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Currently, staging the degree of liver fibrosis predominantly relies on liver biopsy, a method fraught with potential risks, such as bleeding and infection. With the rapid development of medical imaging devices, quantification of liver fibrosis through image processing technology has become feasible. Stacking technology is one of the effective ensemble techniques for potential usage, but precise tuning to find the optimal configuration manually is challenging. Therefore, this paper proposes a novel EVO-MS model—a multiple stacking ensemble learning model optimized by the energy valley optimization (EVO) algorithm to select most informatic features for fibrosis quantification. Liver contours are profiled from 415 biopsied proven CT cases, from which 10 shape features are calculated and inputted into a Support Vector Machine (SVM) classifier to generate the accurate predictions, then the EVO algorithm is applied to find the optimal parameter combination to fuse six base models: K-Nearest Neighbors (KNNs), Decision Tree (DT), Naive Bayes (NB), Extreme Gradient Boosting (XGB), Gradient Boosting Decision Tree (GBDT), and Random Forest (RF), to create a well-performing ensemble model. Experimental results indicate that selecting 3–5 feature parameters yields satisfactory results in classification, with features such as the contour roundness non-uniformity (Rmax), maximum peak height of contour (Rp), and maximum valley depth of contour (Rm) significantly influencing classification accuracy. The improved EVO algorithm, combined with a multiple stacking model, achieves an accuracy of 0.864, a precision of 0.813, a sensitivity of 0.912, a specificity of 0.824, and an F1-score of 0.860, which demonstrates the effectiveness of our EVO-MS model in staging the degree of liver fibrosis. [ABSTRACT FROM AUTHOR]

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

Edge detection in images is a vital application of image processing in fields such as object detection and identification of lesion regions in medical images. This problem is more complex in the domain of color images due to the combination of color layer information and the need to achieve a unified edge boundary across these layers, which increases the complexity of the problem. In this paper, a simple and effective method for edge detection in color images is proposed using a combination of support vector machine (SVM) and the social spider optimization (SSO) algorithm. In the proposed method, the input color image is first converted to a grayscale image, and an initial estimation of the image edges is performed based on it. To this end, the proposed method utilizes an SVM with a Radial Basis Function (RBF) kernel, in which the model's hyperparameters are tuned using the SSO algorithm. After the formation of initial image edges, the resulting edges are compared with pairwise combinations of color layers, and an attempt is made to improve the edge localization using the SSO algorithm. In this step, the optimization algorithm's task is to refine the image edges in a way that maximizes the compatibility with pairwise combinations of color layers. This process leads to the formation of prominent image edges and reduces the adverse effects of noise on the final result. The performance of the proposed method in edge detection of various color images has been evaluated and compared with similar previous strategies. According to the obtained results, the proposed method can successfully identify image edges more accurately, as the edges identified by the proposed method have an average accuracy of 93.11% for the BSDS500 database, which is an increase of at least 0.74% compared to other methods. [ABSTRACT FROM AUTHOR]

With the increase in urban rail transit construction, instances of tunnel disease are on the rise, and cracks have become the focus of tunnel maintenance and management. Therefore, it is essential to carry out crack detection in a timely and efficient manner to not only prolong the service life of the tunnel but also reduce the incidence of accidents. In this paper, the design and structure of a tunnel crack detection system are analyzed. On this basis, this paper proposes a new method for crack identification and feature detection using image processing technology. This method fully considers the characteristics of tunnel images and the combination of these characteristics with deep learning, while a deep convolutional network (Single-Shot MultiBox Detector (SSD)) is proposed based on deep learning for object detection in complex images. The experimental results show that the test set accuracy and training set accuracy of the support vector machine (SVM) in the classification comparison test are up to 88% and 87.8%, respectively; while the test accuracy of Alexnet's deep convolutional neural network-based classification and identification is up to 96.7%, and the training set accuracy is up to 97.5%. It can be seen that this deep convolutional network recognition algorithm based on deep learning and image processing is better and more suitable for the detection of cracks in subway tunnels. [ABSTRACT FROM AUTHOR]

The most significant aspect of promoting greenhouse productivity is the timely monitoring of disease spores and applying proactive control measures. This paper introduces a method to classify spores of airborne disease in greenhouse crops by using fingerprint characteristics of diffraction–polarized images and machine learning. Initially, a diffraction–polarization imaging system was established, and the diffraction fingerprint images of disease spores were taken in polarization directions of 0°, 45°, 90° and 135°. Subsequently, the diffraction–polarization images were processed, wherein the fingerprint features of the spore diffraction–polarization images were extracted. Finally, a support vector machine (SVM) classification algorithm was used to classify the disease spores. The study's results indicate that the diffraction–polarization imaging system can capture images of disease spores. Different spores all have their own unique diffraction–polarization fingerprint characteristics. The identification rates of tomato gray mold spores, cucumber downy mold spores and cucumber powdery mildew spores were 96.02%, 94.94% and 96.57%, respectively. The average identification rate of spores was 95.85%. This study can provide a research basis for the identification and classification of disease spores. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) image and medical image processing, which are considered big data analysis, have attracted significant attention during the last few years. To this end, efficient 3D object recognition techniques could be beneficial to such image and medical image processing. However, to date, most of the proposed methods for 3D object recognition experience major challenges in terms of high computational complexity. This is attributed to the fact that the computational complexity and execution time are increased when the dimensions of the object are increased, which is the case in 3D object recognition. Therefore, finding an efficient method for obtaining high recognition accuracy with low computational complexity is essential. To this end, this paper presents an efficient method for 3D object recognition with low computational complexity. Specifically, the proposed method uses a fast overlapped technique, which deals with higher-order polynomials and high-dimensional objects. The fast overlapped block-processing algorithm reduces the computational complexity of feature extraction. This paper also exploits Charlier polynomials and their moments along with support vector machine (SVM). The evaluation of the presented method is carried out using a well-known dataset, the McGill benchmark dataset. Besides, comparisons are performed with existing 3D object recognition methods. The results show that the proposed 3D object recognition approach achieves high recognition rates under different noisy environments. Furthermore, the results show that the presented method has the potential to mitigate noise distortion and outperforms existing methods in terms of computation time under noise-free and different noisy environments. [ABSTRACT FROM AUTHOR]

In recent years, physical violence detection has become a research hotspot in the area of human activity recognition. With the improvement and full coverage of surveillance systems, automatic physical violence detection becomes possible, which can continuously analyze human behavior in the scene, and greatly liberate human resources. This paper proposes a physical violence detecting method based on distributed surveillance cameras. The cameras capture images, and extract human bone models with an improved OpenPose model. All the surveillance cameras form an Ad hoc network, and transfer the extracted bone models to the monitoring center. Aiming at the problem of missing bone points caused by occlusion, this paper proposes a key point filling algorithm to improve the bone models. Then the monitoring center extracts morphological features and dynamic features from the improved bone models, and filters them with an improved Relief-F algorithm. SVM (Support Vector Machine) performs classification, and gets an average recognition accuracy of 94.2%. [ABSTRACT FROM AUTHOR]

