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Multilevel thresholding is a basic method in image segmentation. The conventional image multilevel thresholding algorithms are computationally expensive when the number of decomposed segments is high. In this paper, a novel and powerful technique is suggested for Crow Search Algorithm (CSA) devoted to segmentation applications. The main contribution of our work is to adapt Darwinian evolutionary theory with heuristic CSA. First, the population is divided into specified groups and each group tries to find better location in the search space. A policy of encouragement and punishment is set on searching agents to avoid being trapped in the local optimum and premature solutions. Moreover, to increase the convergence rate of the proposed method, a gray-scale map is applied to out-boundary agents. Ten test images are selected to measure the ability of our algorithm, compared with the famous procedure, energy curve method. Two popular entropies i.e. Otsu and Kapur are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are implemented and compared to the introduced method. The obtained results show that our method, compared with the original CSA, and other heuristic search methods, can extract multi-level thresholding more efficiently. [ABSTRACT FROM AUTHOR]

Pancreatic cancer is the fourth leading cause of cancer-related death worldwide. Pancreatic tumours are characterised by a peculiar cell progression in intestinal enzymes and hormone-producing cells. Patients with pancreatic tumours have a shorter survival period depending on the type of tumour they have. Enhancing patient survival, diagnosing patients with malignant pancreatic tumours as soon as possible is the need of hour. The commonly used approach for diagnosis and clinical disease staging of pancreatic cancer is Computed Tomography (CT). However, because most abdominal CT images contain noise in addition to visceral fat in the vicinity of the pancreas, prompt detection is difficult, which requires an algorithm that distinguishes the anatomical structure clearly. This paper proposes, Particle Swarm Optimisation incorporating Split and Merge Segmentation to obtain enhanced CT images of pancreatic tumour. The image undergoes image processing procedures such as segmentation using PSO and fetch an enhanced output image with the help of gaussian filter. The algorithm outperforms Conventional PSO in terms of Entropy and Contrast, according to experimental results. The improved CT scans aid in the early detection and diagnosis of pancreatic cancer. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people's safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO's initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu's method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu's method for all the used metrics. [ABSTRACT FROM AUTHOR]

The implication of firefly and fuzzy firefly optimization algorithms has been greatly witnessed in clustering techniques and extensively used in applications such as Image segmentation. Parameters such as step factor and attractiveness have been kept constant in these algorithms, which affect the convergence rate and accuracy of the clustering process. Though fuzzy adaptive firefly algorithm tackled this problem by making those parameters an adaptive one, issues such as low convergence rate, and provision of non-optimal solutions are still there. To tackle these issues, this paper proposed a novel fuzzy adaptive fuzzy firefly algorithm that significantly improves the accuracy and convergence rate while comparing with the existing optimization algorithms. Further, the proposed algorithm fused with existing hybrid clustering algorithms involving fuzzy set, intuitionistic fuzzy set, and rough set resulted in eight novel hybrid clustering algorithms which lead to better performance in optimizing the selection of initial centroids. To validate the proposal, experimental studies have been conducted on datasets found in bench-marking data repositories such as UCI, and Kaggle. The performance and accuracy evaluation of proposed algorithms have been carried out with the aid of seven accuracy measures. Results clearly indicate the improved accuracy and convergence rate of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The automatic segmentation of coronary artery in coronary computed tomography angiography (CCTA) image is of great significance for clinicians to evaluate patients with coronary heart disease. When a 3D image is limited by the amount of available GPU memory, reducing the resolution of 3D image will easily lead to the loss of image detail information. Taking patches of image as input cannot make full use of image context information. Image segmentation based on deep learning is difficult to recover perfect smooth edges. The use of smooth loss function may filter out some small lesions on the coronary artery. In this paper, we present a novel CCTA image segmentation framework that combines deep learning and digital image processing algorithms to address these challenging problems. We first use V-Net to process the CCTA image with lower resolution, and get the basic feature map (rough segmentation result) with the same resolution as the original CCTA image. Then, the original CCTA image is concatenated to the basic feature map and input it into the patch-based cascaded V-shaped module to obtain a accurate coronary artery segmentation image. Finally, the center points of coronary segmentation image and the basic gradient image of the original coronary image are obtained by morphological operation. The center points of coronary artery segmentation image are used as seed points, region growing is performed on the binary basic gradient image until the white contour boundary is searched, so as to obtain a coronary segmentation result with full segmentation and smooth edges. The proposed method is analyzed quantitatively and qualitatively, and the results show that the method is better than the mainstream baseline. The ablation experiment also proved the effectiveness of each module. [ABSTRACT FROM AUTHOR]

