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• Optimized incremental bit-quads count in component trees. • The intuition of incremental bit-quad count in component trees correctness. • A Fast method to compute area, perimeter and Euler Number in grayscale images. • A Fast algorithm to compute continuous perimeter approximation in grayscale images. • Software applications were rewritten using attributes computed by bit-quads. Component tree is a full image representation which encodes all connected components from upper (resp. lower) level sets of a given image through the inclusion relation. Information from this representation can be used in many image processing and computational vision applications, e.g. connected filtering, image segmentation, feature extraction, among others. In general, each node of a component tree represents a connected component of a level set and stores attributes which describes features of this connected component. This paper presents a review of a previously published method to compute attributes such as area, perimeter, and number of Euler by incrementally counting patterns while traversing nodes of a component tree. This method foundation is further detailed in this paper by presenting a novel theoretical background and algorithm correctness intuition. We also present a novel approach for this algorithm showing improvements for run-time execution and precision analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a robust deblurring algorithm for noisy images with just noticeable blur (JNB). Natural images are usually accompanied by JNB and noise when viewed under pixel scale resolution, but are easily neglected. In this paper, we describe the JNB phenomenon and briefly analyze its causes. An image-gradient-related constrained factor is introduced in our algorithm based on the prior of image noise’s low gradient distribution. Richardson-Lucy method is also adopted to reduce algorithm’s time cost. For large-size cellphone images, we put forward an effective image segmentation model suitable for space-variant blur. Experiments show that our method obtains high-quality deblurring images and are competitive to state-of-the-art deblurring algorithms. [ABSTRACT FROM AUTHOR]

The task of segmenting small infrared targets, which have few pixels and weak features, has been a difficult problem in the field of small target image processing. Small targets not only exist in general images, but also widely exist in drone cameras, communication base station cameras, rescue cameras, and vehicle cameras. Research on small target segmentation algorithm is very important for analyzing and utilizing these images, and it also has important application value in security, traffic, and rescue. Traditional small target segmentation algorithms are able to segment objects with simple target contour edges and large differences in signal strength. The traditional algorithm often has a high false detection rate and missed detection rate when facing several targets with weak signal strength. It does not perform well in complex scenes. In this paper, we introduce an infrared small target segmentation scheme facing multiple types and numbers of targets. This paper also produces an infrared UAV and pedestrian dataset for validation. Experiments show that the optimized algorithm has a better detection effect on targets in complex scenes. [ABSTRACT FROM AUTHOR]

• Automated selection of image segmentation algorithms. • Computational cost reduction in image segmentation tasks. • Meta-learning outperformed any single algorithm explored over the whole dataset. • Machine Learning and Gradient-based image segmentation algorithms were studied. • Experiments were conducted over a widely known image dataset with 2100 examples. Image segmentation is a key issue in image processing. New image segmentation algorithms have been proposed in the last years. However, there is no optimal algorithm for every image processing task. The selection of the most suitable algorithm usually occurs by testing every possible algorithm or using knowledge from previous problems. These processes can have a high computational cost. Meta-learning has been successfully used in the machine learning research community for the recommendation of the most suitable machine learning algorithm for a new dataset. We believe that meta-learning can also be useful to select the most suitable image segmentation algorithm. This hypothesis is investigated in this paper. For such, we perform experiments with eight segmentation algorithms from two approaches using a segmentation benchmark of 300 images and 2100 augmented images. The experimental results showed that meta-learning can recommend the most suitable segmentation algorithm with more than 80% of accuracy for one group of algorithms and with 69% for the other group, overcoming the baselines used regarding recommendation and segmentation performance. [ABSTRACT FROM AUTHOR]

