Search Results (46)

Proximate analysis, including ash, volatile matter, moisture, fixed carbon, and calorific value, is a fundamental aspect of fuel testing and serves as the primary method for evaluating coal quality, which is critical for the processing and utilization of coal. The traditional analytical methods involve time-consuming and costly combustion processes, particularly when applied to large volumes of coal that need to be sampled in massive batches. Hyperspectral imaging is promising for the rapid and nondestructive determination of coal quality indices. In this study, a fast and nondestructive coal proximate analysis method with combined spectral-spatial features was developed using a hyperspectral imaging system in the 450–2500 nm range. The processed spectra were evaluated using PLSR, with the most effective MSC spectra selected. To reduce the spectral redundancy and improve the accuracy, the SPA, Boruta, iVISSA, and CARS algorithms were adopted to extract the characteristic wavelengths, and 16 prediction models were constructed and optimized based on the PLSR, RF, BPNN, and LSSVR algorithms within the Optuna framework for each quality indicator. For spatial information, the histogram statistics, gray-level covariance matrix, and Gabor filters were employed to extract the texture features within the characteristic wavelengths. The texture feature-based and combined spectral-texture feature-based prediction models were constructed by applying the spectral modeling strategy, respectively. Compared with the models based on spectral or texture features only, the LSSVR models with combined spectral-texture features achieved the highest prediction accuracy in all quality metrics, with $R_p^2$ values of 0.993, 0.989, 0.979, 0.948, and 0.994 for Ash, VM, MC, FC, and CV, respectively. This study provides a technical reference for hyperspectral imaging technology as a new method for the rapid, nondestructive proximate analysis and quality assessment of coal. Full article

In the process of scratch defect detection in textured materials, there are often problems of low efficiency in traditional manual detection, large errors in machine vision, and difficulty in distinguishing defective scratches from the background texture. In order to solve these problems, we developed an enhanced scratch defect detection system for textured materials based on optimized photometric stereo vision and FFT-Gabor filtering. We designed and optimized a novel hemispherical image acquisition device that allows for selective lighting angles. This device integrates images captured under multiple light sources to obtain richer surface gradient information for textured materials, overcoming issues caused by high reflections or dark shadows under a single light source angle. At the same time, for the textured material, scratches and a textured background are difficult to distinguish; therefore, we introduced a Gabor filter-based convolution kernel, leveraging the fast Fourier transform (FFT), to perform convolution operations and spatial domain phase subtraction. This process effectively enhances the defect information while suppressing the textured background. The effectiveness and superiority of the proposed method were validated through material applicability experiments and comparative method evaluations using a variety of textured material samples. The results demonstrated a stable scratch capture success rate of 100% and a recognition detection success rate of 98.43% ± 1.0%. Full article

Background: In recent years, computational aesthetics and neuroaesthetics have provided novel insights into understanding beauty. Building upon the findings of traditional aesthetics, this study aims to combine these two research methods to explore an interdisciplinary approach to studying aesthetics. Method: Abstract artworks were used as experimental materials. Based on traditional aesthetics and in combination, features of composition, tone, and texture were selected. Computational aesthetic methods were then employed to correspond these features to physical quantities: blank space, gray histogram, Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Gabor filters. An electroencephalogram (EEG) experiment was carried out, in which participants conducted aesthetic evaluations of the experimental materials in different contexts (genuine, fake), and their EEG data were recorded to analyze the impact of various feature classes in the aesthetic evaluation process. Finally, a Support Vector Machines (SVMs) was utilized to model the feature data, Event-Related Potentials (ERPs), context data, and subjective aesthetic evaluation data. Result: Behavioral data revealed higher aesthetic ratings in the genuine context. ERP data indicated that genuine contexts elicited more negative deflections in the prefrontal lobes between 200 and 1000 ms. Class II compositions demonstrated more positive deflections in the parietal lobes at 50–120 ms, while Class I tones evoked more positive amplitudes in the occipital lobes at 200–300 ms. Gabor features showed significant variations in the parieto-occipital area at an early stage. Class II LBP elicited a prefrontal negative wave with a larger amplitude. The results of the SVM models indicated that the model incorporating aesthetic subject and context data (ACC = 0.76866) outperforms the model using only parameters of the aesthetic object (ACC = 0.68657). Conclusion: A positive context tends to provide participants with a more positive aesthetic experience, but abstract artworks may not respond to this positivity. During aesthetic evaluation, the ERP data activated by different features show a trend from global to local. The SVM model based on multimodal data fusion effectively predicts aesthetics, further demonstrating the feasibility of the combined research approach of computational aesthetics and neuroaesthetics. Full article

