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Rheopressure die casting is one of the newest casting processes of present era for manufacturing of near-net-shaped cast components with improved mechanical properties and high-dimensional accuracy. Rheopressure die casting demands especially prepared semi-solid alloy slurry having nearly globular primary Al phase. In this study, a cooling slope has been employed to produce semi-solid slurry of Al-Si-Mg (A356) alloy and successively cast in a metallic mould. Image texture analysis techniques have been implemented for accurate evaluation of the primary phase morphology of cast samples. In this research, efforts have been made to apply fractal analysis and run-length statistical analysis techniques for automatic characterization of optical micrographs of cast samples produced at different processing conditions. [ABSTRACT FROM PUBLISHER]

Quality of machined surfaces and its integrity are strongly dependent on tool wear, which consequently depends on the tool life or machining time. Therefore, prediction of the machining time of the cutting tool during machining processes is very important in order to obtain high precision parts and to reduce manual fit operations and production cost. In this work, the relationship between texture features of the gray-level co-occurrence matrix and the machining time of the cutting tool in turning operations has been investigated, and the results show that four texture features are highly correlated with machining time. These highly correlated texture features were utilized to calculate machining time of the cutting tool from captured images of machined parts. The verification results showed that the maximum percentage of error between the actual and calculated machining time ranged between −4.65% and 7.79%. [ABSTRACT FROM PUBLISHER]

Measuring surface roughness is vital to quality control of the machined workpiece. In recent years, vision systems have made image analysis easier and more flexible for measuring surface roughness by using texture features. In this paper, the texture features of the grey-level co-occurrence matrix (GLCM) have been utilized to estimate surface roughness of specimens machined by turning operations. The relationship between GLCM texture features and surface roughness has been investigated to discover which texture features can be used to estimate surface roughness. The correlation coefficient between each texture feature and the arithmetic average height (Ra) was calculated and discussed. The investigation showed that six texture features are highly correlated with Ra. Therefore, these texture features were used to estimate surface roughness for similar specimens with known values of Ra. The results showed that the maximum percentage of error between the actual Ra and the estimated Ra was about ±7 per cent. [ABSTRACT FROM AUTHOR]

In most fabric industries fabric quality is assessed through manual inspection, which depends on an individual judgment. It is necessary to design an automatic fabric defect performance inspection system for the industry. This study aimed to develop a real-time, low-cost, and high-performance home textile fabric defect inspection machine system. The proposed system uses the Haar wavelet transform to reduce the information content of the fabric image. The brightness of the fabric image is compensated and the camera luminance is corrected in order to filter the image texture for fabric images with the Gaussian filter after correction. After that, the fabric defect classification was performed by using the random forest classifier. The designed system capability can detect and verify 10 kinds of fabrics with different colors. Moreover, the hardware cost of the machine is low and the average true defect recognition detection rate is more than 98.70%, with good adaptability. Meanwhile, the average processing detection time for a single image is 70 ms with a fabric defect inspection speed of 30 m/min. The efficiency of the machine is increased by five times compared with the traditional inspection. The designed inspection machine can also replace manual grading, cutting, and finishing in the processes of labeling defects. Eventually, it can reduced man power and overall mass production cost, so even small-scale home textile industries can afford a machine with high-precision defect detection. [ABSTRACT FROM AUTHOR]

Factories must proof different colors on fabric in order to verify the feasibility of the color scheme. This proofing process is time-consuming and laborious. To recolor printed fabric images, a novel image recoloring method was proposed in this paper. This method can provide convenience for printed fabric designers and can be used to simulate printed fabric proofing. The relative total variation model was implemented to remove the fabric image texture and noise. Next, in the CIE1976 L * a * b * color space, the mean-shift clustering algorithm was utilized to segment the reference image and the target image to obtain the separated color regions. Then, the color regions of the reference image and the target image were matched based on the salient map and the values of the L * a * b * channels. Finally, local color transfer was performed between the matched color regions based on the matching results. Experiments were conducted on 100 printed fabrics with different color schemes. Results indicated that the proposed method can transfer the color appearance of the reference printed fabric image to the target printed fabric image and realize printed fabric image recoloring. [ABSTRACT FROM AUTHOR]

Background: Brain CT needs more attention to improve the extremely low image contrast and image texture. Purpose: To evaluate the performance of iterative progressive reconstruction with visual modeling (IPV) for the improvement of low-contrast detectability (IPV-LCD) compared with filtered backprojection (FBP) and conventional IPV. Materials and methods: Low-contrast and water phantoms were used. Helical scans were conducted with the use of a CT scanner with 64 detectors. The tube voltage was set at 120 kVp; the tube current was adjusted from 60 to 300 mA with a slice thickness of 0.625 mm and from 20 to 150 mA with a slice thickness of 5.0 mm. Images were reconstructed with the FBP, conventional IPV, and IPV-LCD algorithms. The channelized Hotelling observer (CHO) model was applied in conjunction with the use of low-contrast modules in the low-contrast phantom. The noise power spectrum (NPS) and normalized NPS were calculated. Results: At the same standard and strong levels, the IPV-LCD method improved low-contrast detectability compared with the conventional IPV, regardless of contrast-rod diameters. The mean CHO values at a slice thickness of 0.625 mm were 1.83, 3.28, 4.40, 4.53, and 5.27 for FBP, IPV STD, IPV-LCD STD, IPV STR, and IPV-LCD STR, respectively. The normalized NPS for the IPV-LCD STD and STR images were slightly shifted to the higher frequency compared with that for the FBP image. Conclusion: IPV-LCD images further improve the low-contrast detectability compared with FBP and conventional IPV images while maintaining similar FBP image appearances. [ABSTRACT FROM AUTHOR]