Highlights • Propose indirect diffusion model for extraction of deeply concave edges and sharp corners. • Model is defined as two PDEs, one driving level set motion and one smoothing indirectly level set. • Level set function can be initialized to zero function, so allowing for flexible initialization. • It is easy to set a termination criterion for the proposed algorithm. Abstract In this paper, we put forward an idea of indirect diffusion and further develope an indirect diffusion-based level set model for image segmentation. This model is based on the dynamic process of diffusion that is posed indirectly on level set function by way of auxiliary function, coupled with a transition region-based force that exhibits the desired sign-changing property. It is formulated as a coupled system of two evolution equations, in which the first equation drives the motion of zero level set toward the object edges and makes it possible to set a termination criterion on the algorithm, while the second equation (indirect diffusion) smoothens the auxiliary function and keeps the auxiliary function as close to the level set function as possible. The derived model can effectively be solved purely by the simplest explicit finite difference. Experimental results show that the proposed model not only has the strong capability of noise immunity, but it also can much better conduce to extraction of deeply concave edges and preservation of sharp corners, compared with the direct diffusion-based counterpart. [ABSTRACT FROM AUTHOR]

Image segmentation is the most important operation in image processing that changes the representation of an image to be useful in much computer vision applications. During the last years, several image segmentation algorithms have been developed, consequently, a great number of techniques for evaluating segmentation results have been proposed. Unfortunately, the majority of these methods is subjective and cannot be used to judge the performance of different segmentation algorithms. In this paper, we propose a novel evaluation criterion based on the Gini index and the entropy calculation. This new method permits evaluating the conjunction of the regions arrangement in the segmented image, and measuring the pixels homogeneity within each region. In experiments, benchmark images segmented by the multilevel thresholding technique based on particle swarm optimization are used to conclude the strength, the effectiveness and the rapidity of the proposed evaluation criterion. [ABSTRACT FROM AUTHOR]

Deciding a rational number of clusters for the problems to be solved and determining the parameters associated with the clustering algorithms are two critical issues in the configuration of data clustering. Usually, a manual trial-and-error manner is used to induce a feasible configuration. This paper presents an automated clustering method, which determines the clustering configuration automatically. The proposed method is based on the techniques of support-vector machines with a local-search strategy. It starts with running one-class support-vector machines (OCSVM) to partition input data into a random number of clusters. When a result is obtained, the “local-search” mechanism launches several rounds of OCSVM each of which works with a new clustering configuration. Each new configuration is from the current configuration with incrementally modifications. The clustering results obtained from the local searches are post-evaluated by specific clustering validity index and the best one is retained. The clustering configuration of the best result is used by OCSVM for the clustering afterwards. Such a clustering process iterates until no better result can be obtained. This paper describes the clustering algorithm and compares three clustering validity indices, i.e. distance-based index, Davies–Bouldin index, and Xie–Beni index, on their effectiveness. The performance of the proposed method is demonstrated on the segmentation of several aerial images. Experimental results show that the proposed approach is feasible and effective for image segmentation. [ABSTRACT FROM AUTHOR]

Multi-level image thresholding is a common approach to image segmentation where an image is divided into several regions based on its histogram. Otsu's method is the most popular method for this purpose, and is based on seeking for threshold values that maximise the between-class variance. This requires an exhaustive search to find the optimal set of threshold values, making image thresholding a time-consuming process. This is especially the case with increasing numbers of thresholds since, due to the curse of dimensionality, the search space enlarges exponentially with the number of thresholds. Population-based metaheuristic algorithms are efficient and effective problem-independent methods to tackle hard optimisation problems. Over the years, a variety of such algorithms, often based on bio-inspired paradigms, have been proposed. In this paper, we formulate multi-level image thresholding as an optimisation problem and perform an extensive evaluation of 23 population-based metaheuristics, including both state-of-the-art and recently introduced algorithms, for this purpose. We benchmark the algorithms on a set of commonly used images and based on various measures, including objective function value, peak signal-to-noise ratio, feature similarity index, and structural similarity index. In addition, we carry out a stability analysis as well as a statistical analysis to judge if there are significant differences between algorithms. Our experimental results indicate that recently introduced algorithms do not necessarily achieve acceptable performance in multi-level image thresholding, while some established algorithms are demonstrated to work better. [ABSTRACT FROM AUTHOR]