Identification through image processing has been used in various fields, including product examination and more recently infrastructure inspection, such as identifying cracks in structures. The conventional method, in which a human inspector uses a crack gauge or determines the size through a visual evaluation, results in a subjective evaluation of the extent of concrete life. Currently, an operator inspects the concrete structure by visually examining the concrete surface. However, this method presents numerous problems, including when the inspector has to work in dangerously high places. Therefore, we used machine learning to extract the features of cracks in image processing. We identified that non-arbitrary features, such as color-related features, are also important. Because concrete appears monochromatic, it is difficult for humans to analyze this color-related feature. We compared these newly obtained machine learning features with the previously used arbitrary features and confirmed that the machine learning features were more accurate in terms of detection. We also compared the generation of discriminators based on these features with a fixed threshold for discrimination and the utilization of support vector machine (SVM) and other machine learning methods. In this paper, two issues are discussed: an analysis of the effectiveness of machine learning and SVM-based discriminant generation in detecting cracks, and the classification results based on crack width. Crack width classification using machine learning is useful when sufficient image resolution is not available. The final detection accuracy of the new method was 11.7% better than that of the method using arbitrary features; moreover, the false detection rate was also higher in the proposed method. We further attempted to classify these cracks in accordance with their damage levels. To evaluate the degree of damage, we focused on the difference in the width of the cracks and extracted different features in three classes on the basis of the different crack widths, and were able to classify these clacks. • We used machine learning to extract the features of cracks. We identified that non-arbitrary features, such as color-related features, are also important. Because concrete is apparently monochromatic, it is difficult for humans to analyze this color-related feature. We compared these newly obtained machine learning features with the previously used arbitrary features and confirmed that the machine learning features were more accurate in detection.. • We compared the generation of discriminators based on these features with a fixed threshold for discrimination and the utilization of support vector machine (SVM). The final detection accuracy of the new method was 11.7% better than that of the method using arbitrary features; moreover, the false-positive rate was also higher in the proposed method.. • We attempted to classify these cracks according to their damage levels. To evaluate the degree of damage, we focused on the difference in the width of the cracks and extracted different features in three classes based on the different crack widths. In this paper, the following two issues are discussed: the first is an analysis of the effectiveness of machine learning and SVM-based discriminant generation in detecting cracks, and the second is the classification results based on crack width. Finally, we evaluated the degree of damage by classifying the cracks based on crack width using machine learning.. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to propose a novel methodology for classifying the gestures using support vector machine (SVM) classification method. Initially, the Red Green Blue color hand gesture image is converted into YCbCr image in preprocessing stage and then palm with finger region is segmented by threshold process. Then, distance transformation method is applied on the palm with finger segmented image. Further, the center point (centroid) of palm region is detected and the fingertips are detected using SVM classification algorithm based on the detected centroids of the detected palm region. Design/methodology/approach: Gesture is a physical indication of the body to convey information. Though any bodily movement can be considered a gesture, generally it originates from the movement of hand or face or combination of both. Combined gestures are quiet complex and difficult for a machine to classify. This paper proposes a novel methodology for classifying the gestures using SVM classification method. Initially, the color hand gesture image is converted into YCbCr image in preprocessing stage and then palm with finger region is segmented by threshold process. Then, distance transformation method is applied on the palm with finger segmented image. Further, the center point of the palm region is detected and the fingertips are detected using SVM classification algorithm. The proposed hand gesture image classification system is applied and tested on "Jochen Triesch," "Sebastien Marcel" and "11Khands" data set hand gesture images to evaluate the efficiency of the proposed system. The performance of the proposed system is analyzed with respect to sensitivity, specificity, accuracy and recognition rate. The simulation results of the proposed method on these different data sets are compared with the conventional methods. Findings: This paper proposes a novel methodology for classifying the gestures using SVM classification method. Distance transform method is used to detect the center point of the segmented palm region. The proposed hand gesture detection methodology achieves 96.5% of sensitivity, 97.1% of specificity, 96.9% of accuracy and 99.3% of recognition rate on "Jochen Triesch" data set. The proposed hand gesture detection methodology achieves 94.6% of sensitivity, 95.4% of specificity, 95.3% of accuracy and 97.8% of recognition rate on "Sebastien Marcel" data set. The proposed hand gesture detection methodology achieves 97% of sensitivity, 98% of specificity, 98.1% of accuracy and 98.8% of recognition rate on "11Khands" data set. The proposed hand gesture detection methodology consumes 0.52 s as recognition time on "Jochen Triesch" data set images, 0.71 s as recognition time on "Sebastien Marcel" data set images and 0.22 s as recognition time on "11Khands" data set images. It is very clear that the proposed hand gesture detection methodology consumes less recognition rate on "11Khands" data set when compared with other data set images. Hence, this data set is very suitable for real-time hand gesture applications with multi background environments. Originality/value: The modern world requires more numbers of automated systems for improving our daily routine activities in an efficient manner. This present day technology emerges touch screen methodology for operating or functioning many devices or machines with or without wire connections. This also makes impact on automated vehicles where the vehicles can be operated without any interfacing with the driver. This is possible through hand gesture recognition system. This hand gesture recognition system captures the real-time hand gestures, a physical movement of human hand, as a digital image and recognizes them with the pre stored set of hand gestures. [ABSTRACT FROM AUTHOR]

The problem of 3D digital object invariability is encountered in image processing, especially in pattern classification/recognition. The 3D object should be correctly recognized regardless of its particular position and orientation in the scene. This paper proposes a new method to extract 3D Charlier moment invariants to translation and scaling (3DCMITS). These descriptors are extracted directly from discrete orthogonal Charlier polynomials without using 3D geometric moment invariants. This method is fast and does not require any numerical approximation compared to the indirect method based on 3D geometric moment invariants. The results show the proposed method's effectiveness in terms of speed with an improvement exceeding 99,97%. For validation purposes and as an illustration of the interest of 3DCMITS, this paper offers a classification system for 3D objects based on the proposed 3DCMITS and Support Vector Machine (SVM) classifier. The obtained results are verified with K-Nearest Neighbor (KNN) classifier and other existing works in the literature. [ABSTRACT FROM AUTHOR]