Image segmentation is a basic problem in medical image analysis and useful for disease diagnosis. However, the complexity of medical images makes image segmentation difficult. In recent decades, fuzzy clustering algorithms have been preferred due to their simplicity and efficiency. However, they are sensitive to noise. To solve this problem, many algorithms using non-local information have been proposed, which perform well but are inefficient. This paper proposes an improved fuzzy clustering algorithm utilizing nonlocal self-similarity and a low-rank prior for image segmentation. Firstly, cluster centers are initialized based on peak detection. Then, a pixel correlation model between corresponding pixels is constructed, and similar pixel sets are retrieved. To improve efficiency and robustness, the proposed algorithm uses a novel objective function combining non-local information and a low-rank prior. Experiments on synthetic images and medical images illustrate that the algorithm can improve efficiency greatly while achieving satisfactory results. [ABSTRACT FROM AUTHOR]

Recently, saliency maps are widely used as an image feature to improve results of segmentation or classification of defects. In this paper we tackle the problem of defect detection in fabric images. We propose a defect extraction method in textile images based on a modified anisotropic diffusion filter combined with a saliency image feature. The classical anisotropic diffusion models consider only the image gradient information of a diffused pixel. Consequently, they cannot identify defect edges confused with background texture. Since the defects in the neighborhood of the image generally have larger saliency intensity value than the background, the proposed diffusion model incorporates both local gradient magnitude and a modified saliency map. The aim is to preserve defect edges while effectively removing the textured background. Moreover a new diffusion function is proposed that permits to carry on adaptively a smoothing in faultless regions and a sharpening process at defect boundaries. To confirm the effectiveness of our algorithm, we compare tests and results with different methods on textile images including different kinds of defects and fabrics. Results show that our method outperforms the other considered algorithms and can accurately extracts defects. [ABSTRACT FROM AUTHOR]

Each pixel can be classified in the image by the semantic segmentation. The segmentation detection results of pixel level can be got which are similar to the contour of the target object. However, the results of semantic segmentation trained by Fully convolutional networks often lead to the loss of detail information. This paper proposes a CRF-FCN model based on CRF optimization. Firstly, the original image is detected based on feature pyramid networks, and the target area information is extracted, which is used to train the high-order potential function of CRF. Then, the high-order CRF is used as the back-end of the complete convolution network to optimize the semantic image segmentation. The algorithm comparison experiment shows that our algorithm makes the target details more obvious, and improves the accuracy and efficiency of semantic segmentation. [ABSTRACT FROM AUTHOR]

Background: Accurate segmentation and recognition algorithm of lung nodules has great important value of reference for early diagnosis of lung cancer. An algorithm is proposed for 3D CT sequence images in this paper based on 3D Res U-Net segmentation network and 3D ResNet50 classification network. The common convolutional layers in encoding and decoding paths of U-Net are replaced by residual units while the loss function is changed to Dice loss after using cross entropy loss to accelerate network convergence. Since the lung nodules are small and rich in 3D information, the ResNet50 is improved by replacing the 2D convolutional layers with 3D convolutional layers and reducing the sizes of some convolution kernels, 3D ResNet50 network is obtained for the diagnosis of benign and malignant lung nodules. Results: 3D Res U-Net was trained and tested on 1044 CT subcases in the LIDC-IDRI database. The segmentation result shows that the Dice coefficient of 3D Res U-Net is above 0.8 for the segmentation of lung nodules larger than 10 mm in diameter. 3D ResNet50 was trained and tested on 2960 lung nodules in the LIDC-IDRI database. The classification result shows that the diagnostic accuracy of 3D ResNet50 is 87.3% and AUC is 0.907. Conclusion: The 3D Res U-Net module improves segmentation performance significantly with the comparison of 3D U-Net model based on residual learning mechanism. 3D Res U-Net can identify small nodules more effectively and improve its segmentation accuracy for large nodules. Compared with the original network, the classification performance of 3D ResNet50 is significantly improved, especially for small benign nodules. [ABSTRACT FROM AUTHOR]

This paper proposes an image enhancement algorithm based on the theory of image segmentation and image frequency. First, a mathematical model corresponding to the pixel frequency is established by using the difference of pigment values between the pixels in the image and the surrounding pixels (i.e., pixel receptive field). Then, the image is divided into the low frequency region (background area), low-medium frequency region (foreground area), medium-high frequency region (target area) and high frequency region (detail area) by a pixel frequency characteristic graph. Gamma correction, MSRCR, MSR, top hat + bottom hat are used for image enhancement for each area, and then the parts are merged. Three indicators of PSNR, SSIM, and MSE are introduced to evaluate the quality of the enhanced image. The results show that the image enhanced by this algorithm has the highest PSNR and SSIM values and the lowest MSE value, indicating that the enhancement effect of this algorithm is better. Compared with traditional algorithms, the image enhancement algorithm in this paper produces higher image quality and richer details. [ABSTRACT FROM AUTHOR]