Image processing methods for automated concrete crack detection are often challenged by binary noise. Noise removal methods decrease the false positive pixels of crack detection results, often at the cost of a reduction in true positives. This paper proposes a novel method for binary noise removal and segmentation of noisy concrete crack images. The method applies an area threshold before reducing the pixel groups in the image to a skeleton. Each skeleton is connected to its nearest neighbour before the remaining short skeletons in the image are removed using a length threshold. A morphological reconstruction follows to remove all elements in the original noisy image that do not intersect with the skeleton. Finally, pixel groups in close proximity to the endpoints of the pixel groups in the resulting image are reinstated. Testing was conducted on a dataset of noisy binary crack images; the proposed method (Skele-Marker) obtained recall, precision, intersection over union, and F1 score results of 77%, 91%, 72%, and 84%, respectively. Skele-Marker was compared to other methods found in literature and was found to outperform other methods in terms of precision, intersection over union and F1 score. The proposed method is used to make crack detection results more reliable, supporting the ever-growing demand for automated inspections of concrete structures. • We use image processing techniques to remove noise in binary concrete crack images. • False positives in binary concrete crack images can be reduced using our method. • We compare our concrete crack noise removal method to others found in literature. • Our method performed better than other concrete crack segmentation methods. [ABSTRACT FROM AUTHOR]

We propose a novel fully convolutional network architecture for shapes, denoted by Shape Fully Convolutional Networks (SFCN) . 3D shapes are represented as graph structures in the SFCN architecture, based on novel graph convolution and pooling operations , which are similar to convolution and pooling operations used on images. Meanwhile, to build our SFCN architecture in the original image segmentation fully convolutional network (FCN) architecture, we also design and implement a generating operation with bridging function. This ensures that the convolution and pooling operation we have designed can be successfully applied in the original FCN architecture. In this paper, we also present a new shape segmentation approach based on SFCN. Furthermore, we allow more general and challenging input, such as mixed datasets of different categories of shapes which can prove the ability of our generalisation. In our approach, SFCNs are trained triangles-to-triangles by using three low-level geometric features as input. Finally, the feature voting-based multi-label graph cuts is adopted to optimise the segmentation results obtained by SFCN prediction. The experiment results show that our method can effectively learn and predict mixed shape datasets of either similar or different characteristics, and achieve excellent segmentation results. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing is an important technique for remote sensing image exploitation. It aims to decompose a mixed pixel into a collection of spectrally pure components (called endmembers ), and their corresponding proportions (called fractional abundances ). In recent years, many studies have revealed that unmixing using spectral information alone does not sufficiently incorporate the spatial information in the remotely sensed hyperspectral image, as the pixels are treated as isolated entities without taking into account the existing local correlation among them. To address this issue, several spatial preprocessing methods have been developed to include spatial information in the spectral unmixing process. In this paper, we present a new spatial preprocessing method which presents several advantages over existing methods. The proposed method is derived from the Simple Linear Iterative Clustering (SLIC) method, which adapts the global search scope of the clustering into local regions. As a result, the spatial correlation and the spectral similarity are intrinsically incorporated at the clustering step, which results in O ( N ) computational complexity of the clustering procedure with N being the number of pixels in the image. First, a regional clustering is iteratively performed by using spatial and spectral information simultaneously. The obtained result is a set of clustered partitions that exhibit both spectral similarity and spatial correlation. Then, for each partition we select a subset of candidate pixels with high spectral purity. Finally, the obtained candidate pixels are gathered together and fed to a spectral-based endmember extraction method to extract the final endmembers and their corresponding fractional abundances. Our newly developed method naturally integrates the spatial and the spectral information to retain the most relevant endmember candidates. Our experimental results, conducted using both synthetic and real hyperspectral scenes, indicate that the proposed method can obtain accurate unmixing results with less than 0.5% of the number of pixels used by other state-of-the-art methods. This confirms the advantages of integrating spatial and spectral information for hyperspectral unmixing purposes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new distance called the k -max distance that is intended for graphs and images. The length of a path is defined as the sum of the k maximum arc weights along the path. The distance between two nodes is the length of the shortest path between them. We show that the k -max distance is a metric. The algorithm for computing the k -max distance is presented. Certain positive properties of the k -max distance are shown, namely in the context of measuring the distances for image segmentation. The comparison with the geodesic distance, the max-arc distance, the minimum barrier distance, and the random walker technique is carried out in the segmentation of real-life images. [ABSTRACT FROM AUTHOR]