To solve the problem that the traditional hyperspectral image classification method cannot effectively distinguish the boundary of objects with a single scale feature, which leads to low classification accuracy, this paper introduces the idea of guided filtering into hyperspectral image classification, and then proposes a multi-scale guided feature extraction and classification (MGFEC) algorithm for hyperspectral images. Firstly, the principal component analysis theory is used to reduce the dimension of hyperspectral image data. Then, guided filtering algorithm is used to achieve multi-scale spatial structure extraction of hyperspectral image by setting different sizes of filtering windows, so as to retain more edge details. Finally, the extracted multi-scale features are input into the support vector machine classifier for classification. Several practical hyperspectral image datasets were used to verify the experiment, and compared with other spectral feature extraction algorithms. The experimental results show that the multi-scale features extracted by the MGFEC algorithm proposed in this paper are more accurate than those extracted by only using spectral information, which leads to the improvement of the final classification accuracy. This fully shows that the proposed method is not only effective, but also suitable for processing different hyperspectral image data. [ABSTRACT FROM AUTHOR]

Aim: The main objective of this research article is to employ the detection of heart disease by using Support vector machine (SVM) classifier in comparison with Linear regression (LR) model. Materials & Methods: The dataset used in this paper was collected from the UCI machine learning repository database. The sample size for the detection of heart disease was sample 60 (Group 1=30 and Group 2 =30) and calculation was performed utilizing G-power 0.8 with alpha and beta qualities of 0.05, 0.2 with a confidence interval of 95%. The detection of heart disease is performed by the Support Vector Machine (SVM) classifier with a number of samples (N=30) and Linear regression (LR) model with a number of samples (N=30). Results: The Support vector machine (SVM) classifier has a 90.43 percent higher accuracy rate when compared to the accuracy rate of the Linear regression (LR) model is 78.56 percent. The study has a significance value of p=0.021. Conclusion: Support vector machine (SVM) classifier provides better outcomes in accuracy rate when compared to Linear regression (LR) model for detection of heart disease. [ABSTRACT FROM AUTHOR]

This paper presents a turnkey integrated system that can be operated in real time for real textile manufacturers. Eight types of defects in woven fabric, including stain, broken end, broken weft, hole, nep, double pick, kinky weft and float can be recognized and classified. First, an image is captured by a CMOS industrial camera with a pixel size of 4600 × 600 above the batcher at 20 m/min. After that, the four-stage image processing procedure is applied to detect defects and for classification. Stage 1 is image pre-processing; the filtration of the image noise is carried out by a Gaussian filter. The light source is corrected to reduce the uneven brightness resulting from halo formation. The improved mask dodging algorithm is used to reduce the standard deviation of the corrected original image. Afterwards, the background texture is filtered by an averaging filter, and the mean value is corrected for histogram shifting, so that this system is robust to the texture and color changes of woven fabric. The binary segmentation threshold is determined using the mean value and standard deviation of an image with a normal sample. Stage 2 uses adaptive binarization for separation of the background and defects and to filter the noise. In Stage 3, the morphological processing is used before the defect contour is circled, i.e., four features of each block, including the defect area, the aspect ratio of the defect, the average gray level of the defect and the defect orientation, which are calculated according to the range of contour. The image defect recognition dataset consists of 2246 images. The results show that the detection success rate is 96.44%, and the false alarm rate is 3.21%. In Stage 4, the defect classification is implemented. The support vector machine (SVM) is used for classification, 230 defect images are used as training samples, and 206 are used as test samples. The experimental results show that the overall defect recognition rate is 96.60%, providing that the software and hardware equipment designed in this study can implement defect detection and classification for woven fabric effectively. [ABSTRACT FROM AUTHOR]

Traditional machine learning requires users to have a strong ability to control features and distance calculation formulas, especially in the use of support vector machine SVM and nearest neighbor KNN. Traditional machine learning uses PCA in feature extraction will actually lead to Information is lost. In order to solve the problem of low optical film damage detection rate of traditional methods, a new method is proposed in this paper based on a convolutional neural network instead of traditional machine learning to classify CCD images with different damage degrees of SiO2 film and K9 glass. First, film images are collected by online CCD, and the proposed algorithm is designed to extract the image characteristic parameters of the film microscopic images, filter denoising, and run binarization to analyze film images. Second, gray values of images are extracted and classified by unsupervised learning. Finally, the film microscopic images under the microscope are analyzed. The experimental results show that the defect positions on the images can be detected after the images are detected and processed by a convolution neural network, binarization, and connected domains. The defective parts can be intercepted from the images, and the data related is saved for damage type determination. The average classification rate is over 99%, which is better than the traditional method by 9.1%. Therefore, it has a high application value. [ABSTRACT FROM AUTHOR]

• Representative SVM and CNN algorithms in traditional machine learning and deep learning for research. • Under other conditions being the same, the data sets are different. The impact of the results varies. • This article compares and analyzes the accuracy and running time. Image classification is a hot research topic in today's society and an important direction in the field of image processing research. SVM is a very powerful classification model in machine learning. CNN is a type of feedforward neural network that includes convolution calculation and has a deep structure. It is one of the representative algorithms of deep learning. Taking SVM and CNN as examples, this paper compares and analyzes the traditional machine learning and deep learning image classification algorithms. This study found that when using a large sample mnist dataset, the accuracy of SVM is 0.88 and the accuracy of CNN is 0.98; when using a small sample COREL1000 dataset, the accuracy of SVM is 0.86 and the accuracy of CNN is 0.83. The experimental results in this paper show that traditional machine learning has a better solution effect on small sample data sets, and deep learning framework has higher recognition accuracy on large sample data sets. [ABSTRACT FROM AUTHOR]

Rice, which has the highest production and consumption rates worldwide, is among the main nutrients in terms of being economical and nutritious in our country as well. Rice goes through some stages of production from the field to the dinner tables. The cleaning phase is the separation of rice from unwanted materials. During the classification phase, solid ones and broken ones are separated and calibration operations are performed. Finally, in the process of extraction based on color features, the striped and stained ones other than the whiteness on the surface of the rice grain are separated. In this paper, five different varieties of rice belonging to the same trademark were selected to carry out classification operations using morphological, shape and color features. A total of 75,000 rice grain images, including 15,000 for each varieties, were obtained. The images were pre-processed using MATLAB software and prepared for feature extraction. Using a combination of 12 morphological, 4 shape features and 90 color features obtained from five different color spaces, a total of 106 features were extracted from the images. For classification, models were created with algorithms using machine learning techniques of knearest neighbor, decision tree, logistic regression, multilayer perceptron, random forest and support vector machines. With these models, performance measurement values were obtained for feature sets of 12, 16, 90 and 106. Among the models, the success of the algorithms with the highest average classification accuracy was achieved 97.99% with random forest for morphological features. 98.04% were obtained with random forest for morphological and shape features. It was achieved with logistic regression as 99.25% for color features. Finally, 99.91% was obtained with multilayer perceptron for morphological, shape and color features. When the results are examined, it is observed that with the addition of each new feature, the success of classification increases. Based on the performance measurement values obtained, it is possible to say that the study achieved success in classifying rice varieties. [ABSTRACT FROM AUTHOR]