Fabric luster is usually described visually (e.g. dull, bright, glittery, etc). Instrumental reflectance measurements have been applied to gauge luster, but these techniques only give overall reflectance values and do not adequately describe the luster. Image texture analysis, on the other hand, has been successfully applied to characterise luster. In the procedurc a fabric surface is photographed, the photonegative scanned on a digital microdensitometer, and the data processed in a computer. In the work described the images formed by light rellected from carpet surfaces accurately represent the carpet luster appearance. Quantitative and descriptive luster measurements show that people rate carpet luster mainly on the quantity and intensity of specular glitter points. [ABSTRACT FROM PUBLISHER]

This work presents the development of a vibration-based condition monitoring method for early detection and classification of valve wear within industrial reciprocating compressors through the combined use of time-frequency analysis with image-based pattern recognition techniques. Two common valve related fault conditions are spring fatigue and valve seat wear and are seeded on the crank-side discharge valves of a Dresser-Rand ESH-1 industrial compressor. Operational data including vibration, cylinder pressure, and crank shaft position are collected and processed using a transformed time-frequency domain approach. The results are processed as images with features extracted using 1st and 2nd order image texture statistics and binary shape properties. Feature reduction is accomplished by principal component analysis and a Bayesian classification strategy is employed with accuracy rates greater than 90%. [ABSTRACT FROM AUTHOR]

Mangroves are salt tolerant woody plants that form highly productive intertidal ecosystems in tropical and subtropical regions. Despite the established importance of mangroves to the coastal environment, including fisheries, deforestation continues to be a major threat due to pressures for wood and forest products, land conversion to aquaculture, and coastal urban development. Over the past 15 years, remote sensing has played a crucial role in mapping and understanding changes in the areal extent and spatial pattern of mangrove forests related to natural disasters and anthropogenic forces. This paper reviews recent advancements in remote-sensed data and techniques and describes future opportunities for integration or fusion of these data and techniques for large-scale monitoring in mangroves as a consequence of anthropogenic and climatic forces. While traditional pixel-based classification of Landsat, SPOT, and ASTER imagery has been widely applied for mapping mangrove forest, more recent types of imagery such as very high resolution (VHR), Polarmetric Synthetic Aperture Radar (PolSAR), hyperspectral, and LiDAR systems and the development of techniques such as Object Based Image Analysis (OBIA), spatial image analysis (e.g. image texture), Synthetic Aperture Radar Interferometry (InSAR), and machine-learning algorithms have demonstrated the potential for reliable and detailed characterization of mangrove forests including species, leaf area, canopy height, and stand biomass. Future opportunities include the application of existing sensors such as the hyperspectral HYPERION, the application of existing methods from terrestrial forest remote sensing, investigation of new sensors such as ALOS PRISM and PALSAR, and overcoming challenges to the global monitoring of mangrove forests such as wide-scale data availability, robust and consistent methods, and capacity-building with scientists and organizations in developing countries. [ABSTRACT FROM AUTHOR]

This paper describes a new measuring algorithm for three-dimensional (3-D) braided composite materials preform exterior average braided angle based on image texture. In this project, an advanced nonlinear filtered algorithm for image of 3-D braided composite materials preform was developed. The digital signal processor, TMS320C/F240, has been used as a device for the preform exterior image filtering. The nonlinear filter consisted of minimum, median, and maximum filters. The DUAL PORT RAM. TC514256. has been used as image buffer. The proposed implementations achieve more powerful and stable noise reduction than commercial algorithms. In this research we also propose a more effective and rapid system design method and describe the procedure and how to implement our proposed algorithms by using the surface image testing system. A real time preform surface image collecting and filtering system was developed without PC controlling (the FPGA technology and digital signal process were used). To show the effectiveness of the proposed designing scheme, angular power spectrum algorithms were designed and it generated an optimized and portable code using assemble and VC++ language; then it was downloaded to a PC. It built programs that helped speed up the system so as to produce high quality results in less time. Experimental results show that the proposed method is feasible. The research was appraised by Science & Technology Committee of Tianjin. The appraisal report shows the research is advanced in the world. So far. the research is a new development for 3-D braided composite material preform measuring. It will provide the perfect data for improving artifact technology for 3-D braided composite material preform. [ABSTRACT FROM AUTHOR]

Titanium alloys are extensively employed in machining to produce high-value components, where surface roughness is crucial for their functionality. In this study, the roughness of Ti6Al4V and Ti6Al7Nb turned surfaces was analysed. The goal was to compare the alloy's roughness behaviour when machined under the same conditions. Therefore, hypothesis testing was performed to assess the equality of the expected values and population median. It was found that, for some parameters, the mean (RzI, Rt) and median (Rq, RzD, Pt) roughness was statistically different when changing alloys. However, for others, it did not present a significant difference, in terms of mean (RmD, Rp) and median (Ra, Rpm). Consequently, a relevant insight from this study was that the phenomenon interpretation was affected by roughness parameter selection. On the other hand, as expected, the feed rate played a critical role in roughness control. It was observed that Ti6Al7Nb had a smoother topography (Ra was 0.15 ± 0.01 μm and 0.62 ± 0.04 μm) than Ti6Al4V (0.31 ± 0.02 μm and 0.67 ± 0.03 μm) at 0.05 and 0.15 mm/rev. Yet, at 0.1 mm/rev, this behaviour changed, as Ti6Al4V had a smoother surface (Ra was 0.40 ± 0.02 μm) than Ti6Al7Nb (Ra was 0.51 ± 0.03 μm). Finally, the multi-optimization indicated that a low feed rate (0.05 mm/rev) and medium (60 m/min) and high (90 m/min) cutting speed led to lower roughness and higher material removal rate for turning Ti6Al7Nb and Ti6Al7V, respectively. The insights from this study may have a practical application in the manufacturing and quality control of biomedical components. [ABSTRACT FROM AUTHOR]