In the past research, we have proposed a one-shot-projection method for analytic measurement of the shapes of the mirror surfaces, which utilizes the information of two captured laser dots patterns to reconstruct the mirror surfaces. Yet, the automatic image processing algorithms to extract the laser dots patterns have not been addressed. In this paper, a series of automatic image processing algorithms are proposed to segment and classify the projected laser dots robustly and efficiently during measuring the shapes of mirror surfaces and each algorithm is indispensible for the finally achieved accuracy. Firstly, the captured image is modeled and filtered by the designed frequency domain filter. Then, it is segmented by a robust threshold selection method. A novel iterative erosion method is proposed to separate connected dots. Novel methods to remove noise blob and retrieve missing dots are also proposed. An effective registration method is used to help to select the used SNF laser and the dot generation pattern by analyzing if the dot pattern obeys the principle of central projection well. Experimental results verified the effectiveness of all the proposed algorithms. [ABSTRACT FROM AUTHOR]

Image segmentation has been an important technique in the field of image processing. Fine-level manual annotations are very limited and difficult for a large collection of imbalanced images, where each image contains quite different small objects. However, we find that these imbalanced images have similar decision graphs obtained by a lightweight and simple clustering algorithm Density Peaks (DPeaks). Hence, in this paper, we propose a weakly supervised image segmentation algorithm. It trains a decision curve from decision graphs of a few sample imbalanced images by SVM and Support Vector Regression (SVR), which is effective for identifying the sparse region of an imbalanced image. Besides, RangeTree is applied to accelerate RDP for large images due to the high complexity of DPeaks Clustering. Experiments prove that the proposed algorithm works well on imbalanced image data sets, not only it can recognize main things, but also has the ability to identify some relatively small objects. [ABSTRACT FROM AUTHOR]

Mastitis is an infectious disease associated with the increased number of somatic cells in cow’s milk, and it is one of the most relevant cause of economic losses in dairy farming industries. In this paper, we propose a method capable of determine, with 99.7% accuracy, the number of these cells in microscope slide images. This level of accuracy is achieved by changing the image original RGB format to Lab color space and applying k -means clustering algorithm to remove debris and other background features. A new gray level thresholding is proposed, and the remaining bound cells are separated in the final segmentation step applying Watershed transform. Many microscope slide images with debris, contrast, and hue variation were used to validate the experimental results. Comparison between the proposed method and manual counting indicates that this new approach is a robust and promising solution to be incorporated in a future automated somatic cell counting system using optical microscopy. [ABSTRACT FROM AUTHOR]

This paper proposes an image segmentation method for blurred trace infrared images, exploring statistical properties of blurred infrared data to segment target regions. I consider the function of immune factors for blurred infrared image segmentation, combined to the template algorithm framework. First, all of the pixel antigens are divided into three classes by innate recognition factors: target antigens, background antigens and blurred antigens. Next, the innate presentation factors present template characteristics as new features for each antigen. Finally, target and background antigens are used for generating mature adaptive immune factors, and these mature factors will recognise each blurred antigen into two classes: a target pixel or a background pixel. Experimental results indicate that the superstring galaxy template algorithm can improve the target segmentation rate and reduce the segmentation error rate. [ABSTRACT FROM AUTHOR]

Unsupervised data clustering can be addressed by the estimation of mixture models, where the mixture components are associated to clusters in data space. In this paper we present a novel unsupervised classification algorithm based on the simultaneous estimation of the mixture's parameters and the number of components (complexity). Its distinguishing aspect is the way the data space is searched. Our algorithm starts from a single component covering all the input space and iteratively splits components according to breadth first search on a binary tree structure that provides an efficient exploration of the possible solutions. The proposed scheme demonstrates important computational savings with respect to other state-of-the-art algorithms, making it particularly suited to scenarios where the performance time is an issue, such as in computer and robot vision applications. The initialization procedure is unique, allowing a deterministic evolution of the algorithm, while the parameter estimation is performed with a modification of the Expectation Maximization algorithm. To compare models with different complexity we use the Minimum Message Length information criteria that implement the trade-off between the number of components and data fit log-likelihood. We validate our new approach with experiments on synthetic data, and we test and compare to related approaches its computational efficiency in data-intensive image segmentation applications. [ABSTRACT FROM AUTHOR]