Automated examination of biomedical signals plays a vital role to diagnose diseases and offers useful data to several applications in the areas of physiology, sports medicine, and human–computer interface. The latest advancements in Artificial Intelligence (AI) have the ability to manage and analyse enormous biomedical datasets resulting in clinical decision making and real time applications. At the same time, Colorectal cancer (CRC) is the third most deadly disease affecting people over the globe. The utilization of AI techniques for the earlier identification of CRC has gained significant interest among the research communities. Therefore, this paper presents a novel AI based fusion model for CRC disease diagnosis and classification, named AIFM‐CRC. The presented AIFM‐CRC model primarily undergoes Gaussian filtering based noise removal and contrast enhancement as a preprocessing stage. In addition, a fusion based feature extraction process takes place where the SIFT based handcrafted features and Inception v4 based deep features are fused together. Besides, whale optimization algorithm tuned deep support vector machine model is employed as a classification technique to determine the existence of CRC. In order to highlight the proficient results analysis of the AIFM‐CRC model, a comprehensive simulation analysis takes place. The resultant experimental values pointed out the betterment of the AIFM‐CRC model by accomplishing a maximum accuracy of 96.18%. [ABSTRACT FROM AUTHOR]

Salient object detection is a significant task that forms the basis for many image processing and computer vision applications. In recent years, the research in this area is being extended beyond RGB images to applications involving multispectral and hyperspectral images. But most of the existing algorithms for salient object detection often yield an incomplete representation of the object and often produce saliency maps with blurred edges. In this paper, we propose an efficient hyperspectral image salient object detection method through anomaly detection by combining deep learning autoencoders with one-class support vector machines. Here, the saliency detection problem in hyperspectral images is formulated as an unsupervised deep background spectral reconstruction-based anomaly detection. Our proposed method first employs deep autoencoders to model the background of an input hyperspectral image in terms of spectral reconstruction residuals of the autoencoders and then detect the salient objects from the image through a one-class support vector machine-based anomaly detection. The proposed method was evaluated on a publicly available hyperspectral image dataset for salient object detection. The experimental results show that our proposed method is found to be more efficient and superior over other previous methods in terms of various performance measures. [ABSTRACT FROM AUTHOR]

Monitoring the degradation state of hydraulic pumps is of great significance to the safe and stable operation of equipment. As an important step, feature extraction has always been challenging. The non-stationary and nonlinear characteristics of vibration signals are likely to weaken the performance of traditional features. The two-dimensional image representation of vibration signals can provide more information for feature extraction, but it is challenging to obtain sufficient information based on small-size images. To solve these problems, a method for feature extraction based on modified hierarchical decomposition (MHD) and image processing is proposed in this paper. First, a set of signals decomposed by MHD are converted into gray-scale images. Second, features from accelerated segment test (FAST) algorithm are applied to detecting the feature points of the gray-scale image. Third, the real part of Gabor filter bank is used to convolve the images, and the responses of feature points are used to calculate histograms that are regarded as feature vectors. The method for feature extraction fully acquires the multi-layered texture information of small-size images and removes the redundant information. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the proposed method are validated using experimental data, and the results show that the highest recognition rate of our proposed method can reach 100%. The results of the comparison among the proposed method, local binary pattern (LBP), and one-dimensional ternary patterns (1D-TPs) certify the superiorities of the proposed method. It obtains the highest classification accuracy (99.7%–98%) and the lowest feature set dimension (13–10). [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have been widely used in the visual inspection of structural cracks. However, blurry images are inevitably generated during image collecting by UAVs, as they are caused by the motion of UAVs and other factors. This blur affects the retrieval of crack properties from images and degrades the accuracy and reliability of crack damage assessment. At present, blur detection and blurred image removal are mainly achieved manually, which is inefficient and fallible, especially for large image sets. To address this problem, a novel automatic blur detection method for UAV crack image data sets is proposed. This algorithm defines a blur detection metric named the edge average width difference (EAWD), which is based on the principle of a smaller difference between pixels of a more blurred image. Moreover, it is combined with the characteristics of the crack image itself. By calculating this metric and comparing it with other EAWD values from the same data set, the crack images are judged to be blurred or not. Furthermore, a support vector machine classifier is applied to the aforementioned metrics, serving as the image blur quality evaluator. For proper training and assessment of the proposed approach, an image data set consisting of 1,200 crack images is created, which also contains some thin crack images. Several experimental results are provided in this paper to demonstrate that the proposed method is fast, accurate, and reliable. [ABSTRACT FROM AUTHOR]

This paper reports a study which utilizes deep learning for automated detection of the symptoms of diseases on vine leaves. Vine fruits or grapes are very important and have existed in Syria and surrounding areas (e.g., Turkey) for many years. Quality of vine fruits is also very important in grape production as it is consumed in these areas every day. The aim of this study is to improve diseasedetection accuracy in vine leaves and to develop a system to help Syrian and Turkish farmers and agricultural engineers to maintain the quality of grape production. In this study, over 1000 images of vine leaves have been collected from vine yards in Syria and the internet. These images are processed using MATLAB 2018B, Deep Learning Toolbox including convolutional neural networks (CNNs) with AlexNet, GoogleNet and ResNet-18. A standard transfer learning (TL) algorithm is also used with CNNs, whereas a multiclass support vector machine (SVM) is used with AlexNet, whilst GPU and CUDA are used for accelerating the process of the disease detection for vine leaves. A software system has been created that enables the automatic and efficient detection of nine types of leaf diseases and the identification of healthy leaves. Experimental studies showed that the total detection accuracy of this system reaches 92.5%, 87.4% and 85.0%, 85.1% when AlexNet+TL, ResNet-18+TL, GoogleNet+TL and AlexNet+SVM are used respectively. This smart agricultural application can provide early identification of the symptoms of grape diseases on leaves and thus help maintain the quality of vine fruits. [ABSTRACT FROM AUTHOR]

This work researched apple quality identification and classification from real images containing complicated disturbance information (background was similar to the surface of the apples). This paper proposed a novel model based on convolutional neural networks (CNN) which aimed at accurate and fast grading of apple quality. Specific, complex, and useful image characteristics for detection and classification were captured by the proposed model. Compared with existing methods, the proposed model could better learn high-order features of two adjacent layers that were not in the same channel but were very related. The proposed model was trained and validated, with best training and validation accuracy of 99% and 98.98% at 2590th and 3000th step, respectively. The overall accuracy of the proposed model tested using an independent 300 apple dataset was 95.33%. The results showed that the training accuracy, overall test accuracy and training time of the proposed model were better than Google Inception v3 model and traditional imaging process method based on histogram of oriented gradient (HOG), gray level co-occurrence matrix (GLCM) features merging and support vector machine (SVM) classifier. The proposed model has great potential in Apple's quality detection and classification. [ABSTRACT FROM AUTHOR]

