer vision technology for the automatic detection of stromal tumors. Furthermore, there are the following issues in the research on the detection and recognition of SIST. After analyzing mainstream target detection models on SIST data, it was discovered that there is an imbalance in the features at different levels during the feature fusion stage of the network model. Therefore, this paper proposes an algorithm, based on the attention balance feature pyramid (ABFP), for detecting SIST with unbalanced feature fusion in the target detection model. By combining weighted multi-level feature maps from the backbone network, the algorithm creates a balanced semantic feature map. Spatial attention and channel attention modules are then introduced to enhance this map. In the feature fusion stage, the algorithm scales the enhanced balanced semantic feature map to the size of each level feature map and enhances the original feature information with the original feature map, effectively addressing the imbalance between deep and shallow features. Consequently, the SIST detection model's detection performance is significantly improved, and the method is highly versatile. Experimental results show that the ABFP method can enhance traditional target detection methods, and is compatible with various models and feature fusion strategies. [ABSTRACT FROM AUTHOR]

The presence of unburned carbon in coal-burning systems undoubtedly causes a loss in the amount of energy that can be obtained from the system, and also reveals an inadequacy in terms of the usability of the ashes. The expensiveness of existing unburned carbon prediction methods is one of the reasons why these technologies cannot be used. This situation requires working on alternative non-combustible carbon technologies. In this paper, a new approach is presented for estimating unburned carbon in a small-scale coal burner system using the Gaussian regression model and CCD camera-acquired flame image. The proposed approach evaluates brightness, fluctuation amplitude, area, and radiation signal properties of the flame image. The proposed non-combustible carbon estimation technique does not require prior knowledge of CCD camera features. In the feature acquisition phase, results were obtained for each natural component of the flame image in RGB colour space separately, in pairs, all together and for three artificial colour channels (grey image). With the proposed method, the unburned carbon estimation was obtained with an accuracy of R = 0.9664 when all colour channels of the RGB image were used together. This result shows that unburned carbon can be estimated from the instantaneous flame images obtained with the CCD camera. [ABSTRACT FROM AUTHOR]

Ultrasound imaging has been a valuable tool in various fields, including medical diagnostics and non-destructive testing. However, there are limitations to ultrasound imaging that have prevented certain applications. These limitations include susceptibility to aberrations, limited processing bandwidth, the diffraction limit, manual decision-making processes, and restricted modalities. Recent advancements in computational resources have allowed for the development of novel methods to address these challenges. This special issue presents nine papers that explore computational ultrasound approaches to overcome these limitations and improve the diagnostic value of ultrasound imaging. The papers cover a range of applications in medical diagnostics, technical research, and basic research. The use of machine learning technologies in ultrasound processing is also highlighted, emphasizing the need for interpretable results and indicators of uncertainty in high-stakes decisions. [Extracted from the article]

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Assistive medical image classifiers can greatly reduce the workload of medical personnel. However, traditional machine learning methods require large amounts of well-labeled data and long learning times to solve medical image classification problems, which can lead to high training costs and poor applicability. To address this problem, a novel unsupervised breast cancer image classification model based on multiscale texture analysis and a dynamic learning strategy for mammograms is proposed in this paper. First, a gray-level cooccurrence matrix and Tamura coarseness are used to transfer images to multiscale texture feature vectors. Then, an unsupervised dynamic learning mechanism is used to classify these vectors. In the simulation experiments with a resolution of 40 pixels, the accuracy, precision, F1-score and AUC of the proposed method reach 91.500%, 92.780%, 91.370%, and 91.500%, respectively. The experimental results show that the proposed method can provide an effective reference for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Multi-image steganography, a technique for concealing information within multiple carrier mediums, finds remote sensing images to be particularly apt carriers due to their complex structures and abundant texture data. These characteristics bolster the resilience against steganalysis and enhance steganographic capacity. The efficacy of multi-image steganography hinges on the diplomatic strategy of cover selection and the meticulous allocation of the payload. Nevertheless, the majority of current methods, which are empirically formulated, predominantly focus on the texture complexity of individual images, thereby potentially undermining overall security. This paper introduces a security-oriented approach to steganographic payload allocation for multiple remote sensing images aimed at fortifying the security of multi-image steganography. Our primary contributions include employing a steganalysis pre-trained network to quantify texture complexity in remote sensing cover images, directly correlating it with security. Additionally, we have developed an adaptive payload allocation strategy for multiple images, which embeds a payload proximate to each image's maximal steganographic capacity while concurrently ensuring the security of the embedding process. Experimental results corroborate that our methodology excels in cover selection and payload allocation and achieves better undetectability against modern steganalysis tools. [ABSTRACT FROM AUTHOR]

Artifacts are the main cause of degradation in CT image quality and diagnostic accuracy. Because of the complex texture of CT images, it is a challenging task to automatically detect artifacts from limited image samples. Recently, graph convolutional networks (GCNs) have achieved great success and shown promising results in medical imaging due to their powerful learning ability. However, GCNs do not take the attention mechanism into consideration. To overcome their limitations, we propose a novel Regional–Temporal Graph Attention Network for motion artifact detection from computed tomography images (RT-GAT). In this paper, head CT images are viewed as a heterogeneous graph by taking regional and temporal information into consideration, and the graph attention network is utilized to extract the features of the constructed graph. Then, the feature vector is input into the classifier to detect the motion artifacts. The experimental results demonstrate that our proposed RT-GAT method outperforms the state-of-the-art methods on a real-world CT dataset. [ABSTRACT FROM AUTHOR]