This paper is concerned with a two-dimensional fractional optimal control problem whose governing equations are distributed order fractional differential equations in the Caputo sense. A generalized fractional-order Legendre wavelet method has been used to solve the two-dimensional distributed-order fractional optimal control problem. An exact formula for the Riemann–Liouville integration of generalized fractional-order Legendre wavelet has been derived by using regularized beta functions. This formula and the two-dimensional Gauss–Legendre integration formula have been used to solve the two-dimensional distributed order fractional optimal control problem. Moreover, an L 2-error estimate in the approximation of an unknown function with a generalized fractional-order Legendre wavelet has been derived and the estimated order has been verified for a given function. Furthermore, convergence analysis for the proposed method has been presented. In the last, two test problems have been considered to illustrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

To solve the problem of large interference of shearer rocker arm vibration signal and difficulty in feature selection, and recognize accurately the cutting status of shearer, a novel pattern identification method based on Symmetrized Dot Pattern (SDP), Local Mean Decomposition (LMD) and Multi-Scale Convolution Kernel Deep Convolutional Neural Network (MCK-DCNN) is presented. Firstly, the vibration signal of shearer rocker arm is decomposed by LMD to get multiple product functions (PFs). The previous three PFs are transformed into SDP images with different features by SDP method, which are input into MCK-DCNN model to automatically extract features and identify shearer cutting status. The method can achieve the classification rate of 97.9%, which is superior to 1D_CNN and LeNet. The comparison result indicates that the method can provide technical support for improving the automatic coal cutting performance of the shearer. [ABSTRACT FROM AUTHOR]

Hashimoto’s thyroiditis is the most common type of inflammation of the thyroid gland, and accurate diagnosis of Hashimoto’s thyroiditis would be helpful to better manage the disease process and predict thyroid failure. Most of the published computer-based techniques that use ultrasound thyroid images for Hashimoto’s thyroiditis diagnosis are limited by lack of procedure standardization because individual investigators use various initial ultrasound settings. This article presents a computer-aided diagnostic technique that uses grayscale features and classifiers to provide a more objective and reproducible classification of normal and Hashimoto’s thyroiditis-affected cases. In this paradigm, we extracted grayscale features based on entropy, Gabor wavelet, moments, image texture, and higher order spectra from the 100 normal and 100 Hashimoto’s thyroiditis-affected ultrasound thyroid images. Significant features were selected using t-test. The resulting feature vectors were used to build the following three classifiers using tenfold stratified cross validation technique: support vector machine, k-nearest neighbor, and radial basis probabilistic neural network. Our results show that a combination of 12 features coupled with support vector machine classifier with the polynomial kernel of order 1 and linear kernel gives the highest accuracy of 80%, sensitivity of 76%, specificity of 84%, and positive predictive value of 83.3% for the detection of Hashimoto’s thyroiditis. The proposed computer-aided diagnostic system uses novel features that have not yet been explored for Hashimoto’s thyroiditis diagnosis. Even though the accuracy is only 80%, the presented preliminary results are encouraging to warrant analysis of more such powerful features on larger databases. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to texture analysis that could be used to recognize the fabric nature and type of the main weaving texture. First, the color scanner captures the fabric image and saves it as a digital image and then the wavelet transformation is used to display the image texture. The co-occurrence matrix is then applied to calculate the texture characteristics, such as angular second moment, entropy, homogeneity, and contrast, and finally, the learning vector quantization networks (LVQN) are adopted as a classifier to categorize the fabric nature and the type of weaving texture. The experimental result showed that this approach could automatically and accurately classify the fabric nature, including woven fabric, knitted fabric and non-woven fabric, and the type of its main weaving texture, such as plain, twill or satin weave, single or double knitted and non-woven fabric. [ABSTRACT FROM AUTHOR]

The Selective Laser Melting (SLM) process based additive manufacturing has wide applications in medical, aerospace, defense, and automotive industries. To qualify the components for certain tribological applications, the characterization of surface texture is very important. But the applicability of traditional methods and parameters to characterize the surface texture were under evaluation. As the nature manufacturing the components were very different and complex, the unconventional surface characterization methods also under evaluation to reveal much more meaningful information. This study demonstrates the surface characterization of Ti-6Al-4V SLM components using fractal analysis of the surface images. The computed fractal dimension using the Fourier transform method showed a strong correlation of more than 0.8 with the measured 3D surface roughness parameters. The change in anisotropic nature of the surface images with the process parameter variation is studied and found that the surface textures showed a weaker anisotropic nature at lower laser power ranges, high scanning speed, and high hatch distance values. The lacunarity analysis is carried out using the gliding box algorithm to study the homogeneity nature of the surface texture and found that the surface texture is more homogeneous at higher surface roughness conditions. The study results can be utilized for the development of a quick, low-cost surface monitoring system in real-time for additive manufacturing industries. [ABSTRACT FROM AUTHOR]

In the modern manufacturing industries, the 3D reconstruction of scenes emerges as a critical element with far-reaching implications. This study presents the development of a 3D reconstruction system based on active illuminated Shape from focus to capture images with added surface texture. An algorithm is introduced to select the most suitable image frame from a series of images with different texture patterns based on their focus metrics, thereby enhancing the accuracy of depth interpretation. A comparative analysis is conducted between the proposed method for depth map reconstruction and the conventional shape-from-focus approach. The results demonstrate significant improvements in accuracy and performance. Root mean square errors for three different shape samples are reported as 2.50, 1.72, and 1.23, accompanied by correlation values of 0.56, 0.25, and 0.39. Furthermore, median filtering with a dynamic window size is employed to refine the initial depth map further, resulting in enhanced performance. The resulting root mean square errors are measured as 1.71, 1.12, and 1.13, corresponding correlation values of 0.82, 0.38, and 0.42. The results obtained in this study provide strong evidence supporting the effectiveness of the proposed method combined with median filtering using a dynamic window size in significantly improving accuracy and performance for depth map reconstruction. [ABSTRACT FROM AUTHOR]