In this paper, the energy characteristics of Gabor texture are used for cloud detection in high-resolution multispectral images. First, the satellite remote-sensing image is divided into superpixels using simple linear iterative clustering (SLIC), and then, the energy characteristics of Gabor texture and spectral characteristics are computed by extracting the texture features of the superpixels. The features of the cloud superpixels are used as the learning sample of the support vector machine (SVM) classifier, and a classification model is obtained by training the SVM classifier. Finally, a cloud-detection experiment is conducted for various sensor images with three visible bands and one near-infrared band. The experimental results showed that the proposed method provides an excellent average overall accuracy for thick and thin clouds in a complex background of forests, harbours, snow and mountains. The characteristic parameters of this paper are not limited by the image parameters; thus, they provide good results and universality for various types of sensors. [ABSTRACT FROM AUTHOR]

With the rapidly increasing demand for security and E-health applications, device-free human detection has attracted interest because it does not require a wearable device or camera setup. This paper proposes a deep-learning-based approach that monitors wireless signals to learn three human modes, i.e., absence, working, and sleeping, in realistic indoor environments. This paper integrates the amplitude and phase of channel state information to propose a hybrid complex feature; this facilitates robust and efficient human detection even with fewer data samples. Experiments conducted in two unmodified WiFi networks demonstrate the effectiveness of the proposed algorithms. Four machine-learning algorithms provide satisfactory performance with sufficient data, and deep neural networks perform the best. Results show that by using 6% training samples, the proposed hybrid feature still achieves 93% accuracy and can even outperform three typical machine learning algorithms that use full training samples. Moreover, the proposed feature significantly improves detection accuracy by 11.62%–27.76% than traditional amplitude feature with fewer training samples. [ABSTRACT FROM AUTHOR]

We present an approach to extend the functionality and the use of kernel machines in image processing applications. We introduce a novel way to design spatial kernel machines with spatial properties and demonstrate how those newly introduced spatial properties enhance the possibilities of the use of kernel machines in image processing applications as a proof of concept. In this paper, we demonstrate four particular extensions: 1) how to model shapes efficiently with spatially computed kernel parameters in a geometrically scalable way; 2) how to visualize the kernel parameters precisely and intuitively on binary 2D shapes; 3) how to construct a one-class classifier from the binary classifier in a straightforward manner without re-training; and 4) how to use the computed kernel parameters for filtering. The existing literature on kernel machines mostly focuses on estimating the optimal kernel parameters via additional cost function(s). In this paper, instead of employing an additional cost function to estimate the kernel-related parameters, we investigate on an analytical solution to predict the actual kernel parameters locally and show how to build a spatial kernel machine with our analytical approach. Classical kernel machines do not perform well on precise shape modeling with a low number of support vectors as demonstrated in this paper. However, we demonstrate and visualize that our analytical approach provides a natural means to relate the kernel parameters to the 2D shapes for sparse shape modeling, where the shape boundary represents the decision boundary. For that, we incorporate the selected kernel function’s geometric properties as an additional constraint into the classifier’s optimization problem by defining an easy-to-explain and intuitive concept: similarity domains. In our experiments, we study and demonstrate how the resulting new kernel machine enhances the capabilities of the classical kernel machines with applications on shape modeling, (geometrically) scaling the non-linear decision boundary at various scales and precise visualization of the kernel parameters in 2D images. [ABSTRACT FROM AUTHOR]

A support vector machine (SVM) is not a popular method for a very large dataset classification because the training and testing time for such data are computationally expensive. Many researchers try to reduce the training time of SVMs by applying sample reduction methods. Many methods reduced the training samples by using a clustering technique. To reduce its high computational complexity, several data reduction methods were proposed in previous studies. However, such methods are not effective to extract informative patterns. This paper demonstrates a new supervised classification method, multiseed-based SVM (MSB-SVM), which is particularly intended to deal with very large datasets for multiclass classification. The main contributions of the paper are (i) an efficient multiseed technique for selection of seed points from circular/elongated class training samples, (ii) adjacent class pair selection from the set of multiseeds by using the minimum spanning tree, and (iii) extraction of support vectors from class pair seed equivalent regions to manage multiclass classification problems without being computationally expensive. Experimental results on a variety of datasets showed better performance compared to other sample-reducing methods in terms of training and testing time. Traditional support vector machine (SVM) solution suffers from O(n²) time complexity, which makes it impractical for very large datasets. Here, multiseed point technique depends on the estimated density of each data, and the order of computation is O(n log n). Using the estimated density, the computational cost of the seed selection algorithm is O(n). So, this is the only burden for reducing the sample. However, reducing the sample takes less time with the proposed algorithm compared to the clustering methods. At the same time, the number of support vectors has been abruptly reduced, which takes less time to find the decision surface. Apart from this, the classification accuracy of the proposed technique is significantly better than other existing sample reduction methods especially for large datasets. [ABSTRACT FROM AUTHOR]

• This paper developed a circular saw core localization system using machine vision. • A perspective transformation method is applied to transform the captured oblique view image into a vertical view image. • The Hough transform is used to detect the center of the circular saw. • Maximum overlapping region algorithm is used to obtain the accurate position of the circular saw on the quench platen. The quenching of the core of a circular saw determines the stress, hardness and flatness of the saw, which greatly affect its cutting performance. We have designed a new heat-treatment production line to model the quenching process, in which the quenching performance is determined by the position of the circular saw core on the quenching platen. In this short communication, we propose a circular saw core localization system based on machine vision that can be applied during the quenching process. In the proposed system, a perspective transformation method is applied to transform the captured oblique-view image into a vertical-view image. Then, a support vector machine is used to classify two types of circular saws. An image processing algorithm is applied to extract the circular saw core from the vertical-view image. The Hough transform is used to detect the center of the circle. Finally, the maximum overlapping region algorithm is used to obtain the accurate position of the circular saw on the quenching platen. To verify the robustness and universality of the circular saw localization system, we tested circular saw cores under different conditions. The test results show that in a real quenching scenario, the proposed system exhibits high localization accuracy and universality. This system can be effectively applied for quenching process monitoring in industry. [ABSTRACT FROM AUTHOR]