In the treatment process of brain tumors, it is of great importance to develop a set of MRI image segmentation methods with high accuracy and low cost. In order to extract the feature information for each region of the brain tumor more effectively, this paper proposes a new model Ga-U-Net based on Gabor convolution and an attention mechanism. Based on 3D U-Net, Gabor convolution is added at the shallow layer of the encoder, which is able to learn the local structure and texture information of the tumor better. After that, the CBAM attention mechanism is added after the output of each layer of the encoder, which not only enhances the network’s ability to perceive the brain tumor boundary information but also reduces some redundant information by allocating the attention to the two dimensions of space and channel. Experimental results show that the model performs well for multiple tumor regions (WT, TC, ET) on the brain tumor dataset BraTS 2021, with Dice coefficients of 0.910, 0.897, and 0.856, respectively, which are improved by 0.3%, 2%, and 1.7% compared to the base network, the U-Net network, with an average Dice of 0.887 and an average Hausdorff distance of 9.12, all of which are better than a few other state-of-the-art deep models for biomedical image segmentation. Full article

This paper introduces a novel background subtraction method that utilizes texture-level analysis based on the Gabor filter bank and statistical moments. The method addresses the challenge of accurately detecting moving objects that exhibit similar color intensity variability or texture to the surrounding environment, which conventional methods struggle to handle effectively. The proposed method accurately distinguishes between foreground and background objects by capturing different frequency components using the Gabor filter bank and quantifying the texture level through statistical moments. Extensive experimental evaluations use datasets featuring varying lighting conditions, uniform and non-uniform textures, shadows, and dynamic backgrounds. The performance of the proposed method is compared against other existing methods using metrics such as sensitivity, specificity, and false positive rate. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and robustness. It effectively handles scenarios with complex backgrounds, lighting changes, and objects that exhibit similar texture or color intensity as the background. Our method retains object structure while minimizing false detections and noise. This paper provides valuable insights into computer vision and object detection, offering a promising solution for accurate foreground detection in various applications such as video surveillance and motion tracking. Full article

As prior information for precise nitrogen fertilization management, plant nitrogen content (PNC), which is obtained timely and accurately through a low-cost method, is of great significance for national grain security and sustainable social development. In this study, the potential of the low-cost unmanned aerial vehicle (UAV) RGB system was investigated for the rapid and accurate estimation of winter wheat PNC across the growing season. Specifically, texture features were utilized as complements to the commonly used spectral information. Five machine learning regression algorithms, including support vector machines (SVMs), classification and regression trees, artificial neural networks, K-nearest neighbors, and random forests, were employed to establish the bridge between UAV RGB image-derived features and ground-truth PNC, with multivariate linear regression serving as the reference. The results show that both spectral and texture features had significant correlations with ground-truth PNC, indicating the potential of low-cost UAV RGB images to estimate winter wheat PNC. The H channel, S4O6, and R_SE and R_EN had the highest correlation among the spectral indices, Gabor texture features, and grey level co-occurrence matrix texture features, with absolute Pearson’s correlation coefficient values of 0.63, 0.54, and 0.69, respectively. When the texture features were used together with spectral indices, the PNC estimation accuracy was enhanced, with the root mean square error (RMSE) decreasing from 2.56 to 2.24 g/kg, for instance, when using the SVM regression algorithm. The SVM regression algorithm with validation achieved the highest estimation accuracy, with a coefficient of determination (R²) of 0.62 and an RMSE of 2.15 g/kg based on the optimal feature combination of B_CON, B_M, G_DIS, H, NGBDI, R_EN, R_M, R_SE, S3O7, and VEG. Overall, this study demonstrated that the low-cost UAV RGB system could be successfully used to map the PNC of winter wheat across the growing season. Full article