Compared with solid-colored fabrics, the textures in yarn-dyed fabric images are more complex, making the task of defect detection more challenging. To achieve efficient detection, this study proposes an automatic detection framework for dyed fabric defects. The proposed framework consists of a hardware system and a detection algorithm. For efficient and high-quality acquisition of fabric images, an image acquisition assembly equipped with three sets of light sources and a mirror was developed. In addition, a defect detection algorithm based on Fourier convolution and a convolutional autoencoder is proposed. Abandoning the common way of adding noise, this paper proposes to generate image pairs for training using a random masking method in the training phase. In the autoencoder, some traditional convolutional layers are replaced with Fourier convolutional layers. Ablation experiments verify the effectiveness of the mask generation method and Fourier convolution. Compared with other defect detection methods, the proposed method achieves the best performance, which verifies the superiority of the method. The maximum detection speed of the developed system can reach 41 meters per minute, which can meet real-time requirements. [ABSTRACT FROM AUTHOR]

Bearings, as a key part of rotating machinery, are prone to failure due to fatigue and aging resulting from their long-term and high-load operation. To ensure stability of the mechanical equipment, monitoring bearing health is helpful to guarantee smooth operation of machinery and increasing machinery availability. This article puts forward an intelligent non-invasive thermal images-based fault diagnostic approach to periodically monitor condition of the rolling contact bearings in respect of their deterioration due to defects on the inner race, outer race and balls/rollers. Thermal images of four bearing conditions, including one healthy and three faulty states, have been considered followed by a performance classification based comparative analysis using Support Vector Machines (SVM) and Convolutional Neural Network (CNN). The CNN consists of many tools under its cap but for this work, the AlexNet architecture is used which has proved to be more effective than SVM. The experimental findings reveal that non-contact infrared thermography has enormous potential for automatically identifying problems and detecting early warning, regardless of speed, resulting in negligible shutdowns of the system due to bearing failure. [ABSTRACT FROM AUTHOR]

The rail train number is the only sign information of the vehicle. It is very important to use the image processing method to locate the train number to improve the efficiency of rail train management. Therefore, an automatic image positioning framework for rail train numbers is developed. First, aiming at the problem of low contrast between the rail train number and vehicle body caused by insufficient illumination of the rail train in a tunnel and indoor, the improved single-scale Retinex algorithm based on brightness control is used to enhance the image, which provides the basis for subsequent train number image positioning. Then, a train number location algorithm based on speed-up robust features and stroke width transform is proposed to locate the train number accurately. The experimental results show that the accuracy of the developed method is 96.91% in the complex environment of uneven illumination and large distortion of rail train numbers. [ABSTRACT FROM AUTHOR]

Heart disease has a higher fatality rate than any other disease. Increased Atrial fat on the left atrium has been discovered to cause Atrial Fibrillation (AF) in most patients. AF can put one's life at risk and eventually lead to death. AF might worsen over time; therefore, it is crucial to have an early diagnosis and treatment. To evaluate the left atrium fat tissue pattern using Radon descriptor-based machine learning. This study developed a bridge between the Radon transform framework and machine learning to distinguish two distinct patterns. Motivated by a Radon descriptor-based machine learning approach, the patches of eight patients from CT images of the heart were used and categorized into "epicardial fat tissue" and "nonfat tissue" groups. The 10 feature vectors are extracted from each big patch using Radon descriptors and then fed into a traditional machine learning model. The results show that the proposed methodology discriminates between fat tissues and nonfat tissues clearly. KNN has shown the best performance with 96.77% specificity, 98.28% sensitivity, and 97.50% accuracy. To our knowledge, this study is the first attempt to provide a Radon transform-based machine learning method to distinguish between fat tissue and nonfat tissue on the left atrium. Our proposed research method could be potentially used in advanced interventions. [ABSTRACT FROM AUTHOR]

Cashmere and wool play an important role in the wool industry and textile industry, and suitable features are the key to identifying them. To obtain effective features and improve the accuracy of cashmere and wool classification, the multi-feature selection and random forest method is used to express in this article. Firstly, the gray-gradient co-occurrence matrix model is used for texture feature extraction to construct the original high-dimensional feature data set; secondly, considering that the original feature data set contains a large number of invalid and redundant features, the feature selection algorithm combining correlation analysis and principal component analysis–weight coefficient evaluation is used to obtain important features, independent features, and principal component sensitive features to complement each other; last but not least, the optimized random forest model analyzes the results. The results show that the combination of multi-feature selection subsets and random forest makes the classification accuracy of cashmere and wool more reliable, and the accuracy fluctuates around 90%. [ABSTRACT FROM AUTHOR]

Aiming to accurately detect various defects in the fabric production process, we propose a fabric defect detection algorithm based on the feature fusion of a convolutional neural network (CNN) and optimized extreme learning machine (ELM). Firstly, we use transfer learning to transfer the parameters of the first 13 convolutional layers and first two fully connected layers of a VGG16 network model as pre-trained by ImageNet to the initial model and fine-tune the parameters. Subsequently, the fine-tuned model is used as a feature extractor to extract features of RGB images and their corresponding L-component images. A principal component analysis is used to reduce the dimensionality of the features and fuse the reduced features. The moth flame optimization (MFO) algorithm is used to initialize the optimization variables of a parallel chaotic search (PCS) algorithm, and the PCS algorithm (as optimized by the MFO algorithm) is used to optimize the input weight and bias of the ELM (i.e., the PCS-MFO-ELM (PMELM)). Finally, the PMELM is used to replace the softmax classifier of the CNN to classify and detect fabric defect features. The experimental results show that on the amplified TILDA dataset, the precision, recall, F1-score, and accuracy rates of this algorithm for fabric holes, stains, warp breaks, dragging, and folds in fabric can reach 98.57%, 98.52%, 98.52%, and 98.50%, respectively, that is, higher than those of other algorithms. Through a validity experiment, this method is shown to be suitable for defect detection for unpatterned fabrics, regular patterned fabrics, and irregularly patterned fabrics. [ABSTRACT FROM AUTHOR]