In this paper, a new variational model is proposed for image segmentation based on active contours, nonlinear diffusion and level sets. It includes a Chan-Vese model-based data fitting term and a regularized term that uses the potential functions (PF) of nonlinear diffusion. The former term can segment the image by region partition instead of having to rely on the edge information. The latter term is capable of automatically preserving image edges as well as smoothing noisy regions. To improve computational efficiency, the implementation of the proposed model does not directly solve the high order nonlinear partial differential equations and instead exploit the efficient alternating direction method of multipliers (ADMM), which allows the use of fast Fourier transform (FFT), analytical generalized soft thresholding equation, and projection formula. In particular, we creatively propose a new fast algorithm, normal vector projection method (NVPM), based on alternating optimization method and normal vector projection. Its stability can be the same as ADMM and it has faster convergence ability. Extensive numerical experiments on grey and colour images validate the effectiveness of the proposed model and the efficiency of the algorithms. [ABSTRACT FROM AUTHOR]

The 3D extension of High Efficiency Video Coding (3D-HEVC) is the latest international 3D video coding standard, which enhances the compression efficiency of 3D videos with multi-view plus depth (MVD) format. However, it is at the cost of increasing coding complexity. Therefore, this paper proposes a fast inter-prediction algorithm based on depth segmentation for the depth coding of 3D-HEVC to reduce the coding complexity of the depth map. We aim to reduce the coding time by efficiently utilizing the properties of the depth map. First, the proposed algorithm divides a depth map into background, middle ground and foreground, based on automatic thresholding technique. Then, we adjust the search range according to the classification of the coding tree unit (CTU). In addition, an early termination decision by utilizing the correlation of spatial, temporal, and inter-view neighboring CTUs and a fast prediction unit mode decision are also proposed to reduce coding time. The experimental results show that the proposed algorithm can reduce the depth coding time and still maintain good video quality. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to construct 3D meshes from a single image quickly and intuitively, this paper presents a 3D reconstruction method based on control point grid. The key idea is to calculate the 2D control point grid of the target area and elevate each control point according to a parameterized 3D growing rule proposed from prior knowledge. First, the contour of the target object is divided into a major component and side components and the skeleton of each component is extracted. 2D control point grid is calculated combining the curvature and geometric feature of the contour and lifted into 3D space with corresponding height defined by parameter mapping. Finally, a complete 3D model is obtained after component combination and texturing. Experimental results show that this method can reasonably and efficiently recover the 3D shape of the target object while maintaining fine curvature and geometric features. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid active contour model driven by novel global and local fitting energies for image segmentation. First, the global fitting term is defined by minimizing the difference between the weighted global fitted image and the original image, which describes the global information more accurately. Second, the local fitting term is defined by the cross entropy to compute the local information, which improves the generality of the proposed model. Experiments are performed on synthetic and real images and the results demonstrate that compared with existing active contour models, the proposed model has advantages in terms of segmentation performance and robustness to initial contour and noise. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a novel method for text line segmentation of Tibetan historical document image with uchen script based on contour tracking. Our method is mainly to segment the text lines from the image documents using the contour curve of the text lines, which consists of three parts: First, we calculate the barycentre coordinates of the connected components for the text regions, and then the barycentre of each text line is connected in order, so that the main part of each text line is connected and a new connected component is formed; then the contour curve of the connected component is obtained using the contour tracing algorithm; Second, the contour curve and the barycentre gravity are used to assign key elements (such as the syllable point, the upper vowel, the lower vowel, and the broken strokes and so on) of the text lines, and next the candidate text lines are obtained based on these connected components; Finally, the contour tracking algorithm is used to calculate the contour curve of the candidate text lines and segment the text lines. We evaluated our text line segmentation method on the 200 document image data sets. Experimental results show that the proposed method based on contour curve tracing can accurately segment the text lines of image documents and achieve the encouraging results. [ABSTRACT FROM AUTHOR]

Abstract: This paper surveys mesh segmentation techniques and algorithms, with a focus on part‐based segmentation, that is, segmentation that divides a mesh (featuring a 3D object) into meaningful parts. Part‐based segmentation applies to a single object and also to a family of objects (i.e. co‐segmentation). However, we shall not address here chart‐based segmentation, though some mesh co‐segmentation methods employ such chart‐based segmentation in the initial step of their pipeline. Finally, the taxonomy proposed in this paper is new in the sense that one classifies each segmentation algorithm regarding the dimension (i.e. 1D, 2D and 3D) of the representation of object parts. The leading idea behind this survey is to identify the properties and limitations of the state‐of‐the‐art algorithms to shed light on the challenges for future work. [ABSTRACT FROM AUTHOR]