In order to accurately extract the characteristics of debris flow caused by group rainstorms, effectively identify the on-site information of debris flow, and provide a scientific basis for debris flow monitoring, early warning and disaster control, this paper proposes a method for extracting the characteristics of heavy rainstorm debris flow using multiregional ecological environment remote sensing. In the ecological environment where debris flows occur frequently, remote sensing data of heavy rainstorm debris flows are preprocessed using remote sensing technology, providing an important basis for the feature extraction of debris flows. The kernel principal component analysis method and Gabor filters are innovatively used to extract the spectral and texture features of rainstorm and debris flow remote sensing images, and the convolutional neural network structure is improved based on the open source deep learning framework, integrating multilevel features to generate debris flow feature maps. The improved convolution neural network is then used to extract the secondary features of the fusion feature map, and the feature extraction of heavy rainstorm debris flow is realized. The experiment shows that this method can accurately extract the characteristics of heavy rainstorm debris flow. Fused remote sensing images of debris flow effectively ameliorate the problem of insufficient informational content in a single image and improve image clarity. When the Gabor kernel function has eight different directions, the feature extraction effect of the debris flow image in each direction of the heavy rainstorm is the best. Full article

Multi-source image registration has often suffered from great radiation and geometric differences. Specifically, grayscale and texture from similar landforms in different source images often show significantly different visual features, and these differences disturb the corresponding point extraction in the following image registration process. Considering that edges between heterogeneous images can provide homogeneous information and more consistent features can be extracted based on image edges, an edge consistency radiation-change insensitive feature transform (EC-RIFT) method is proposed in this paper. Firstly, the noise and texture interference are reduced by preprocessing according to the image characteristics. Secondly, image edges are extracted based on phase congruency, and an orthogonal Log-Gabor filter is performed to replace the global algorithm. Finally, the descriptors are built with logarithmic partition of the feature point neighborhood, which improves the robustness of the descriptors. Comparative experiments on datasets containing multi-source remote sensing image pairs show that the proposed EC-RIFT method outperforms other registration methods in terms of precision and effectiveness. Full article

Fabric inspection is critical in fabric manufacturing. Automatic detection of fabric defects in the textile industry has always been an important research field. Previously, manual visual inspection was commonly used; however, there were drawbacks such as high labor costs, slow detection speed, and high error rates. Recently, many defect detection methods based on deep learning have been proposed. However, problems need to be solved in the existing methods, such as detection accuracy and interference of complex background textures. In this paper, we propose an efficient segmentation algorithm that combines traditional operators with deep learning networks to alleviate the existing problems. Specifically, we introduce a Gabor filter into the model, which provides the unique advantage of extracting low-level texture features to solve the problem of texture interference and enable the algorithm to converge quickly in the early stages of training. Furthermore, we design a U-shaped architecture that is not completely symmetrical, making model training easier. Meanwhile, multi-stage result fusion is proposed for precise location of defects. The design of this framework significantly improves the detection accuracy and effectively breaks through the limitations of transformer-based models. Experimental results show that on a dataset with one class, a small amount of data, and complex sample background texture, our method achieved 90.03% and 33.70% in ACC and IoU, respectively, which is almost 10% higher than other previous state of the art models. Experimental results based on three different fabric datasets consistently show that the proposed model has excellent performance and great application potential in the industrial field. Full article