In medical image processing, the detection, classification and segmentation of the tumor region from MRI scans accurately are very complicated, significant and time-consuming process. When there is a scenario occurs to handle with large amount of images for tumor diagnosis, there is need of an efficient and adaptive classification model to handle with the anomalous structures of human brains. The MRI brain images show the typical internal brain structure and hence help scholars and medical practitioners in accurate disease diagnosis. With that note, this paper develops a model called improved classification model for brain tumor diagnosis for appropriate classification of tumor images from input MRI images. Initially, filtering techniques are applied for preprocessing the acquired scan images and feature extraction is done with gray-level co-occurrence matrix and discrete wavelet transform equations, which produces more precise results. And, classification is done with the technique called support vector machine, in which the binary classifications are effectively done. The proposed model is evaluated under simulation, and the obtained results outperform the results of traditional brain tumor detection process based on precision, recall and processing time. [ABSTRACT FROM AUTHOR]

Local ternary pattern (LTP) is a simple, yet high-discriminative texture feature model. In order to further promote the performance of LTP in noisy texture classification, this paper proposes a multi-scale median LTP (MLTP) framework. It firstly designs a non-overlapped median sampling scheme to resist against noise, and then re-defines central descriptor, radial descriptor and magnitude descriptor for MLTP. Moreover, it also proposes the pattern mapping solution named rotation-invariant uniform three (riu3) so as to project original MLTP codes from high-dimensional patterns to low-dimensional ones. In our multi-scale algorithm, every image is sampled and encoded separately by three MLTP descriptors, and then original patterns are mapped by a pre-stored riu3 pattern lookup table. Finally, the frequency histogram of joint distribution on three mapped MLTP code images is computed to generate the feature vector of given sampling scale. The different vectors under different scales are concatenated together to produce the objective vectors for training classifier or texture classification. The available classifiers for our algorithm include nearest neighbor classifier (NNC), and support vector machine (SVM), etc. The experiments on five publicly-available texture databases show that the multi-scale MLTP method proposed in this paper could obtain an average accuracy of 99.48% on dataset OTC12, 96.97% on UIUC, 98.71% on KTH-TIPS2b, 98.55% on Brodatz and 97.49% on ALOT. Compared with other state-of-the-art approaches, under the low-intensity conditions of Gaussian, Salt & Pepper and random pixel corruption noise, multi-scale MLTP + SVM could still keep higher accuracy and have better noise robustness than others. [ABSTRACT FROM AUTHOR]

Analyzing geological drilling hole images acquired by Axial View Panoramic Borehole Televiewer (APBT) is a key step to explore the geological structure in a geological exploration. Conventionally, the borehole images are examined by technicians, which is inefficient and subjective. In this paper, three dominant types of borehole-wall images on coal-rock mass structure, namely, border images, fracture images and intact rock mass images are mainly studied. The traditional image classification methods based on unified feature extraction algorithm and single classifier is not effect for the borehole images. Therefore, this paper proposes a novel two-stage classification approach to improve the classification performance of borehole images. In the first-stage classification, the border images are identified from three kinds of images based on texture features and gray-scale histograms features. For the remaining two types of images, in the second-stage classification, Gabor filter is first applied to segment the region of interest (ROI) (such as microfracture, absciss layer and horizontal cracks, etc.) and the central interference region. Then, using the same feature vector after eliminating the central interference region, fracture images are separated from intact rock mass images. We test our two-stage classification system with real borehole images. The results of experimental show that the two-stage classification method can effectively classify three major borehole-wall images with the correction rate of 95.55% in the first stage and 95% in the second stage. [ABSTRACT FROM AUTHOR]

The current detection technology for vegetable pests mainly relies on artificial statistics, which exists many shortages such as requiring a large amount of labor, low efficiency, feedback delay and artificial faults. By rapid detection and image processing technology targeting at vegetable pests, not only can reduce manpower and pesticide use, but also provide decision support for precise spraying and improve the quality of vegetables. Practical research achievements are still relatively lacking on the rapid identification technology based on image processing technology in vegetable pests. Given the above background, this paper presents a classification and recognition scheme based on the bag-of-words model and support vector machine (BOF-SVM) on four important southern vegetable pests including Whiteflies, Phyllotreta Striolata, Plutella Xylostella and Thrips. This paper consists of four sub-algorithms. The first sub-algorithm is to compute the character description of pest images based on scale-invariant feature transformation. The second sub-algorithm is to compute the visual vocabulary based on bag of features. The third sub-algorithm is to compute the classifier of pests based on support vector machines. The last one is to classify the pest images using the classifier. In this study, C++ and Python language were used as implementation technologies with OpenCV and LibSVM function library based on BOF-SVM classification algorithm. Experiments showed that the average recognition accuracy was 91.56% for a single image category judgment with 80 images from the real environment, and the average time was 0.39 seconds. This algorithm has achieved the ideal operating speed and precision. It can provide decision support for UAV precise spraying, and also has good application prospect in agriculture. [ABSTRACT FROM AUTHOR]

In this paper an exploratory classification, so called open set problem, is investigated. Open set recognition assumes there is incomplete knowledge of the world at training time, and unknown classes can be submitted to an algorithm during testing. For this problem we elaborated a theoretical model, Double Probability Model (DPM), based on likelihoods of a classifier.We developed it with double smoothing solution in order to solve technical difficulties avoiding zero values in the predictions. We applied the GMM based Fisher vector for the mathematical representation of the images and the C-SVC with RBF kernel for the classification. The last contributions of the paper are new goodness indicators for classification in open set problem, the new type of accuracies. The experimental results present that our Double Probability Model helps with classification, the accuracy increases by using our proposed model. We compared our method to a state-of-the-art open set recognition solution and the results showed that DPM outperforms existing techniques. [ABSTRACT FROM AUTHOR]

This paper presented a non-destructive approach for detection of intact and crack eggs using transmission imaging combined with support vector machine classifier. Two hundred brown chicken eggs, including 100 intact and 100 crack eggs were collected as samples. Transmission vision system was developed to capture the sample images. Green color component and edge algorithm based on confidence were then used to transform the color image into edge image for next analysis. Features (mean, variance and third moment) characterizing the differences between crack and intact eggs were extracted through analyzing projection functions as input vectors of the detection model. The detection model in this paper was conducted by support vector machine (SVM). Cracked and Intact eggs could be distinguished by SVM using the statistics parameters. Experimental results showed that the overall identification accuracy in training and test sets were 94% and 93% using 10-fold cross validation approach, respectively. [ABSTRACT FROM AUTHOR]