To solve the problem of inefficiency and inaccuracy associated with the classification of fabric wrinkles by human eyes, as well as improve current deficiencies in the application of neural networks for the classification of fabric wrinkles, we propose a model based on the salp swarm algorithm improved by ant lion optimization to optimize the random vector functional link to objectively evaluate the fabric wrinkle level. First, to improve the global searchability of the salp swarm algorithm and avoid the local optima problem, the use of ant lion optimization to improve the salp swarm algorithm is proposed in this study. Afterward, the improved salp swarm algorithm is used to optimize the input weight and hidden layer bias of the random vector functional link to avoid the inaccuracy and instability of random vector functional link classification owing to the randomness of the parameters. Finally, the performance of the proposed algorithm is verified using a fabric wrinkle dataset. Comparative experiments show that the classification accuracy of the proposed ant lion optimization - salp swarm algorithm - random vector functional link algorithm were 8.46%, 2.05%, 10.28%, 3.50%, and 4.42% higher than those of random vector functional link, improved random vector functional link based on salp swarm algorithm, extreme learning machine, improved extreme learning machine based on whale optimization algorithm, and improved backpropagation based on the Levenberg-Marquardt algorithm. Furthermore, the classification accuracy of the wrinkle level was effectively improved. All the fabrics used in this study were monochromatic, and multi-color printed fabrics have a significant impact on the difficulty of image processing and classification results. The next research step is to evaluate the wrinkle level of multi-color printed fabrics. [ABSTRACT FROM AUTHOR]

Deep learning–based fabric defect detection methods have been widely investigated to improve production efficiency and product quality. Although deep learning–based methods have proved to be powerful tools for classification and segmentation, some key issues remain to be addressed when applied to real applications. Firstly, the actual fabric production conditions of factories necessitate higher real-time performance of methods. Moreover, fabric defects as abnormal samples are very rare compared with normal samples, which results in data imbalance. It makes model training based on deep learning challenging. To solve these problems, an extremely efficient convolutional neural network, Mobile-Unet, is proposed to achieve the end-to-end defect segmentation. The median frequency balancing loss function is used to overcome the challenge of sample imbalance. Additionally, Mobile-Unet introduces depth-wise separable convolution, which dramatically reduces the complexity cost and model size of the network. It comprises two parts: encoder and decoder. The MobileNetV2 feature extractor is used as the encoder, and then five deconvolution layers are added as the decoder. Finally, the softmax layer is used to generate the segmentation mask. The performance of the proposed model has been evaluated by public fabric datasets and self-built fabric datasets. In comparison with other methods, the experimental results demonstrate that segmentation accuracy and detection speed in the proposed method achieve state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Background: Radiation-related cancer risk is an object of concern in CT of trauma patients, as these represent a young population. Different radiation reducing methods, including iterative reconstruction (IR), and spilt bolus techniques have been introduced in the recent years in different large scale trauma centers. Purpose: To compare image quality in human cadaver exposed to thoracoabdominal computed tomography using IR and standard filtered back-projection (FBP) at different dose levels. Material and methods: Ten cadavers were scanned at full dose and a dose reduction in CTDIvol of 5 mGy (low dose 1) and 7.5 mGy (low dose 2) on a Siemens Definition Flash 128-slice computed tomography scanner. Low dose images were reconstructed with FBP and Sinogram affirmed iterative reconstruction (SAFIRE) level 2 and 4. Quantitative image quality was analyzed by comparison of contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). Qualitative image quality was evaluated by use of visual grading regression (VGR) by four radiologists. Results: Readers preferred SAFIRE reconstructed images over FBP at a dose reduction of 40% (low dose 1) and 56% (low dose 2), with significant difference in overall impression of image quality. CNR and SNR showed significant improvement for images reconstructed with SAFIRE 2 and 4 compared to FBP at both low dose levels. Conclusions: Iterative image reconstruction, SAFIRE 2 and 4, resulted in equal or improved image quality at a dose reduction of up to 56% compared to full dose FBP and may be used a strong radiation reduction tool in the young trauma population. [ABSTRACT FROM AUTHOR]

This work focuses on the identification of five of the most common ferritic morphologies present in welded fusion zones of low carbon steel through images acquired by photomicrographies. With this regards, we discuss the importance of the gray-level co-occurrence matrix to extract the features to be used as the input of the computational intelligence techniques. We use artificial neural networks and support vector machines to identify the proportions of each morphology and present the error identification rate for each technique. The results show that the use of gray-level co-occurrence extraction allows a less intense computational model with statistical validity and the support vector machine as a computational intelligence technique allows smaller variability when compared to the artificial neural networks. [ABSTRACT FROM AUTHOR]

Background: A novel Deep Learning Image Reconstruction (DLIR) technique for computed tomography has recently received clinical approval. Purpose: To assess image quality in abdominal computed tomography reconstructed with DLIR, and compare with standardly applied iterative reconstruction. Material and methods: Ten abdominal computed tomography scans were reconstructed with iterative reconstruction and DLIR of medium and high strength, with 0.625 mm and 2.5 mm slice thickness. Image quality was assessed using eight visual grading criteria in a side-by-side comparative setting. All series were presented twice to evaluate intraobserver agreement. Reader scores were compared using univariate logistic regression. Image noise and contrast-to-noise ratio were calculated for quantitative analyses. Results: For 2.5 mm slice thickness, DLIR images were more frequently perceived as equal or better than iterative reconstruction across all visual grading criteria (for both DLIR of medium and high strength, p < 0.001). Correspondingly, DLIR images were more frequently perceived as better (as opposed to equal or in favor of iterative reconstruction) for visual reproduction of liver parenchyma, intrahepatic vascular structures as well as overall impression of image noise and texture (p < 0.001). This improved image quality was also observed for 0.625 mm slice images reconstructed with DLIR of high strength when directly comparing to traditional iterative reconstruction in 2.5 mm slices. Image noise was significantly lower and contrast-to-noise ratio measurements significantly higher for images reconstructed with DLIR compared to iterative reconstruction (p < 0.01). Conclusions: Abdominal computed tomography images reconstructed using a DLIR technique shows improved image quality when compared to standardly applied iterative reconstruction across a variety of clinical image quality criteria. [ABSTRACT FROM AUTHOR]