Background and Objective. Skin cancer is the most common cancer worldwide. One of the most common non-melanoma tumors is basal cell carcinoma (BCC), which accounts for 75% of all skin cancers. There are many benign lesions that can be confused with these types of cancers, leading to unnecessary biopsies. In this paper, a new method to identify the different BCC dermoscopic patterns present in a skin lesion is presented. In addition, this information is applied to classify skin lesions into BCC and non-BCC. Methods. The proposed method combines the information provided by the original dermoscopic image, introduced in a convolutional neural network (CNN), with deep and handcrafted features extracted from color and texture analysis of the image. This color analysis is performed by transforming the image into a uniform color space and into a color appearance model. To demonstrate the validity of the method, a comparison between the classification obtained employing exclusively a CNN with the original image as input and the classification with additional color and texture features is presented. Furthermore, an exhaustive comparison of classification employing different color and texture measures derived from different color spaces is presented. Results. Results show that the classifier with additional color and texture features outperforms a CNN whose input is only the original image. Another important achievement is that a new color cooccurrence matrix, proposed in this paper, improves the results obtained with other texture measures. Finally, sensitivity of 0.99, specificity of 0.94 and accuracy of 0.97 are achieved when lesions are classified into BCC or non-BCC. Conclusions. To the best of our knowledge, this is the first time that a methodology to detect all the possible patterns that can be present in a BCC lesion is proposed. This detection leads to a clinically explainable classification into BCC and non-BCC lesions. In this sense, the classification of the proposed tool is based on the detection of the dermoscopic features that dermatologists employ for their diagnosis. [ABSTRACT FROM AUTHOR]

Correct classification of skin lesions is a key step in skin cancer screening, which requires high accuracy and interpretability. This paper proposes a multimodal method for differentiating various clinical forms of basal cell carcinoma and benign neoplasms that includes machine learning. This study was conducted on 37 neoplasms, including benign neoplasms and five different clinical forms of basal cell carcinoma. The proposed multimodal screening method combines diffuse reflectance spectroscopy, optical coherence tomography and high-frequency ultrasound. Using diffuse reflectance spectroscopy, the coefficients of melanin pigmentation, erythema, hemoglobin content, and the slope coefficient of diffuse reflectance spectroscopy in the wavelength range 650–800 nm were determined. Statistical texture analysis of optical coherence tomography images was used to calculate first- and second-order statistical parameters. The analysis of ultrasound images assessed the shape of the tumor according to parameters such as area, perimeter, roundness and other characteristics. Based on the calculated parameters, a machine learning algorithm was developed to differentiate the various clinical forms of basal cell carcinoma. The proposed algorithm for classifying various forms of basal cell carcinoma and benign neoplasms provided a sensitivity of 70.6 ± 17.3%, specificity of 95.9 ± 2.5%, precision of 72.6 ± 14.2%, F1 score of 71.5 ± 15.6% and mean intersection over union of 57.6 ± 20.1%. Moreover, for differentiating basal cell carcinoma and benign neoplasms without taking into account the clinical form, the method achieved a sensitivity of 89.1 ± 8.0%, specificity of 95.1 ± 0.7%, F1 score of 89.3 ± 3.4% and mean intersection over union of 82.6 ± 10.8%. [ABSTRACT FROM AUTHOR]

The composition statistics of gynecological microscopic images play an important role in the diagnosis and treatment of gynecology. However, current methods mainly rely on manual analysis of microscopic images, which is time-consuming and subjective. Therefore, the purpose of this paper is to develop a gynecological microscopic image component statistics method based on optical medical equipment and medical intelligent system to improve the accuracy and efficiency of gynecological diagnosis. The method of combining optical medical equipment and medical intelligent system is used to realize the automatic analysis of gynecological microscopic images. A large number of gynecological microscopic image samples were collected, and image processing technology was used to preprocess the images. Then computer vision algorithm and machine learning method are used to extract and classify the image features. Finally, the new gynecological microscopic images were analyzed statistically according to the trained model. The experimental results show that the method presented in this paper has a good effect on the composition statistics of gynecological microscopic images. Compared with the traditional manual analysis method, this method can analyze gynecological microscopic images more quickly and accurately, and provide effective diagnostic results. [ABSTRACT FROM AUTHOR]

Sparse view computed tomography (SVCT) aims to reduce the number of X-ray projection views required for reconstructing the cross-sectional image of an object. While SVCT significantly reduces X-ray radiation dose and speeds up scanning, insufficient projection data give rise to issues such as severe streak artifacts and blurring in reconstructed images, thereby impacting the diagnostic accuracy of CT detection. To address this challenge, a dual-domain reconstruction network incorporating multi-level wavelet transform and recurrent convolution is proposed in this paper. The dual-domain network is composed of a sinogram domain network (SDN) and an image domain network (IDN). Multi-level wavelet transform is employed in both IDN and SDN to decompose sinograms and CT images into distinct frequency components, which are then processed through separate network branches to recover detailed information within their respective frequency bands. To capture global textures, artifacts, and shallow features in sinograms and CT images, a recurrent convolution unit (RCU) based on convolutional long and short-term memory (Conv-LSTM) is designed, which can model their long-range dependencies through recurrent calculation. Additionally, a self-attention-based multi-level frequency feature normalization fusion (MFNF) block is proposed to assist in recovering high-frequency components by aggregating low-frequency components. Finally, an edge loss function based on the Laplacian of Gaussian (LoG) is designed as the regularization term for enhancing the recovery of high-frequency edge structures. The experimental results demonstrate the effectiveness of our approach in reducing artifacts and enhancing the reconstruction of intricate structural details across various sparse views and noise levels. Our method excels in both performance and robustness, as evidenced by its superior outcomes in numerous qualitative and quantitative assessments, surpassing contemporary state-of-the-art CNNs or Transformer-based reconstruction methods. [ABSTRACT FROM AUTHOR]