Given a single outdoor image, we propose a collaborative learning approach using novel weather features to label the image as either sunny or cloudy. Though limited, this two-class classification problem is by no means trivial given the great variety of outdoor images captured by different cameras where the images may have been edited after capture. Our overall weather feature combines the data-driven convolutional neural network (CNN) feature and well-chosen weather-specific features. They work collaboratively within a unified optimization framework that is aware of the presence (or absence) of a given weather cue during learning and classification. In this paper we propose a new data augmentation scheme to substantially enrich the training data, which is used to train a latent SVM framework to make our solution insensitive to global intensity transfer. Extensive experiments are performed to verify our method. Compared with our previous work and the sole use of a CNN classifier, this paper improves the accuracy up to 7-8 percent. Our weather image dataset is available together with the executable of our classifier. [ABSTRACT FROM PUBLISHER]

The goal of computer vision is to identify objects of interest from images. Copy-move forgery is the process of copying one or more parts of an image and moved into another part of an equivalent image. The detection of copy-move images has many limitations in recognition of objects. In this paper, the proposed work uses Speeded Up Robust Feature (SURF) feature extraction, and the specific object is recognized with the help of the support vector machine. When copy-move forgery was performed, some modifications were done to the image. For instance, turning, scaling, darkening, compression, and noise addition are applied to make effective impersonation forgeries. Here, feature matching process uses the image rotate function, which consists of bicubic and crop operations, and calculates the difference using the blend, scale and joint operation. The results show that forged images are extracted from a given set of test images. The test results exhibit that the proposed technique can get noteworthy and impressive results. [ABSTRACT FROM AUTHOR]

Deep belief networks (DBNs) are used in many applications such as image processing and pattern recognition. But the data vectorized resulting in the loss of high-dimensional data and valuable spatial information. The classical DBNs model is based on restricted Boltzmann machines (RBMs) and full connectivity between the visible units and the hidden units. It requires a large number of parameters to be trained using an army of training samples. However, it is difficult to obtain so many training samples in reality. To solve this problem, this paper proposes a matrix-variate deep belief networks (MVDBNs) model which is created from matrix variate RBMs whose parameter is restricted as canonical polyadic (CP) decomposition. MVDBNs are composed of two or more matrix-variate restricted Boltzmann machines (MVRBMs) whose input and latent variables are in matrix form. The MVDBNs have much fewer model parameters and deeper layer with better features to avoid overfitting and more accurate model easier to be learned. We demonstrate the capacity of MVDBNs on handwritten digit classification and medical image processing. We also extend MVRBMs to a multimodal case for image super-resolution. [ABSTRACT FROM AUTHOR]

Presently, the fruit industry requires a fast and efficient method for classification and recognition of the quality of fruits in bulk processing. Fruit recognition based on computer vision is quite challenging as it is based on the intensity, size, contour, and texture features extraction from fruits along with their suitable classifier selection. In this paper, the pixels containing the defected regions are segmented and their features are extracted. Further, a support vector machine (SVM) classifier is used to identify the defects and recognizes the cause with its stage. During the process of classification, fruits are categorized into two groups, defected and no-defect. The sample image observed defected further classified into three categories as the first, second and final stage of fruit defect. The sample testing at an early stage helps one to further proceed with the production or halt based on the outcome of a computer vision-based recognition system. [ABSTRACT FROM AUTHOR]

Video-based human motion analysis is an important research direction in the field of computer vision. It detects moving objects from video sequences, extracts key parts of the human body, and obtains useful information for human movements. In this paper, the joints of the teeing arm are first color-coded. The tennis teeing video is collected by a high-speed camera. The coordinates of the tacking points in each frame are used instead of the knuckles to study the trajectory of the teeing arm. In the process of video processing, after constructing a dictionary for a series of noise maps, the sparse representation idea was used to reconstruct an interference-free service diagram, and a mixture of Gaussian background modeling was used to extract the foreground of the motion. After obtaining the motion foreground, the marker points are extracted through the color features, and binarization operations are performed on the marker points. Next, the outline of the marker points is searched, the outline is surrounded by the minimum circle, and the returned circle center coordinates are used as the joint point coordinates. Taking the trajectory of the shoulder marking point as a research object, a tennis serving model based on an improved support vector machine was established. [ABSTRACT FROM AUTHOR]

The paper aims to study the identification and diagnosis of infrared thermal fault of airborne circuit board of equipment, expand the application of intelligent algorithm in infrared thermal fault diagnosis, and promote the development of computer image processing technology and neural network technology in the field of thermal diagnosis. Taking the airborne circuit board in the boiler plant as the research object, first, the sequential analysis method was selected to collect the temperature changes during the operation of the circuit board. Second, on the basis of convolutional neural network, the program was written in Python, and the Relu function was used as the activation function establish the thermal fault diagnosis method of the on-board circuit board of the boiler plant equipment based on the convolutional neural network model. Third, based on the support vector machine intelligent algorithm, genetic algorithm was used to optimize the parameters, and combined with the grey prediction model, the infrared thermal fault diagnosis scheme of the circuit board of the multistage support vector machine boiler plant equipment was constructed. The results showed that the accuracy of the model after 6000 iterations was stable between 0.92-0.96, and the loss function value was stable at about 0.17. After the optimization of genetic algorithm, the accuracy of thermal fault diagnosis based on support vector machine model was optimized. Compared with grey prediction model, the accuracy of support vector machine model for fault diagnosis was higher, mean square error value was 0.0258, and the correlation coefficient was 91.55%. To sum up, the support vector machin model shows higher accuracy than grey prediction model, which can be used for thermal fault diagnosis. [ABSTRACT FROM AUTHOR]

Potholes are the most common form of distress on cement concrete pavements, which can compromise pavement safety and ridability. Thus, timely and accurate pothole detection is an important task in developing proper maintenance strategies and ensuring driving safety. This paper proposes a method of integrating the processing of grayscale and texture features. This method mainly combines industrial camera to realize rapid and accurate detection of pothole. Image processing techniques including texture filters, image grayscale, morphology, and extraction of the maximum connected domain are used synergistically to extract useful features from digital images. A machine learning model based on the library for support vector machine (LIBSVM) is constructed to distinguish potholes from longitudinal cracks, transverse cracks, and complex cracks. The method is validated using data collected from agricultural and pastoral areas of Inner Mongolia, China. The comprehensive experiments for recognition of potholes show that the recall, precision, and F1-Score achieved are 100%, 97.4%, and 98.7%, respectively. In addition, the overlap rate between the extracted pothole region and the original image is estimated. Images with an overlap rate greater than 90% accounted for 76.8% of the total image, and images with an overlap rate greater than 80% accounted for 94% of the total image. A comparison discloses that the proposed approach is superior to the existing method not only from the perspective of the accuracy of pothole detection but also from the perspective of the segmentation effect and processing efficiency. [ABSTRACT FROM AUTHOR]