Colored spun fabrics are difficult to accurately characterize with a local binary pattern due to texture anisotropy caused by the uneven distribution of dyed fibers. In this paper, we present a texture representation model based on spatial and frequency characteristics. The proposed model takes advantage of the local binary pattern and local phase quantization to extract the texture of woven fabric. Then, the two features are connected in series, and the features of dimension reduction by principal component analysis are used to represent the texture of the fabric image. Finally, the hierarchical hybrid classifier is applied to classify the fabric structure. The experimental results show that the local phase quantization feature is robust to the fuzzy transformation and the texture representation model has a stronger ability of texture description than the single local binary pattern feature, with the average classification accuracy of 97.59% on 336 samples. In addition, compared with the deep learning algorithm, the texture representation algorithm can ensure a high classification accuracy. [ABSTRACT FROM AUTHOR]

Precise and timely detection of brain tumor area has a very high effect on the selection of medical care, its success rate and following the disease process during treatment. Existing algorithms for brain tumor diagnosis have problems in terms of better performance on various brain images with different qualities, low sensitivity of the results to the parameters introduced in the algorithm and also reliable diagnosis of tumors in the early stages of formation. A computer aided system is proposed in this research for automatic brain tumors diagnosis. The method includes four main parts: pre-processing and segmentation techniques, features extraction and final categorization. Gray-level co-occurrence matrix (GLCM) and Discrete Wavelet Transform (DWT) were applied for characteristic extraction of the MR images which are then injected to an optimized convolutional neural network (CNN) for the final diagnosis. The CNN is optimized by a new design of Sparrow Search Algorithm classification (ESSA). Finally, a comparison of the results of the method with three state of the art technique on the Whole Brain Atlas (WBA) database to show its higher efficiency. [ABSTRACT FROM AUTHOR]

Using reasonable cutting parameters of machining process is an effective and convenient means of improving the topography of the machined surfaces. In this study, the methods to find optimised cutting parameters can be obtained by studying the relationship between the cutting parameters and the micro-topography of frequency features in the machined KH2PO4 surfaces. Using the power spectral density and continuous wavelet transform methods, the 2D micro-topographies of frequencies corresponding to different cutting parameters are extracted from the machined KH2PO4 surfaces. The results for the extracted micro-topography are used to analyse the influence of cutting parameters on the spatial frequency feature which consists of the wavelength and amplitude. The middle-frequency feature reflects the variations of depth of cut and spindle speed, and the amplitude of it is directly proportional to depth of cut and spindle speed. The low-frequency feature reflects the variations of the feed rate and decreases to a smaller value when the feed rate increases. The high-frequency feature is mainly affected by the material properties and the vibrations that occur during processing. Comparing the micro-topography of frequencies under different cutting parameters, the depth of cut (3 μm), the spindle speed (400 r/min) and the feed rate (8 μm/r) are the optimised cutting parameters for the machine tools used in this article. In the process of reconstructing the arbitrary frequency topography, the continuous wavelet transform method can compensate for the deficiencies of the power spectral density method for extracting frequencies. [ABSTRACT FROM AUTHOR]

Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima–media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima–media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning–based approaches like self-organizing map, k -nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner. [ABSTRACT FROM AUTHOR]

Background: Hepatic computed tomography (CT) with decreased peak kilovoltage (kVp) may be used to reduce contrast medium doses in patients at risk of contrast-induced acute kidney injury (CI-AKI); however, it increases image noise. To preserve image quality, noise has been controlled by X-ray tube loading (mAs) compensation (TLC), i.e. increased mAs. Another option to control image noise would be to use iterative reconstructions (IR) algorithms without TLC (No-TLC). It is unclear whether this may preserve image quality or only reduce image noise. Purpose: To evaluate image quality of 80 kVp hepatic CT with TLC and filtered back projection (FBP) compared with 80 kVp with No-TLC and IR algorithms (SAFIRE 3 and 5) in patients with eGFR <45 mL/min. Material and Methods: Forty patients (BMI 18–32 kg/m2) were examined with both protocols following injection of 300 mg I/kg. Hepatic attenuation, image noise, enhancement, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective image quality were evaluated for each patient. Results: Comparing TLC/FBP with No-TLC/IR-S5, there were no significant differences regarding hepatic attenuation, image noise, enhancement, SNR and CNR: 114 vs. 115 HU, 14 vs. 14 HU, 55 vs. 57 HU, 8.0 vs. 8.4, and 3.8 vs. 4.0 in median, respectively. No-TLC/IR-S3 resulted in higher image noise and lower SNR and CNR than TLC/FBP. Subjective image quality scoring with visual grading showed statistically significantly inferior scores for IR-S5 images. Conclusion: CT of 80 kVp to reduce contrast medium dose in patients at risk of CI-AKI combined with IR algorithms with unchanged tube loading to control image noise does not provide sufficient diagnostic quality. [ABSTRACT FROM AUTHOR]

Fractal analysis was used in the study to determine a set of feature descriptors which could be applied in the process of diagnosing bone damage caused by osteoporosis. The subject of the research involved the computed tomography images of vertebrae on the thoraco-lumbar region. The data set contained the images of healthy patients and patients diagnosed with osteoporosis. On the basis of fractal analysis and feature selection by linear stepwise regression, three descriptors were obtained. They were two fractal dimensions calculated with the variation method (transect - first differences and filter 1 estimators) and one fractal lacunarity calculated by means of the box counting method. The first two descriptors were obtained as a result of the analysis of grey images, and the third was the result of analysis of binary images. The effectiveness of the descriptors was verified using six popular supervised classification methods: linear and quadratic discriminant analysis, naive Bayes classifier, decision tree, K-nearest neighbours and random forests. The best results were obtained using the K-nearest neighbours classifier; they were as follows: overall classification accuracy - 81%, classification sensitivity - 78%, classification specificity - 90%, positive predictive value - 90%, and negative predictive value - 77%. The results of the research showed that fractal analysis can be a useful tool to extract feature vector of spinal computed tomography images in the diagnosis of osteoporotic bone defects. [ABSTRACT FROM AUTHOR]