Cracks are fractures or breaks that occur in materials such as concrete, metals, rocks, and other solids. Various methods are used to detect and monitor cracks; among many of them, image-based methodologies allow fast identification of the distress and easy quantification of the percentage of cracks in the scene. Two main categories can be identified: classical and deep learning approaches. In the last decade, the tendency has moved towards the use of the latter. Even though they have proven their outstanding predicting performance, they suffer some drawbacks: a “black-box” nature leaves the user blind and without the possibility of modifying any parameters, a huge amount of labeled data is generally needed, a process that requires expert judgment is always required, and, finally, they tend to be time-consuming. Accordingly, the present study details the methodology for a new algorithm for crack segmentation based on the theory of minimal path selection combined with a region-based approach obtained through the segmentation of texture features extracted using Gabor filters. A pre-processing step is described, enabling the equalization of brightness and shadows, which results in better detection of local minima. These local minimal are constrained by a minimum distance between adjacent points, enabling a better coverage of the cracks. Afterward, a region-based segmentation technique is introduced to determine two areas that are used to determine threshold values used for rejection. This step is critical to generalize the algorithm to images presenting close-up scenes or wide cracks. Finally, a geometrical thresholding step is presented, allowing the exclusion of rounded areas and small isolated cracks. The results showed a very competitive F1-score (0.839), close to state-of-the-art values achieved with deep learning techniques. The main advantage of this approach is the transparency of the workflow, contrary to what happens with deep learning frameworks. In the proposed approach, no prior information is required; however, the statistical parameters may have to be adjusted to the particular case and requirements of the situation. The proposed algorithm results in a useful tool for researchers and practitioners needing to validate their results against some reference or needing labeled data for their models. Moreover, the current study could establish the grounds to standardize the procedure for crack segmentation with a lower human bias and faster results. The direct application of the methodology to images obtained with any low-cost sensor makes the proposed algorithm an operational support tool for authorities needing crack detection systems in order to monitor and evaluate the current state of the infrastructures, such as roads, tunnels, or bridges. Full article

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. Full article

One of the most serious and dangerous ocular problems in premature infants is retinopathy of prematurity (ROP), a proliferative vascular disease. Ophthalmologists can use automatic computer-assisted diagnostic (CAD) tools to help them make a safe, accurate, and low-cost diagnosis of ROP. All previous CAD tools for ROP diagnosis use the original fundus images. Unfortunately, learning the discriminative representation from ROP-related fundus images is difficult. Textural analysis techniques, such as Gabor wavelets (GW), can demonstrate significant texture information that can help artificial intelligence (AI) based models to improve diagnostic accuracy. In this paper, an effective and automated CAD tool, namely GabROP, based on GW and multiple deep learning (DL) models is proposed. Initially, GabROP analyzes fundus images using GW and generates several sets of GW images. Next, these sets of images are used to train three convolutional neural networks (CNNs) models independently. Additionally, the actual fundus pictures are used to build these networks. Using the discrete wavelet transform (DWT), texture features retrieved from every CNN trained with various sets of GW images are combined to create a textural-spectral-temporal demonstration. Afterward, for each CNN, these features are concatenated with spatial deep features obtained from the original fundus images. Finally, the previous concatenated features of all three CNN are incorporated using the discrete cosine transform (DCT) to lessen the size of features caused by the fusion process. The outcomes of GabROP show that it is accurate and efficient for ophthalmologists. Additionally, the effectiveness of GabROP is compared to recently developed ROP diagnostic techniques. Due to GabROP’s superior performance compared to competing tools, ophthalmologists may be able to identify ROP more reliably and precisely, which could result in a reduction in diagnostic effort and examination time. Full article