• Pixel level segmentation algorithm of irregular aggregate based on deep learning. • Online measurement system of stacking aggregate gradation containing water and mud. The quality of image processing affects the results of aggregate particle size detection. So far, the aggregate image segmentation is still limited to the traditional watershed algorithm (WA) and threshold segmentation algorithm, and their segmentation effect is limited. Therefore, a deep learning algorithm (DLA) to segment stacked coarse aggregate images was presented in this paper. Then it was used to segment the images of aggregates with different aggregate materials, particle sizes and mixing ratios, and the segmentation results were analyzed. The dataset required was made by the labelme, and trained to obtain the segmentation model of the DLA. The test results were compared with that of the traditional WA. For single-stage aggregates with particle sizes of 5–10 mm, 10–20 mm and 20–31.5 mm, the proportion of cumulative gradation calculated by DLA was increased by 5.93%, 28.62% and 23.62%, respectively, for different base materials of coarse aggregate, the proportion of cumulative gradation calculated by DLA was increased by 28.62%, 37.68% and 12.78%, respectively. For aggregate images with different mixing ratios, the segmentation results of DLA were closer to the actually configured gradation. The experimental results showed that the DLA could avoid the common phenomenon of over-segmentation and under-segmentation in the WA, and had general applicability in segmenting coarse aggregates with different particle sizes and aggregate materials. [ABSTRACT FROM AUTHOR]

Superpixel is one of the most popular image over-segmentations with broad applications in the computer vision field to reduce their computations by replacing pixels as primitives. The main concerns of one superpixel generation algorithm are its accuracy and efficiency. One of the most important things in superpixel accuracy is to fit the image boundaries tightly with a few pixels as possible (namely minimal contour density, which is measured by the percent of superpixel contour pixels in the whole image). In this paper, we propose a new fast algorithm based on the clustering method to produce superpixels accurately with low contour density. First, we adopt the linear path from a pixel to one superpixel seed to define a regular term and propose a new distance measurement between them. In addition, we introduce the gradient and Local Binary Pattern (LBP) features and propose formulas of parameters in the proposed method adaptively. In this way, we can use the new distance measurement to group pixels as initial regions adaptively and produce the final superpixels by merging those small ones. Finally, we test the new algorithm on two public datasets and compare it with the state-of-the-art. Our method can generate superpixels with lower contour density while being competitive in accuracy and computational time. [ABSTRACT FROM AUTHOR]

Accurately segmenting adjacent regions in images is one of the greatest challenges in image processing. In this paper, we introduce a double level set segmentation model based on mutual exclusion, which can accurately and independently segment adjacent regions. In order to avoid the creation of intersection regions and ensure the independence of neighboring regions, the model uses two level set functions, and defines the mutual exclusion term of neighboring regions according to the area of the joint region segmented by the two level set functions. Furthermore, we manually set the approximate contours of the target region as a prior information, defining prior constraints in the energy functional to ensure that the model only segments the organization we wanted. Combining the data term, the mutual exclusion term, the prior constraint term, the length term and the regularization term, we give the level set formulation of this double level sets energy functional and apply the gradient descent algorithm to solve its energy minimization problem. Applying the double level sets model to brain MR images, the experimental results show that the model can accurately and independently segment adjacent tissues in brain. The comparison experimental results with other models show that the double level sets model has a higher accuracy than the classical models in segmenting adjacent tissues. In addition, experiments on synthetic images show that the double level sets model can accurately segment images with intensity inhomogeneity and is robust to noise. Code is avaliable at https://github.com/ruicx/Mutual-Exclusion-Level-Set. [ABSTRACT FROM AUTHOR]

Infrared pedestrian segmentation is a challenging problem in infrared image processing. Kaniadakis entropy thresholding method can segment the infrared image with long tail distribution histogram. However, it failed in images with noise and complex background. In this paper, a novel infrared pedestrian segmentation algorithm based on the two-dimensional Kaniadakis entropy thresholding is proposed to address this problem. First, in order to introduce the spatial information of pixels, the two-dimensional Kaniadakis entropy thresholding algorithm is proposed through extending the one-dimensional Kaniadakis entropy thresholding algorithm and using the two-dimensional histogram of image. The recursive formulas of the two-dimensional algorithm are also presented to improve the computation efficiency. Second, an intensity suppressed strategy is embedded in the optimal thresholds searching process of the two-dimensional Kaniadakis entropy thresholding algorithm to segment the pedestrian in infrared images with complex background. In the experiment, the proposed algorithm is compared with the state-of-the-art image segmentation methods on several infrared images with different backgrounds. The experimental results verify the effectiveness of the proposed method both qualitatively and quantitatively. • Extend the Kaniadakis entropy thresholding to 2d. • Derive the recursive formulas of the 2d Kaniadakis entropy thresholding. • Propose a suppressed strategy in 2d-Kan for infrared pedestrian segmentation. • Verify the effectiveness of the proposed infrared pedestrian segmentation algorithm. [ABSTRACT FROM AUTHOR]