Sustainable agricultural practices help to manage and use natural resources efficiently. Due to global climate and geospatial land design, soil texture, soil–water content (SWC), and other parameters vary greatly; thus, real time, robust, and accurate soil analytical measurements are difficult to be developed. Conventional statistical analysis tools take longer to analyze and interpret data, which may have delayed a crucial decision. Therefore, this review paper is presented to develop the researcher's insight toward robust, accurate, and quick soil analysis using artificial intelligence (AI), deep learning (DL), and machine learning (ML) platforms to attain robustness in SWC and soil texture analysis. Machine learning algorithms, such as random forests, support vector machines, and neural networks, can be employed to develop predictive models based on available soil data and auxiliary environmental variables. Geostatistical techniques, including kriging and co-kriging, help interpolate and extrapolate soil property values to unsampled locations, improving the spatial representation of the data set. The false positivity in SWC results and bugs in advanced detection techniques are also evaluated, which may lead to wrong agricultural practices. Moreover, the advantages of AI data processing over general statistical analysis for robust and noise-free results have also been discussed in light of smart irrigation technologies. Conclusively, the conventional statistical tools for SWCs and soil texture analysis are not enough to practice and manage ergonomic land management. The broader geospatial non-numeric data are more suitable for AI processing that may soon help soil scientists develop a global SWC database. [ABSTRACT FROM AUTHOR]

The analysis of textures is an important task in image processing and computer vision because it provides significant data for image retrieval, synthesis, segmentation, and classification. Automatic texture recognition is difficult, however, and necessitates advanced computational techniques due to the complexity and diversity of natural textures. This paper presents a method for classifying textures using graphs; specifically, natural and horizontal visibility graphs. The related image natural visibility graph (INVG) and image horizontal visibility graph (IHVG) are used to obtain features for classifying textures. These features are the clustering coefficient and the degree distribution. The suggested outcomes show that the aforementioned technique outperforms traditional ones and even comes close to matching the performance of convolutional neural networks (CNNs). Classifiers such as the support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) are utilized for the categorization. The suggested method is tested on well-known image datasets like the Brodatz texture and the Salzburg texture image (STex) datasets. The results are positive, showing the potential of graph methods for texture classification. [ABSTRACT FROM AUTHOR]

As one of fundamental texture classification methods, LBP-based descriptors have attracted considerable attention due to the efficiency, simplicity, and high performance. However, most of binary pattern methods cannot effectively capture the texture information with scale changes. Inspired by this, this paper proposes a multi-scale threshold integration encoding strategy for texture classification. The essence of this strategy is to introduce the multi-scale local texture information in the view of thresholding. Based on this, we propose the local multi-scale center pattern, local multi-scale sign pattern, and local multi-scale magnitude pattern to extract and describe the multi-scale local texture information. Then, the three sub-patterns are jointly combined to generate the final descriptor for texture classification tasks. The experimental results on three popular texture databases significantly demonstrate that the proposed texture descriptor is very discriminative and powerful for visual texture classification tasks. [ABSTRACT FROM AUTHOR]

Image inpainting is an effective method to repair damaged image. In this paper, we mainly studied some typical image inpainting models and analyzed these corresponding repair qualities. Firstly, we studied three typical algorithms based on PDE (Partial Differential Equation) model to repair images whose damaged areas are relative small. And then we studied the image inpainting algorithm based on texture synthesis. This kind of inpainting algorithm is suitable for image with large damaged area. Finally, we studied Criminisi inpainting algorithm based on image segmentation in detail, on which we proposed an improved priority formula image repair algorithm, the simulation results indicate that the improved algorithm of this paper showed better performance in repair quality from both subjective and objective aspects. [ABSTRACT FROM AUTHOR]

Since two years ago, the COVID-19 virus has spread strongly in the world and has killed more than 6 million people directly and has affected the lives of more than 500 million people. Early diagnosis of the virus can help to break the chain of transmission and reduce the death rate. In most cases, the virus spreads in the infected person's chest. Therefore, the analysis of a chest CT scan is one of the most efficient methods for diagnosing a patient. Until now, various methods have been presented to diagnose COVID-19 disease in chest CT-scan images. Most recent studies have proposed deep learning-based methods. But handcrafted features provide acceptable results in some studies too. In this paper, an innovative approach is proposed based on the combination of low-level and deep features. First of all, local neighborhood difference patterns are performed to extract handcrafted texture features. Next, deep features are extracted using MobileNetV2. Finally, a two-level decision-making algorithm is performed to improve the detection rate especially when the proposed decisions based on the two different feature set are not the same. The proposed approach is evaluated on a collected dataset of chest CT scan images from June 1, 2021, to December 20, 2021, of 238 cases in two groups of patient and healthy in different COVID-19 variants. The results show that the combination of texture and deep features can provide better performance than using each feature set separately. Results demonstrate that the proposed approach provides higher accuracy in comparison with some state-of-the-art methods in this scope. [ABSTRACT FROM AUTHOR]

Our world consists not only of objects and scenes but also of materials of various kinds. Being able to recognize the materials that surround us (e.g., plastic, glass, concrete) is important for humans as well as for computer vision systems. Unfortunately, materials have received little attention in the visual recognition literature, and very few computer vision systems have been designed specifically to recognize materials. In this paper, we present a system for recognizing material categories from single images. We propose a set of low and mid-level image features that are based on studies of human material recognition, and we combine these features using an SVM classifier. Our system outperforms a state-of-the-art system (Varma and Zisserman, TPAMI 31(11):2032-2047, ) on a challenging database of real-world material categories (Sharan et al., J Vis 9(8):784-784a, ). When the performance of our system is compared directly to that of human observers, humans outperform our system quite easily. However, when we account for the local nature of our image features and the surface properties they measure (e.g., color, texture, local shape), our system rivals human performance. We suggest that future progress in material recognition will come from: (1) a deeper understanding of the role of non-local surface properties (e.g., extended highlights, object identity); and (2) efforts to model such non-local surface properties in images. [ABSTRACT FROM AUTHOR]