In order to overcome the shortcoming that a fabric defect detection method can only fit into a certain type of fabric, this paper presents a novel method by integrating the idea of the integral image into the Elo-rating algorithm (IIER), which can detect the defects of various types of fabric speedily. Firstly, the golden sub-blocks are extracted from defect-free images. The whole images are divided into many sub-blocks, and the integral images of these sub-blocks are obtained. Next, the R sub-blocks are randomly selected from these integral sub-blocks, and each block is assigned an initial Elo point. Afterwards, the R sub-blocks are matched against all sub-blocks and the Elo points are updated after each competition. Finally, regions with bright defects accumulate high Elo points and regions with dark defects accumulate low Elo points. Thus, the threshold value image can be obtained by thresholding the final Elo points, in which white, gray and black regions correspond to bright, dark-defect and defect-free regions, respectively. The performance of the proposed method is evaluated on databases of three categories of fabric, namely raw fabric, yarn-dyed fabric and patterned fabric. The experimental results show that the IIER is a universal algorithm, which has high detection rate for different types of fabrics; in particular, the average correct detection rate can reach 100% for dot-patterned fabric. In addition, the detection time can be significantly reduced comparing with the Elo-rating algorithm (ER). Particularly for star-patterned fabric, the average detection time per image is 24.18 seconds less than the ER. [ABSTRACT FROM AUTHOR]

With huge and ever-growing products in the factory, image retrieval can help the worker retrieve the same, or similar, existing products rapidly and accurately to guide production. In this paper, an effective method based on Fourier transform and local binary pattern is proposed to improve the retrieval efficiency of wool fabric. After capturing the fabric image, histogram equalization was implemented on the value of the Hue, Saturation, Value (HSV) mode to enhance the contrast. Subsequently, Fourier transform together with local binary pattern operator were performed to obtain the frequency spectrum and the local binary pattern, respectively. Each frequency spectrum was divided into 22 rings with the same width, and the standard deviation of the frequencies in each ring was calculated as a Fourier feature. Distinct output values of each local binary pattern were counted and normalized as local binary pattern features. Finally, Euclidean distance was adopted to measure the similarity based on the Fourier feature and local binary pattern feature. Twenty thousand wool fabric images were captured to demonstrate the efficacy of the proposed method. Experimental results indicate that the framework is effective and superior for image retrieval of wool fabric, providing referential assistance for the worker in the factory and improving retrieval efficiency. [ABSTRACT FROM AUTHOR]

Two-dimensional braided composite materials consist of an impregnated and cured braided fiber structure. The braid angle, which describes the orientation of fibers on the mandrel, affects the material properties of the composite part. Furthermore, braid production is often inaccurate, requiring quality assurance measurements of the fiber orientation. In this study, a frequency domain machine vision algorithm was applied to tubular braided preforms to measure fiber alignment during production. Several approaches to compensate for the tubular mandrel shape were tested and the fiber distribution in both the spatial and frequency domains was compared. The developed machine vision system allowed braid angle measurements to be made in real-time and in-line during the braiding process. The integration of such a system eliminates the need to make manual quality assurance measurements of the braided fiber structure, it further automates the braiding process, and it decreases production error. [ABSTRACT FROM AUTHOR]

Image retrieval is a task which retrieves similar images from a large database based on a given input query image. The lacy and embroidered fabric contains repetitive patterns and rich texture, making the image retrieval difficult. The GIST feature is a spatial information feature that performs well on retrieving images with duplicate patterns. Speeded-up robust features (SURF) feature is invariant to rotation, which makes it powerful in retrieving rotated images. The method proposed in this paper is to combine the benefits of both GIST and SURF features, supporting the image retrieval from a fabric image database. In addition, we extract the structure from the texture via the relative total variation to eliminate the influence of complex texture on the feature point extraction. A key insight and contribution of our paper is that the combination enables accurate fabric image retrieval, especially for rotated images. To demonstrate the robustness and accuracy of our method, we applied it to a database that contains 527 fabric images. The experimental results show that the proposed algorithm outperforms the state-of-the-art methods on the fabric images with hollow and embroidery patterns. [ABSTRACT FROM AUTHOR]

To obtain a stable fabric texture representation result and improve the computation speed, a novel method based on dictionary learning is presented. The dictionary is learned by the alternating least-squares method using discrete cosine transform (DCT) as the initiation dictionary. To test the effectiveness of the dictionary, we comprehensively investigated 42 diverse fault-free woven fabric samples, and three fabrics with defects. After the preprocessing procedure, the woven fabric samples were characterized by the learned dictionary. The experiments on 37 samples with different fabric densities demonstrated that the Peak Signal to Noise Ratio becomes larger while the Root Mean Square Error (RMSE) diminishes as the weaving density increases. For defect fabric samples, the proposed algorithm can efficiently inspect the different types of fabric flaws. Results revealed that the learned dictionary is stable, highly efficient, and suitable for modeling fabric textures. In addition, the algorithm was validated by comparing it with the K-Singular Value Decomposition dictionary and the DCT dictionary. The learned dictionary presented strikingly better results in terms of calculation speed, consistent results, and RMSE. In general, the proposed method can be applied in studying the influence of fabric density on the representation of the fabric texture and detecting fabric flaws. [ABSTRACT FROM AUTHOR]