Image segmentation process is one of the most interesting and challenging problems in digital image processing tasks. The segmentation process involves finding similar regions within an image. Many segmentation problems are achieved by the incorporation of clustering techniques. One of the most common technique for clustering process is the Fuzzy C-means (FCM) algorithm. However, even when FCM is one of the most popular techniques applied in image segmentation, it presents some issues such as large computational time complexity, noise sensitivity, and initial cluster centers dependency. In order to solve these problems, this paper presents a Histogram Based Fuzzy Clustering (HBFC) technique using an improved version of Firefly Algorithm (FA). In the proposed approach, the FA involves three search strategies: rough set-based population, random attraction and local search mechanism. Also, the clustering process is conducted based on gray level histograms instead of single pixels of an image. Under such circumstances, the occurrence of misclassification of pixels is reduced. A rigorous comparative study is conducted among the proposed approach and several state-of-art Nature-Inspired Optimization Algorithms (NIOAs) and traditional clustering techniques. The numerical results indicate that the proposed approach outperform the well-known NIOA based clustering methods in terms of precision, robustness and quality of the segmented outputs. • An improved Firefly algorithm has been proposed for Image Clustering. • Random Attraction model and rough set have been utilized. • The proposed approach is a histogram based fast fuzzy clustering model. • The proposed method is compared against state-of-art segmentation methods. • Numerical results indicate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

The goal of the segmentation of brain images is to separate the images in different non-compatible homogenous areas reflecting the numerous anatomical structures. Brain segmentation by magnetic resonance has numerous implications for diagnosing brain disorganizations such as Alzheimer's, Parkinson-related syndrome among others. However, it is not an simple job to automatically segment the MR image. The main motive of this study is to provide a better segmentation approach for the segment of the ROI (Region Of Interest) region from the MRI image by solving the issues that currently exist in the literary works. MRI segmentation is not a trivial task, because acquired MR images are imperfect and are often corrupted by noise and other image artifacts. The variety in technologies for image processing has contributed to the creation in numerous image segmentation techniques. That is because there is no universal approach, nor are all methods necessarily appropriate for a specific form of picture suitable for all pictures. Other approaches still use the gray level histogram, for example, while others integrate detailed spatial picture details for bleeding conditions. Some methods use statistical techniques, but some do incorporate existing information to enhance segmentation efficiency. Some methods utilize probabilistic or fuzzy methods. Yet there are certain inconveniences of all the current processes. Therefore, we have intended to propose a new segmentation approach for the ROI region segmentation. The proposed work comprised of three phases namely preprocessing, edge detection and segmentation. At first, the MRI images are extracted from the database and that each of the input images is enhanced by applying a high pass filter. After completing the preprocessing method, the enhanced canny edge detection (ECED) approach is used to enhance the image. After that, the images are given to the modified watershed segmentation (MWS) algorithm which separates the ROI part from MRI Image. The testing consequences demonstrate that the proposed system accomplishes to give the good result related to the available strategies. Xilinx Virtex-5 FPGA is used to implement in this paper. [ABSTRACT FROM AUTHOR]