The proposed model in "Self-assessment and deep learning-based coronavirus detection and medical diagnosis systems for healthcare", improves the medical image classification and optimization for better disease diagnosis. The manuscript entitled "Deep learning on digital mammography for expert-level diagnosis accuracy in breast cancer detection", proposes a breast cancer detection system using deep learning. The results obtained within the scope of the study indicate that diversifying X-ray images with tissue analysis methods in the diagnosis of COVID-19 and including CNN input provides significant improvements in the results. Text, radiological pictures, audio notes, video, and other types of multimedia healthcare data are all generated by today's smart healthcare system [[1]]. [Extracted from the article]

The objective of this paper is to develop a Non Destructive Testing (NDT) method for the detection and classification of defects in composite materials at a micro level and to devise methodologies to analyse the corrosion resistance behavior using Scanned Electron Microscope (SEM) imagery. The defects on the Stainless Steel – Molybdenum (SS-Mo) Nanocomposite coating is estimated from their Scanning Electron Micrographs by using Image Processing algorithms. For this, the SS-Mo Nano Composite coatings are fabricated using a DC magnetron sputtering process using an indigenously prepared sputtering target. Depositions are carried out on Glass substrate for the evaluation of structural, morphological, chemical composition and corrosion resistance of the coatings prepared under different conditions (deposition of SS at 300°C and RT (Room Temperature); deposition of SS + Mo at 300°C and RT). The structural and compositional analysis performed with X-ray Diffraction (XRD) and Energy-Dispersive X-ray spectroscopy (EDX) has confirmed the formation of Stainless Steel Molybdenum Composite, when the deposition is at 300°C. The SS-Mo composite deposited at 300°C is also observed to yield high corrosion resistance of the order 0.058 mm/year. A novel texture – morphology based image feature descriptor has been proposed for corrosion resistance to evaluate the composite material in a Non-destructive manner. The analysis of SEM image of the developed coatings using the proposed feature along with machine learning algorithm reveals the superior property for SS-Mo coatings deposited at 300°C which is also demonstrated by the laboratory experiments. [ABSTRACT FROM AUTHOR]

The goal of Super-Resolution (SR) is to generate a plausible and visually pleasing High-Resolution (HR) image from a degenerate Low-Resolution (LR) image. High-resolution medical images help improve the accuracy of subsequent operations such as segmentation or remote diagnosis. In this paper, we propose a novel learning-based medical image SR method which directly establishes a bi-directional projection for each texture pattern (texture structures which appear periodically in images) in HR and LR domains. In particular, considering that the feature extraction method should be closely combined with the subsequent steps and the loss of detail is different when different texture patterns degenerate, we propose an auxiliary network to extract features and generate representative texture patterns simultaneously. Then, the textural dictionary is constructed for each texture pattern by introducing texture complexity prior. Hence, the dictionary contains more information when the corresponding texture pattern is complex. Finally, the projections are calculated on these dictionaries with low rank constraint. Extensive experimental results indicate that the proposed method is effectiveness and can deliver higher quality of SR results. [ABSTRACT FROM AUTHOR]

Ice aprons are defined as very small ice bodies covering steep rock slopes. They have only been the subject of increased scientific interest for a few years, despite the fact that they are a condition for mountaineering and obvious elements in the high-alpine landscapes. However, very little is known about their distribution, evolution and physical characteristics. In this paper, we review the existing knowledge on ice aprons, which have almost exclusively been investigated in the Mont-Blanc massif, Western Alps. We supplement this review with novel results from recent surveys of ice aprons. We used a wide array of methodologies, from remote sensing (multi-source imagery) to in situ (stakes and thermal monitoring) and laboratory (radiocarbon dating and texture analysis) glaciological investigations. In the Mont-Blanc massif, ice aprons occupy 4.2 km2 within the alpine permafrost zone. Temperature measured at the ice–rock interface is indeed largely negative. Thinness of ice aprons coupled with the cold context implies a quasi-stationary shear regime without basal Sliding. Only ice at the surface can possibly melt in warm periods. After a shrinking period from the end of the Little Ice Age to the mid-to-late-1960s, ice aprons experienced a short period of expansion, followed by an accelerated shrinkage since the beginning of the 21st century. This shrinkage now favours rockfall triggering and poses a serious threat to a glaciological heritage since ice aprons host several-thousand-year-old ice. Finally, we synthesize this information to assess the existing definition of ice aprons, and propose some future research directions. [ABSTRACT FROM AUTHOR]

In medical research and clinical applications, the utilization of MRI datasets from multiple centers has become increasingly prevalent. However, inherent variability between these centers presents challenges due to domain shift, which can impact the quality and reliability of the analysis. Regrettably, the absence of adequate tools for domain shift analysis hinders the development and validation of domain adaptation and harmonization techniques. To address this issue, this paper presents a novel Domain Shift analyzer for MRI (DSMRI) framework designed explicitly for domain shift analysis in multi-center MRI datasets. The proposed model assesses the degree of domain shift within an MRI dataset by leveraging various MRI-quality-related metrics derived from the spatial domain. DSMRI also incorporates features from the frequency domain to capture low- and high-frequency information about the image. It further includes the wavelet domain features by effectively measuring the sparsity and energy present in the wavelet coefficients. Furthermore, DSMRI introduces several texture features, thereby enhancing the robustness of the domain shift analysis process. The proposed framework includes visualization techniques such as t-SNE and UMAP to demonstrate that similar data are grouped closely while dissimilar data are in separate clusters. Additionally, quantitative analysis is used to measure the domain shift distance, domain classification accuracy, and the ranking of significant features. The effectiveness of the proposed approach is demonstrated using experimental evaluations on seven large-scale multi-site neuroimaging datasets. [ABSTRACT FROM AUTHOR]