Surface braiding angle and pitch length are two important parameters for characterizing and evaluating the performance of three-dimensional braided composite preforms. In this paper a new method based on an improved curvature scale space corner detector with adaptive threshold is proposed for measuring these parameters, with applications to three-dimensional, four-directional carbon-fiber braided composite preforms. First, the original image is acquired using a system employing ‘dome light source + CCD camera + circular polarizing filter’. Second, the original image is processed using Lab transform and BM3D (block-matching and 3D filter). Third, the corners are detected using an improved curvature scale space corner detector with adaptive threshold. Finally, the pitch lengths and surface braiding angles are measured from the detected corners. Experimental results show that the proposed method can achieve automatic measurement of the pitch length and surface braiding angle with smaller average errors relative to manual measurements compared with alternative schemes. [ABSTRACT FROM AUTHOR]

Chronic liver diseases are fifth leading cause of fatality in developing countries. Early diagnosis is important for timely treatment and to salvage life. Ultrasound imaging is frequently used to examine abnormalities of liver. However, ambiguity lies in visual interpretation of liver stages on ultrasound images. This difficult visualization problem can be solved by analysing extracted textural features from images. Grey-level difference matrix, a texture feature extraction method, can provide information about roughness of liver surface, sharpness of liver borders and echotexture of liver parenchyma. In this article, the behaviour of variants of grey-level difference matrix in characterizing liver stages is investigated. The texture feature sets are extracted by using variants of grey-level difference matrix based on two, three, five and seven neighbouring pixels. Thereafter, to take the advantage of complementary information from extracted feature sets, feature fusion schemes are implemented. In addition, hybrid feature selection (combination of ReliefF filter method and sequential forward selection wrapper method) is used to obtain optimal feature set in characterizing liver stages. Finally, a computer-aided system is designed with the optimal feature set to classify liver health in terms of normal, chronic liver, cirrhosis and hepatocellular carcinoma evolved over cirrhosis. In the proposed work, experiments are performed to (1) identify the best approximation of derivative (forward, central or backward); (2) analyse the performance of individual feature sets of variants of grey-level difference matrix; (3) obtain optimal feature set by exploiting the complementary information from variants of grey-level difference matrix and (4) analyse the performance of proposed method in comparison with existing feature extraction methods. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The results show that classification accuracy of 94.5% is obtained by optimal feature set having complementary information from variants of grey-level difference matrix. [ABSTRACT FROM AUTHOR]

Indirect tool condition monitoring in end milling is inevitable to produce high-quality finished products due to the complexity of end-milling process. Among the various indirect tool condition monitoring techniques, monitoring based on image processing by analyzing the surface images of final product is gaining high importance due to its non-tactile and flexible nature. The advances in computing facilities, texture analysis techniques and learning machines make these techniques feasible for progressive tool flank wear monitoring. In this article, captured end-milled surface images are analyzed using gray level co-occurrence matrix–based and discrete wavelet transform–based texture analyses to extract features which have a good correlation with progressive tool flank wear. Contrast and second diagonal moment are extracted from gray level co-occurrence matrix and root mean square and energy are extracted from discrete wavelet decomposition of end-milled surface images as features. Finally, these four features are utilized to build support vector machine–based regression models for predicting progressive tool flank wear with 94.8% average correlation between predicted and measured tool flank wear values. [ABSTRACT FROM AUTHOR]

Color texture classification as a part of fabric analysis is significant for textile manufacturing. In this research, a new artificial intelligence method based on a dual-side co-occurrence matrix and a back propagation neural network has been proposed for color texture classification, which could achieve relatively accurate classification results for yarn-dyed woven fabric compared with the traditional co-occurrence matrix for a single-side image. Firstly, a laboratory dual-side imaging system has been established to digitize the upper-side and lower-side images sequentially. Secondly, the dual-side co-occurrence matrix could be generated based on these dual images; four texture features could be extracted for the evaluation of the fabric texture characteristics. Thirdly, a well-trained back propagation neural network was established with the four defined features as the input vectors and the color texture type of yarn-dyed woven fabric as the output vector. The efficiency of two different classification systems based on a dual-side co-occurrence matrix and a single-side co-occurrence matrix has been compared systematically. Our experimental results show that the artificial intelligence system based on a dual-side co-occurrence matrix and back propagation neural network model could achieve a relatively better classification effect, with the high coefficient ratio (R = 0.9726) when d = 0. [ABSTRACT FROM AUTHOR]

The purpose of the study was to construct an efficient classifier that, along with a given reduced set of discriminant features, could be used as a part of the computer system in automatic identification and classification of ultrasound images of the thyroid gland, which is aimed to detect cases affected by Hashimoto's thyroiditis. A total of 10 supervised learning techniques and a majority vote for the combined classifier were used. Two models were proposed as a result of the classifier's construction. The first one is based on the K-nearest neighbours method (for K = 7). It uses three discriminant features and affords sensitivity equal to 88.1%, specificity of 66.7% and classification error at a level of 21.8%. The second model is a combined classifier, which was constructed using three-component classifiers. They are based on the K-nearest neighbours method (for K = 7), linear discriminant analysis and a boosting algorithm. The combined classifier is based on 48 discriminant features. It allows to achieve the classification sensitivity equal to 88.1%, specificity of 69.4% and classification error at a level of 20.5%. The combined classifier allows to improve the classification quality compared to the single model. The models, built as a part of the automatic computer system, may support the physician, especially in first-contact hospitals, in diagnosis of cases that are difficult to recognise based on ultrasound images. The high sensitivity of constructed classification models indicates high detection accuracy of the sick cases, and this is beneficial to the patients from a medical point of view. [ABSTRACT FROM AUTHOR]

Physiological modeling of retina plays a vital role in the development of high-performance image processing methods to produce better visual perception. People with normal vision have an ability to discern different colors. The situation is different in the case of people with color blindness. The aim of this work is to develop a human visual system model for detecting the level of perception of people with red, green and blue deficiency by considering properties like luminance, spatial and temporal frequencies. Simulation results show that in the photoreceptor, outer plexiform and inner plexiform layers, the energy and intensity level of the red, green and blue component for a normal person is proved to be significantly higher than for dichromats. The proposed method explains with appropriate results that red and blue color blindness people could not perceive red and blue color completely. [ABSTRACT FROM AUTHOR]