The security of a computer system can be harmed by specific applications, such as malware. Malware comprises unwanted, dangerous enemies that aim to compromise the security and generate significant loss. Consequently, Malware Detection (MD) and Malware Classification (MC) has emerged as a key issue for the cybersecurity society. MD only involves locating malware without determining what kind of malware it is, but MC comprises assigning a class of malware to a particular sample. Recently, a few techniques for analyzing malware quickly have been put out. However, there remain numerous difficulties, such as the low classification accuracy of samples from related malware families, the computational complexity, and consumption of resources. These difficulties make detecting and classifying malware very challenging. Therefore, in this paper, we proposed an efficient malware detection and classification technique that combines Segmentation-based Fractal Texture Analysis (SFTA) and Gaussian Discriminant Analysis (GDA). The outcomes of the experiment demonstrate that the SFTA-GDA produces a high classification rate. There are three main steps involved in our malware analysis, namely: (i) malware conversion; (ii) feature extraction; and (iii) classification. We initially convert the RGB malware images into grayscale malware images for effective malware analysis. The SFTA and Gabor features are then extracted from gray-scale images in the feature extraction step. Finally, the classification is carried out by GDA and Naive Bayes (NB). The proposed method is evaluated on a common MaleVis dataset. The proposed SFTA-GDA is the effective choice since it produces the highest accuracy rate across all families of the MaleVis Database. Experimental findings indicate that the accuracy rate was 98%, which is higher than the overall accuracy from the existing state-of-the-art methods. Full article

The benefits of autonomous image segmentation are readily apparent in many applications and garners interest from stakeholders in many fields. The wide range of benefits encompass applications ranging from medical diagnosis, where the shape of the grouped pixels increases diagnosis accuracy, to autonomous vehicles where the grouping of pixels defines roadways, traffic signs, other vehicles, etc. It even proves beneficial in many phases of machine learning, where the resulting segmentation can be used as inputs to the network or as labels for training. The majority of the available image segmentation algorithmic development and results focus on visible image modalities. Therefore, in this treatment, the authors present the results of a study designed to identify and improve current semantic methods for infrared scene segmentation. Specifically, the goal is to propose a novel approach to provide tile-based segmentation of occlusion clouds in Long Wave Infrared images. This work complements the collection of well-known semantic segmentation algorithms applicable to thermal images but requires a vast dataset to provide accurate performance. We document performance in applications where the distinction between dust cloud tiles and clear tiles enables conditional processing. Therefore, the authors propose a Gray Level Co-Occurrence Matrix (GLCM) based method for infrared image segmentation. The main idea of our approach is that GLCM features are extracted from local tiles in the image and used to train a binary classifier to provide indication of tile occlusions. Our method introduces a new texture analysis scheme that is more suitable for image segmentation than the solitary Gabor segmentation or Markov Random Field (MRF) scheme. Our experimental results show that our algorithm performs well in terms of accuracy and a better inter-region homogeneity than the pixel-based infrared image segmentation algorithms. Full article

Land Use and Land Cover (LULC) monitoring is crucial for global transformation, sustainable land control, urban planning, urban growth prediction, and the establishment of climate regulations for long-term development. Remote sensing images have become increasingly important in many environmental planning and land use surveys in recent times. LULC is evaluated in this research using the Sat 4, Sat 6, and Eurosat datasets. Various spectral feature bands are involved, but unexpectedly little consideration has been given to these characteristics in deep learning models. Due to the wide availability of RGB models in computer vision, this research mainly utilized RGB bands. Once the pre-processing is carried out for the images of the selected dataset, the hybrid feature extraction is performed using Haralick texture features, an oriented gradient histogram, a local Gabor binary pattern histogram sequence, and Harris Corner Detection to extract features from the images. After that, the Improved Mayfly Optimization (IMO) method is used to choose the optimal features. IMO-based feature selection algorithms have several advantages that include features such as a high learning rate and computational efficiency. After obtaining the optimal feature selection, the LULC classes are classified using a multi-class classifier known as the Multiplicative Long Short-Term Memory (mLSTM) network. The main functionality of the multiplicative LSTM classifier is to recall appropriate information for a comprehensive duration. In order to accomplish an improved result in LULC classification, a higher amount of remote sensing data should be processed. So, the simulation outcomes demonstrated that the proposed IMO-mLSTM efficiently classifies the LULC classes in terms of classification accuracy, recall, and precision. When compared with ConvNet and Alexnet, the proposed IMO-mLSTM method accomplished accuracies of 99.99% on Sat 4, 99.98% on Sat 6, and 98.52% on the Eurosat datasets. Full article