The binary pattern family has drawn wide attention for texture representation due to its promising performance and simple operation. However, most binary pattern methods focus on local neighborhoods but ignore center pixels. Even if some studies introduce the center based sub-pattern to provide complementary information, existing center based sub-patterns are much weaker than other local neighborhood based subpatterns. This severe unbalance limits the classification performance of fusion features significantly. To alleviate this problem, this paper designs a multiple threshold center pattern (MTCP) to provide a more discriminative and complementary local texture representation with a compact form. First, a multiple threshold encoding strategy is designed to encode the center pixel that generates three 1-bit binary patterns. Second, it adopts a compact multi-pattern encoding strategy to combine them into a 3-bit MTCP. Furthermore, this paper proposes a completed multiple threshold encoding pattern by fusing the MTCP, local sign pattern, and local magnitude pattern. Comprehensive experimental evaluations on three popular texture classification benchmarks confirm that the completed multiple threshold encoding pattern achieves superior texture classification performance. [ABSTRACT FROM AUTHOR]

This paper presents a deep learning approach to identify and classify various defects in the laser-directed energy manufactured components. It mainly focuses on the Convolutional Neural Network (CNN) architectures, such as VGG16, AlexNet, GoogLeNet and ResNet to perform the automated classification of defects. The main objectives of this research are to manufacture components using the laser-directed energy deposition process, prepare a dataset of horizontal wall structure, vertical wall structure and cuboid structure with three defective classes such as voids, flash formation, and rough textures, and one non-defective class, use this dataset with a deep learning algorithm to classify the defect and use the efficient algorithm to detect defects. The next objective is to compare the performance parameters of VGG16, AlexNet, GoogLeNet and ResNet used for classifying defects. It has been observed that the best results were obtained when the VGG16 architecture was applied to an augmented dataset. With augmentation, the VGG16 architecture gave a test accuracy of 94.7% and a precision of 80.0%. The recall value is 89.3% and an F1-Score is 89.5%. The VGG16 architecture with augmentation is highly reliable for automating the defect detection process and classifying defects in the laser additive manufactured components. [ABSTRACT FROM AUTHOR]

Texture analysis plays an important role in many image processing applications to describe the objects. On the other hand, visual Fabric Defect Detection (FDD) is a highly research field in the computer vision. Surface defect refers to abnormalities in the texture of the surface. So, in this paper a dual purpose benchmark dataset is proposed for texture image analysis and surface defect detection. The first framework is based Segmentation with Contrast Limited Histogram Equalization (CLAHE) and finally FE for Classification (SCFC). The SCFC depends on improvement using CLAHE in addition to pre-processing followed by segmentation by OT and finally FE for classification task. The second scheme is relied on merging the features of A Trous Algorithm with Homomorphic Method (HM) (AH) following by Segmentation and Feature Extraction (FE) for Classification (AHSFC). The AHSFC depends on enhancement using AH in addition to pre-processing followed by segmentation using Optimum Global Thresholding (OT) and finally FE for the detection or classification task. The performance quality metrics for the suggested techniques are entropy, average gradient, contrast, Sobel edge magnitude, sensitivity, specificity, precision, accuracy and identification time.Simulation results prove that the success of both techniques in detecting the FDD. By comparing the first and the second presented algorithms, it is clear that the second suggested technique gives superior for the FDD the clothing. [ABSTRACT FROM AUTHOR]

This paper investigates deep learning (DL)-based semantic segmentation of textured mosaics. Existing popular datasets for mosaic texture segmentation, designed prior to the DL era, have several limitations: (1) training images are single-textured and thus differ from the multi-textured test images; (2) training and test textures are typically cut out from the same raw images, which may hinder model generalization; (3) each test image has its own limited set of training images, thus forcing an inefficient training of one model per test image from few data. We propose two texture segmentation datasets, based on the existing Outex and DTD datasets, that are suitable for training semantic segmentation networks and that address the above limitations: SemSegOutex focuses on materials acquired under controlled conditions, and SemSegDTD focuses on visual attributes of textures acquired in the wild. We also generate a synthetic version of SemSegOutex via texture synthesis that can be used in the same way as standard random data augmentation. Finally, we study the performance of the state-of-the-art DeepLabv3+ for textured mosaic segmentation, which is excellent for SemSegOutex and variable for SemSegDTD. Our datasets allow us to analyze results according to the type of material, visual attributes, various image acquisition artifacts, and natural versus synthetic aspects, yielding new insights into the possible usage of recent DL technologies for texture analysis. Article highlights: We propose two texture segmentation datasets that address the limitations of existing texture segmentation datasets. Experiments with materials and attributes shed a new light on recent deep learning technologies for texture analysis. Our results also suggest that synthetic textures can be used for data augmentation to improve segmentation results. [ABSTRACT FROM AUTHOR]

The early hepatic fibrosis staging is very important for timely diagnosis, prognosis, and treatment of all chronic liver diseases. Diffraction-enhanced imaging, which can provide much more information on soft tissue morphology than conventional absorption radiography, might be a potential noninvasive technique to diagnose and stage hepatic fibrosis. This paper presents different feature extraction strategies and classification methods to automatically classify hepatic fibrosis using diffraction-enhanced imaging images. Texture features are obtained using a total of three methods including first order feature, gray level co-occurrence matrix, and grayscale gradient co-occurrence matrix. The fusion of these texture features is also studied. The principal component analysis is used to reduce the dimension of the features and redundant information among data. The features are classified using two popular classification techniques, namely, K-nearest neighbors and support vector machines. On the basis of the comparison of different feature strategies and classification methods, we can identify the suitable methods for grading hepatic fibrosis. The proposed approach efficiently classifies the hepatic fibrosis DEI images into four classes with the highest classification accuracy of 99.99%. We further demonstrate the potential of the DEI images in staging hepatic fibrosis. [ABSTRACT FROM AUTHOR]