In order to meet the real-time and robustness requirement of driverless cars driving on highway, this paper proposed a lane line identification method based on the Deep Learning. This method first built a lane line image library and then input the pictures of the lane line image for denoising and normalized processing. Secondly, the Lenet – 5 network model was used for classification and recognition, with a recognition rate of 99.4%, and the lane line type was displayed through GUI interface. Finally, this method was compared with the support vector machine and BP neural network, and the results effectively verified that the method can satisfy the requirement of real-time and accuracy of lane line identification. [ABSTRACT FROM AUTHOR]

Object detection in embedded systems is important for many contemporary applications that involve vision and scene analysis. In this paper, we propose a novel architecture for object detection implemented in FPGA, based on the Stripe Memory Engine (SME), and point out shortcomings of existing architectures. SME processes a stream of image data so that it stores a narrow stripe of the input image and its scaled versions and uses a detector unit which is efficiently pipelined across multiple image positions within the SME. We show how to process images with up to 4K resolution at high frame rates using cascades of SMEs. As a detector algorithm, the SMEs use boosted soft cascade with simple image features that require only pixel comparisons and look-up tables; therefore, they are well suitable for hardware implemenation. We describe the components of our architecture and compare it to several published works in several configurations. As an example, we implemented face detection and license plate detection applications that work with HD images ($1280\times 720$ pixels) running at over 60 frames/s on Xilinx Zynq platform. We analyzed their power consumption, evaluated the accuracy of our detectors, and compared them to Haar Cascades from OpenCV that are often used by other authors. We show that our detectors offer better accuracy as well as performance at lower power consumption. [ABSTRACT FROM AUTHOR]

The multispectral instrument (MSI) carried by Sentinel-2A has 13 spectral bands with various spatial resolutions (i.e., four 10-m, six 20-m, and three 60-m bands). A wide range of applications requires a 10-m resolution for all spectral bands, including the 20- and 60-m bands. To achieve this requirement, previous studies used conventional pansharpening techniques, which require a simulated 10-m panchromatic (PAN) band from four 10-m bands [blue, green, red, and near infrared (NIR)]. The simulated PAN band may not have all the information from the original four bands and may have no spectral response function that overlaps the 20- or 60-m bands to be sharpened, which may degrade fusion quality. This paper presents a machine learning method that can directly use the information from multiple 10-m resolution bands for fusion. The method first learns the spectral relationship between the 20- or 60-m band to be sharpened and the selected 10-m bands degraded to 20 or 60 m using the support vector regression (SVR) model. The model is then applied to the selected 10-m bands to predict the 10-m-resolution version of the 20- or 60-m band. The image degradation process was tuned to closely match the Sentinel-2A MSI modulation transfer function (MTF). We applied our method to three data sets in Guangzhou, China, New South Wales, Australia, and St. Louis, USA, and achieved better fusion results than other commonly used pansharpening methods in terms of both visual and quantitative factors. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the most common causes of death among all cancer-related diseases (Cancer Research UK in Cancer mortality for common cancers. http://www.cancerresearchuk.org/health-professional/cancer-statistics/mortality/common-cancers-compared, 2017). It is primarily diagnosed by performing a scan analysis of the patient's lung. This scan analysis could be of X-ray, CT scan, or MRI. Automated classification of lung cancer is one of the difficult tasks, attributing to the varying mechanisms used for imaging patient's lungs. Image processing and machine learning approaches have shown a great potential for detection and classification of lung cancer. In this paper, we have demonstrated effective approach for detection and classification of lung cancer-related CT scan images into benign and malignant category. Proposed approach firstly processes these images using image processing techniques, and then further supervised learning algorithms are used for their classification. Here, we have extracted texture features along with statistical features and supplied various extracted features to classifiers. We have used seven different classifiers known as k-nearest neighbors classifier, support vector machine classifier, decision tree classifier, multinomial naive Bayes classifier, stochastic gradient descent classifier, random forest classifier, and multi-layer perceptron (MLP) classifier. We have used dataset of 15750 clinical images consisting of both 6910 benign and 8840 malignant lung cancer related images to train and test these classifiers. In the obtained results, it is found that accuracy of MLP classifier is higher with value of 88.55% in comparison with the other classifiers. [ABSTRACT FROM AUTHOR]

The systems based on image processing have numerous applications in the domain of motion control of robots and autonomous vehicles. The current paper is oriented to the solution of the problem that precedes the implementation of automatic avoidance of the on-road obstacles-how to detect them, to track in the sequence of images, and to recognize which of them are stationary, incoming, or outgoing from the camera. The overall algorithm of obstacle classification presented in this paper consists of three basic phases: (1) image segmentation in order to extract the pixels belonging to the image of a road and the objects over it; (2) extraction of characteristic points inside the area of the obstacle, their description and tracking in following frames; and (3) estimation of distances between the camera, the obstacles and their rates of change (relative velocities). The verifications of particular steps of the proposed algorithm are illustrated using real road-traffic images, while the overall algorithm is tested using both synthesized sequences of images and the ones acquired in real driving. [ABSTRACT FROM AUTHOR]

In the present world, there is a great necessity to have reliable and quality power distribution, and so there is great scope for research on automation of distribution system. The main objective of this paper is to analyze and comprehend different machine learning and image processing based algorithms to find a practical solution for automated inspection of overhead power line insulators. This method is a relatively new approach for. This paper also highlights the constraints and limitations that are present in the various existing methodologies to achieve the objective. Traditionally the workers who inspect these lines check them in close proximity by going for foot-patrolling and pole-climbing. With an incredible expansion of power distribution network even to remote areas, previously mentioned methods do not seem to be viable. The development of an efficient method of condition monitoring by using image processing followed by machine learning techniques is found to be a suitable method and thus emerging as a feasible option for real-time implementation. The few techniques like artificial neural networks (ANN), Hidden Markov Model (HMM), k-means clustering, Wavelet transform features, S-transform features, and support vector machines (SVM) applied in the domain of condition monitoring of the insulators were presented. [ABSTRACT FROM AUTHOR]

Virus image classification is an important issue in clinical virology, highly accurate algorithm of automatic virus image classification is very helpful. In this paper, instead of extracting virus feature from the original image, we propose a novel method that extracts the virus feature from the filtered images by multi-scale principal component analysis (PCA). Firstly, multi-scale PCA filters are learned from all original images in the data set. Secondly, the original images are convolved with the learned filters. Therefore, the filtered images can capture the principal texture information from different perspectives. Then, the completed local binary pattern (CLBP) descriptor is firstly utilized to depict the features of all filtered virus images. The multi-scale CLBP features extracted from filtered images by multi-scale PCA are combined as the feature MPMC (Multi-scale PCA and Multi-scale CLBP), which is proposed in this paper. Finally, support vector machine (SVM) with polynomial kernel is used for classification. Experiments show that the classification accuracy based on MPMC outperforms the previous methods in the literature for the same virus image data set. [ABSTRACT FROM AUTHOR]