• Our work proposed to use image processing technology to process the acquired infrared video. • The improved super green algorithm is proposed to feature analysis in the actual situation. • The results showed that the proposed algorithm had convictive validity. In this study, we propose using an unmanned aerial vehicle infrared thermal imager to obtain infrared video, and using image processing technology to process the acquired video. Moreover, based on research regarding traditional algorithms, an improved super green algorithm is proposed, and a feature analysis is conducted according to the actual situation. In this study, an effective identification model was designed for the most common types of faults, the most common electrical equipment components for research were collected, tests for analyzing the effectiveness of the algorithm were designed, and relevant data and images were recorded. The research shows that the proposed algorithm has validity, and can provide a theoretical reference for subsequent related research. Image, graphical abstract In order to solve the problem that people are difficult to reach, people have proposed to use helicopters to inspect transmission lines. However, at this time, the helicopter inspection of the transmission line was mostly based on visual inspection, and the test results were not guaranteed. In order to ensure the continuity and safety of users, traditional manual inspection methods have not been able to achieve the desired goals. This research mainly introduces fault image segmentation and morphological processing algorithms. Theoretical research and experimental tests are performed on various segmentation algorithms such as histogram threshold segmentation, iterative threshold segmentation, and maximum inter-class variance. Combining the two fault image features, an RGB component constraint segmentation algorithm is proposed. Morphological processing is performed on the image after segmentation to improve the quality of the binary image. Summarize the fault status judgment and fault area identification methods of power equipment. For the rust failure of the hardware, the rust failure is judged by marking the fault area after the division and counting the area ratio. For insulator faults, combined with the regular arrangement of insulators, a bilateral contour difference matching algorithm was proposed to determine the state of the insulators and realize fault detection of power equipment. Fig. 1 shows the color image of the rust fault and its R, G, and B channel grayscale images. Since there is a strong correlation between the three components in the RGB space, and the pixel color is determined by the three channels together, the processing of the gray image of the specific channel alone has no adaptability and accuracy. Therefore, this method is used less. Fig. 2 shows the original grayscale image of the insulator. Fig. 3 is the original grayscale histogram obtained by statistical drawing on the basis of Fig. 2. It is difficult to obtain valid information from Fig. 2 and Fig. 3, and the fault feature cannot be extracted. A fault judgment algorithm is designed for two fault types. For the rust corrosion of the metal fittings, the rust fault is judged by red marking the divided fault areas and counting the area ratio. In addition, for the insulation fault, combined with the regular arrangement law of the insulator, a bilateral contour differential matching algorithm is proposed to judge the state of the insulator. In addition, this paper designs experiments to improve the effectiveness of the algorithm. The research results show that the results of this study have certain validity and can be applied to fault identification of power equipment. [ABSTRACT FROM AUTHOR]

Cardiac magnetic resonance imaging (CMR) is a widely used non-invasive imaging modality for evaluating cardiovascular diseases. CMR is the gold standard method for left and right ventricular functional assessment due to its ability to characterize myocardial structure and function and low intra- and inter-observer variability. However the post-processing segmentation during the functional evaluation is time-consuming and challenging. A fully automated segmentation method can assist the experts; therefore, they can do more efficient work. In this paper, a regression-based fully automated method is presented for the right- and left ventricle segmentation. For training and evaluation, our dataset contained MRI short-axis scans of 5570 patients, who underwent CMR examinations at Heart and Vascular Center, Semmelweis University Budapest. Our approach is novel and after training the state-of-the-art algorithm on our dataset, our algorithm proved to be superior on both of the ventricles. The evaluation metrics were the Dice index, Hausdorff distance and volume related parameters. We have achieved average Dice index for the left endocardium: 0.927, left epicardium: 0.940 and right endocardium: 0.873 on our dataset. We have also compared the performance of the algorithm to the human-level segmentation on both ventricles and it is similar to experienced readers for the left, and comparable for the right ventricle. We also evaluated the proposed algorithm on the ACDC dataset, which is publicly available, with and without transfer learning. The results on ACDC were also satisfying and similar to human observers. Our method is lightweight, fast to train and does not require more than 2 GB GPU memory for execution and training. • A novel regression-based method for the fully-automatic segmentation of CMR short-axis images on both ventricles. • The algorithm performance is similar to an experienced radiologist on the left and right ventricle. • Our algorithm is more general because our dataset was more comprehensive and diverse compared to the other datasets used for developing segmentation algorithms. It does not contain only healthy subjects but a wide scale of cardiovascular diseases. • More accurate results than the baselines, we trained Bai's method from scratch on our dataset. • Lightweight model, requires fewer memory and computation to train and use the model. [ABSTRACT FROM AUTHOR]