Blind noise-level estimation (NLE) is a fundamental issue in digital image processing. This paper provides a noise-level estimator for additive white Gaussian noise and multiplicative Gaussian noise using principal texture patches (PTPs). The proposed algorithm first identifies the principal texture of the noisy image by using the principal component analysis, and then, it chooses PTPs to calculate the noise level. The major contributions of this paper toward addressing the challenges in the NLE literature include: 1) analyzing the nonlinear relationship between the smallest eigenvalue, true noise level, number of chosen patches, and block size; 2) extracting PTPs; and 3) computing noise level using PTPs. The abundant experimental results show that the proposed method works well for various natural images over a large range of noise levels and it performs well for multiplicative noise. Compared with some state-of-the-art approaches, our method has the best performance and a faster running speed. [ABSTRACT FROM AUTHOR]

Series machines, i.e., machines (which are usually unreliable) arranged in series with no buffering, are pervasive in production systems. In the analysis, design, and optimization of the series-machine system, the efficiency analysis is one of the most fundamental issues. There are not a lot of researches analyzing the efficiency of the series-machine system, and almost all of them assume that the system operates under type-I failure mechanisms (i.e., the breakdown of a machine could make all other series machines forced down) rather than under type-II mechanisms (i.e., the breakdown of a machine does not make any other series machines forced down). The reason that the type-I failure mechanisms are usually assumed in the literature is that the analysis of the series-machine system under type-II mechanisms is much more complex than under type-I mechanisms, although type-II mechanisms are more common in practice. To thoroughly and systematically estimate the efficiency of the series-machine system, in this paper, we propose a unified analytical approach to investigate the efficiency under both type-I and type-II failure mechanisms. Both cases of deterministic and random cycle times are considered. Different from under type-I failure mechanisms, analytical expressions of the efficiency of series-machine systems under type-II failure mechanisms are extremely hard to obtain, and thus, limit bounds of the efficiency are derived and algorithms are developed to calculate its exact value. Results show that the series-machine system under type-II failure mechanisms is more efficient than under type-I mechanisms, which, intuitively making sense, is the reason that type-II mechanisms are more common in the industry. Note to Practitioners—The series-machine system is pervasive in industry and it usually operates under type-II failure mechanisms (i.e., the breakdown of a machine does not make any other series machines forced down). Nevertheless, the existing research on type-II failure mechanisms is very limited. Thus, the efficiency and other performance measures of such systems are, in general, approximately evaluated based on type-I mechanisms (i.e., the breakdown of a machine could make all other series machines forced down). However, the error of this approximation may be large and unacceptable. In this paper, a unified approach is proposed to analyze the efficiency of the series-machine system. Due to the difficulties in deriving closed-form expressions under type-II failure mechanisms, algorithms are developed to calculate the efficiency of the series-machine system. Theoretical results ensure the effectiveness of the algorithms. [ABSTRACT FROM AUTHOR]

Biometric recognition technology based on eye-movement dynamics has been in development for more than ten years. Different visual tasks, feature extraction and feature recognition methods are proposed to improve the performance of eye movement biometric system. However, the correct identification and verification rates, especially in long-term experiments, as well as the effects of visual tasks and eye trackers’ temporal and spatial resolution are still the foremost considerations in eye movement biometrics. With a focus on these issues, we proposed a new visual searching task for eye movement data collection and a new class of eye movement features for biometric recognition. In order to demonstrate the improvement of this visual searching task being used in eye movement biometrics, three other eye movement feature extraction methods were also tested on our eye movement datasets. Compared with the original results, all three methods yielded better results as expected. In addition, the biometric performance of these four feature extraction methods was also compared using the equal error rate (EER) and Rank-1 identification rate (Rank-1 IR), and the texture features introduced in this paper were ultimately shown to offer some advantages with regard to long-term stability and robustness over time and spatial precision. Finally, the results of different combinations of these methods with a score-level fusion method indicated that multi-biometric methods perform better in most cases. [ABSTRACT FROM AUTHOR]

Different kinds of illustrations and artistic imagery can be generated or simulated through the nonphotorealistic rendering (NPR) technique. However, designing and simulating new NPR artistic styles remains extremely challenging. Chalk art style is a very famous artistic work all over the world, and few algorithms have been put forward to illustrate this style. This paper presents a novel NPR technique which generates a chalk art drawing from a 2D photograph automatically. We aim at obtaining a set of lines surface with coarse appearance and generating stroke textures of the real chalk painting. Firstly, the edge of the source image is extracted by difference-of-Gaussian filter method. To simulate chalk painting's lines, image diffusion and enhancement techniques are proposed to produce coarse and rough lines. Secondly, we developed an improved line integral convolution and dilation operation methods to produce the chalk stroke texture. Finally, the edge image, stroke texture image and color image will be mapped to another background image to generate the chalk art drawing. Experimental results are presented to show the effectiveness of our method in producing the color chalk stylistic illustrations, and the methods can simulate the characters of the real chalk art painting. The proposed method of this paper will enlarge the research and application fields of NPR. Meanwhile, it provides a tool for the user to create chalk art paintings via computers even without painting skill. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a statistical model that predicts the CPX-Level at 80 km/h of porous and semi-porous mixes as a function of porosity and texture band levels. The model was calibrated using data from six pavement sections laid on Italian motorway corridors, two paved with open-graded mixes and four with semi-open-graded mixes. The parameter representative of porosity is the ratio between the voids in the compacted mix (Va) and the Voids in Mineral Aggregate (VMA). The texture band levels were defined upon a statistical analysis comprising the correlation coefficient, the Mallows coefficient and the analysis of variance. The results show that the main texture band levels involved belong to the range of macrotexture and that the model provides adequate accuracy, which improves when the equation is used with average values along the test sections of 200 metres. [ABSTRACT FROM AUTHOR]

Sessile drying droplets manifest distinct morphological patterns, encompassing diverse systems, viz., DNA, proteins, blood, and protein-liquid crystal (LC) complexes. This study employs an integrated methodology that combines drying droplet, image texture analysis (features from First Order Statistics, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, and Gray Level Dependence Matrix), and statistical data analysis (Generalized Additive Modeling and K-means clustering). It provides a comprehensive qualitative and quantitative exploration by examining LC-protein droplets at varying initial phosphate buffered concentrations (0x, 0.25x, 0.5x, 0.75x, and 1x) during the drying process under optical microscopy with crossed polarizing configuration. Notably, it unveils distinct LC-protein textures across three drying stages: initial, middle, and final. The Generalized Additive Modeling (GAM) reveals that all the features significantly contribute to differentiating LC-protein droplets. Integrating the K-means clustering method with GAM analysis elucidates how textures evolve through the three drying stages compared to the entire drying process. Notably, the final drying stage stands out with well-defined, non-overlapping clusters, supporting the visual observations of unique LC textures. Furthermore, this paper contributes valuable insights, showcasing the efficacy of drying droplets as a rapid and straightforward tool for characterizing and classifying dynamic LC textures. [ABSTRACT FROM AUTHOR]

Image segmentation is an important part of the standard digital rock physics (DRP) workflows. In this paper, we present ground-truthing of digital rock images using texture analysis. We propose a deep learning–based approach for automated segmentation which is validated using the extracted ground-truth. To generate the ground-truth, we utilize the a priori knowledge of minerals and pores. We perform texture based iterative intensity thresholding to calculate the porosity and mineral composition. The ground-truth is generated for the berea sandstone image dataset and the grosmont carbonate image dataset. Results obtained from the experiments conform to the porosity and composition ranges provided in the literature. For the purpose of automated segmentation, we use a SegNet deep network which performs pixel-wise segmentation of digital rock images into pores and minerals. We train and test the SegNet using 1024 images of berea sandstone dataset ground-truthed using iterative intensity thresholding. We utilize a split of 90% and 10% for training and testing, respectively. Experimental outcomes show that the SegNet-based segmentation results for the minerals such as clay, ankerite, and zircon are better than the existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we mathematically analyze one of the most challenging issues in color image-guided depth upsampling: the texture copy artifacts. The optimal guidance weights denoted by balanced weights are proposed to best suppress texture copy artifacts. To both suppress texture copy artifacts and preserve depth discontinuities, a new general weighting scheme called variable bandwidth weighting is proposed. The variable bandwidth weighting scheme is able to adjust guidance weights according to the local depth smoothness. A new concept called relative smoothness is proposed for measuring the local depth smoothness. Given this quantitative smoothness measurement, the proposed weighting scheme can adaptively adjust the bandwidth for calculating the guidance weights in the existing methods. As we use the computationally efficient balanced weights instead of the guidance weights of a large bandwidth, the proposed method can speed up the upsampling process for about $2\times \sim 5\times $ when compared with the original upsampling methods. Experimental results show the effectiveness and efficiency of the proposed method in suppressing texture copy artifacts, preserving the depth discontinuities and reducing the computational cost at the same time. [ABSTRACT FROM PUBLISHER]

This Texture classification plays an important role in many areas such as remote sensing, construction filed, medical imaging. The present paper proposes an innovative technique that classifies stone texture into four categories i.e. Brick, Marble, Granite and Mosaic based on the Surface Patterns (SP). Surface Pattern of the stone image is represented using Decreased Dimension and Reduced Gray level Range Matrix (DDRGRM) model. The DDRGRM model reduces the dimension of the image from 5x5 into 2x2 and also reduces the gray level range by preserving the significant feature information. The proposed technique focuses on the Surface Pattern (SP) which appears on DDRGRM image i.e. the patterns formed by Bezier curve (BC), U, V and T patterns. Hence, this model is termed as Surface Patterns on DDRGRM (SP-DDRGRM). The SPs on DDRGRM model of the stone texture are measured on a 5x5 sub image, because the surface patterns can be better viewed and they are difficult to fit in to 3x3 window. Based on the surface patterns count, the present paper develops a user defined algorithm for efficient classification that classifies stone image into 4 groups i.e. bricks, marble, granite and mosaic. The experimental analysis gives a clear idea about the features extracted in stone images by using SP-DDRGRM model which is suitable for both categories of classification algorithms i.e. standard algorithms like k-NNC, ID3 and SVM etc., and user defined algorithms. The experimental results indicate that proposed technique suits well for both standard and user-defined algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a new formula is proposed that uses local entropy for texture feature extraction. This new method is similar to entropy; however, it calculates the local entropy of each local patch of textures. Entropy (ENT) is an attribute that measures the randomness of gray-level distribution of image. Entropy extracts dissimilarity of each local patch. In this paper, local entropy is compared to Local Binary Pattern (LBP) and local variance (VAR). All of these descriptors are rotation invariant and are used for extracting the features from each local neighborhood of textures. In spite of low accuracy of VAR and LBP the performance of ENT does not decrease significantly for noisy textures. In other words, ENT is more robust to noise than VAR and LBP. Implementations on Outex, UIUC, CUReT and MeasTex datasets show that entropy is more accurate than variance and LBP. Similar to VAR and LBP, ENT can be combined with other descriptors to improve the performance of classification. For almost all datasets that are used in implementation part, LBP/ENT is more accurate than LBP/VAR for normal and noisy textures. Also the ENT accuracy outperforms the accuracy of VAR and LBP and most of the advanced noise robust LBP versions for low Signal to Noise Ratio (SNR) values (SNR < 10). ENT feature is a continuous value so it is necessary to quantize to discrete value for histogram. The quantization and train step of ENT is the same as VAR. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to propose a color-texture classification approach which uses color sensor information and texture features jointly. High accuracy, low noise sensitivity and low computational complexity are specified aims for this proposed approach.Design/methodology/approach One of the efficient texture analysis operations is local binary patterns (LBP). The proposed approach includes two steps. First, a noise resistant version of color LBP is proposed to decrease its sensitivity to noise. This step is evaluated based on combination of color sensor information using AND operation. In a second step, a significant points selection algorithm is proposed to select significant LBPs. This phase decreases final computational complexity along with increasing accuracy rate.Findings The proposed approach is evaluated using Vistex, Outex and KTH-TIPS-2a data sets. This approach has been compared with some state-of-the-art methods. It is experimentally demonstrated that the proposed approach achieves the highest accuracy. In two other experiments, results show low noise sensitivity and low computational complexity of the proposed approach in comparison with previous versions of LBP. Rotation invariant, multi-resolution and general usability are other advantages of our proposed approach.Originality/value In the present paper, a new version of LBP is proposed originally, which is called hybrid color local binary patterns (HCLBP). HCLBP can be used in many image processing applications to extract color/texture features jointly. Also, a significant point selection algorithm is proposed for the first time to select key points of images. [ABSTRACT FROM AUTHOR]

The paper proposes an interactive algorithm for a three-dimensional (3D) surface that can substantially improve its cracking detection performance by adjustments based on the observer's feedback. Since the correct response to the observer's feedback is critical to the performance of an interactive algorithm, the paper presents a series of novel methodologies to accomplish the interaction between the detection system and the observer with a high level of success. A computer program is developed to establish the interactive detection system and implement two levels of detection. The bottom level is automated and can be used to find the majority of cracking. The top level is assisted by the users and can be applied for semi-automated refinement. In particular, the top level is designated to find missed cracks or delete noises within user-defined regions. The techniques of precision recall and confusion matrix are used to determine effectiveness of the developed system of algorithms. The results from the case study on a highly cracked pavement segment indicate that the automated level of the proposed interactive system can achieve high detection accuracy with appropriate training, and the assisted level can retrieve nearly all cracks missed by the automated level and delete almost all noises from the processing using the automated level. [ABSTRACT FROM AUTHOR]

A stain defect is difficult to detect with the human eye because of its characteristic of having a very minimal difference in brightness with the local area of the surface. Recently, with the development of Deep learning, the Convolutional Neural Network (CNN) based stain defect classification method has been proposed. This paper proposes a Dual-stream CNN for stain defect classification using a Gabor filter image. Using Dual-stream structure CNN, Gabor filter images and Gray image (Original) preserve their respective features. The experiment based on the Magnetic Tile (MT) stain data set and the Compact Camera Module (CCM) stain dataset confirms that the proposed method has an improved performance based on the precision, recall, and F1-score in comparison to the Single-stream extraction-based method. Gabor filter images have an advantage in image texture analysis and can be used as an input to the CNNs. The Dual-stream structure better extracts the features needed for classification. [ABSTRACT FROM AUTHOR]

Aiming at the problems of long time, high cost, invasive sampling damage, and easy emergence of drug resistance in lung cancer gene detection, a reliable and non-invasive prognostic method is proposed. Under the guidance of weakly supervised learning, deep metric learning and graph clustering methods are used to learn higher-level abstract features in CT imaging features. The unlabeled data is dynamically updated through the k-nearest label update strategy, and the unlabeled data is transformed into weak label data and continue to update the process of strong label data to optimize the clustering results and establish a classification model for predicting new subtypes of lung cancer imaging. Five imaging subtypes are confirmed on the lung cancer dataset containing CT, clinical and genetic information downloaded from the TCIA lung cancer database. The successful establishment of the new model has a significant accuracy rate for subtype classification (ACC = 0.9793), and the use of CT sequence images, gene expression, DNA methylation and gene mutation data from the cooperative hospital in Shanxi Province proves the biomedical value of this method. The proposed method also can comprehensively evaluate intratumoral heterogeneity based on the correlation between the final lung CT imaging features and specific molecular subtypes. [ABSTRACT FROM AUTHOR]

Forest above-ground biomass (AGB) is an excellent indicator for the health status and carbon sink potential of forest ecosystems, as well as the effectiveness of sustainable forest management practices. However, due to the strong heterogeneity of forest structures, acquiring high-accuracy and high-resolution AGB distributions over wide regions is often prohibitively expensive. To fill the resulting gap, this paper uses part of Lishui city, Zhejiang province as the study area, based on 168 forest sample observations, and proposes a novel integrated framework that combines a multi-scale geographically weighted regression (MGWR) with the co-kriging algorithm to refine the spatial downscaling of AGB. Specifically, optimal predictor variable sets identified by random forest importance ranking, multiple stepwise regression, and Pearson VIF methods were first assessed based on their total explanatory power (R square), followed by reconfirmation of the optimal predictor variable set based on the non-stationarity impact of each variable's action scale (bandwidth) on the output pattern of AGB downscaling. The AGB downscaling statistical algorithms included MGWR, GWR, random forest (RF), and the ordinary least square (OLS), and their downscaling performances were quantitatively compared to determine the best downscaling method. Ultimately, the downscaled AGB pattern was produced using the best method, which was further refined by considering the spatial autocorrelation in AGB samples by implementing a co-kriging interpolation analysis of the predicted AGB downscaling residuals. The results indicated that the variable set selected by random forest importance ranking had the strongest explanatory power, with a validation R square of 0.58. This was further confirmed by the MGWR analysis which showed that the set of variables produced a more spatially smooth downscaled AGB pattern. Among the set of optimal variables, elevation and aspect affected AGB at local scales, representing a strong spatial heterogeneity. Some textural features and spectral features showed a smooth action scale relative to AGB, showing insignificant spatial scale processes. In the study area with complex terrain, using aspect as a covariant, the co-kriging (CK) model achieved a higher simulation accuracy for the MGWR-predicted AGB residuals than the ordinary kriging model. Overall, the proposed MGWR-CK model, with a final validation R square value of 0.62, effectively improved the spatial distribution characteristics and textural details of AGB mapping without the additional costs of procuring finer satellite images and GIS-based features. This will contribute to the accurate assessment of carbon sinks and carbon stock changes in subtropical forest ecosystems globally. [ABSTRACT FROM AUTHOR]