Traumatic Brain Injury (TBI) leads to intracranial hemorrhages (ICH), which is a severe illness resulted in death if it is not properly diagnosed and treated in the earlier stage. Presently, computer tomography (CT) images are widely used by radiologists to identify and locate the regions of ICH. But it is a tedious task and mainly depends on the professional radiologists. This paper develops new deep learning (DL)-based ICH diagnosis and classification (DL-ICH) model using optimal image segmentation with Inception Network. The proposed DL-ICH model involves preprocessing, segmentation, feature extraction, and classification. Firstly, the input data undergo format conversion where the NIfTI files are converted into JPEG format. Next, Kapur's thresholding with an elephant herd optimization (EHO) algorithm called KT-EHO is employed for image segmentation. Then, DL based Inception v4 network is applied as a feature extractor to extract a useful set of features and a multilayer perceptron (MLP) is used for classification. An extensive set of simulations takes place to ensure the effective diagnostic performance of the DL-ICH model and the results are investigated under diverse dimensions. The experimental results achieved better accuracy rate. [ABSTRACT FROM AUTHOR]

Due to the haze weather, the outdoor image quality is degraded, which reduces the image contrast, thereby reducing the efficiency of computer vision systems such as target recognition. There are two aspects of the traditional algorithm based on the principle of dark channel to be improved. First, the restored images obviously contain color distortion in the sky region. Second, the white regions in the scene easily affect the atmospheric light estimated. To solve the above problems, this paper proposes a single-image dehazing and image segmentation method via dark channel prior (DCP) and adaptive threshold. The sky region of hazing image is relatively bright, so sky region does not meet the DCP. The sky part is separated by the adaptive threshold, then the scenery and the sky area are dehazed, respectively. In order to avoid the interference caused by white objects to the estimation of atmospheric light, we estimate the value of atmospheric light using the separated area of the sky. The algorithm in this paper makes up for the shortcoming that the algorithm based on the DCP cannot effectively process the hazing image with sky region, avoiding the effect of white objects on estimating atmospheric light. Experimental results show the feasibility and effectiveness of the improved algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Image segmentation is a prime operation to understand the content of images. Multilevel thresholding is applied in image segmentation because of its speed and accuracy. In this paper, a novel multilevel thresholding algorithm based on Cuckoo search (CS) is introduced. One of the major drawbacks of metaheuristic algorithms is the stagnation phenomenon which leads to a fall into local optimums and premature convergence. To overcome this shortcoming, the idea of Darwinian theory is incorporated with CS algorithm to increase the diversity and quality of the individuals without decreasing the convergence speed of CS algorithm. A policy of encouragement and punishment is considered to lead searching agents in the search space and reduce the computational time. The algorithm is implemented based on dividing the population into specified groups and each group tries to find a better location. Ten test images are selected to verify the ability of our algorithm using the famous energy curve method. Two popular entropies criteria, Otsu and Kapur, are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are also implemented and compared with our method. Experimental results manifest that DCS is a powerful tool for multilevel thresholding and the obtained results outperform the CS algorithm and other heuristic search methods. [ABSTRACT FROM AUTHOR]

Copyright of Mineral Resources Management / Gospodarka Surowcami Mineralnymi is the property of Mineral & Energy Economy Research Institute of the Polish Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Telemedicine applications allow the patient and doctor to communicate with each other through network services. Several medical image compression techniques have been suggested by researchers in the past years. This review paper offers a comparison of the algorithms and the performance by analysing three factors that influence the choice of compression algorithm, which are image quality, compression ratio, and compression speed. The results of previous research have shown that there is a need for effective algorithms for medical imaging without data loss, which is why the lossless compression process is used to compress medical records. Lossless compression, however, has minimal compression ratio efficiency. The way to get the optimum compression ratio is by segmentation of the image into region of interest (ROI) and non-ROI zones, where the power and time needed can be minimised due to the smaller scale. Recently, several researchers have been attempting to create hybrid compression algorithms by integrating different compression techniques to increase the efficiency of compression algorithms. [ABSTRACT FROM AUTHOR]

Existing attribute learning methods rely on predefined attributes, which require manual annotations. Due to the limitation of human experience, the predefined attributes are not capable enough of providing enough description. This paper proposes a self-supervised attribute learning (SAL) method, which automatically generates attribute descriptions by differentially occluding the object region to deal with the above problems. The relationship between attributes is formulated with triplet loss functions and is utilized to supervise the CNN. Attribute learning is used as an auxiliary task of a multitask image classification and segmentation network, in which self-supervision of attributes motivates the CNN to learn more discriminative features for the main semantic tasks. Experimental results on public benchmarks CUB-2011 and Pascal VOC show that the proposed SAL-Net can obtain more accurate classification and segmentation results without additional annotations. Moreover, the SAL-Net is embedded into a multiobject recognition and segmentation system, which realizes instance-aware semantic segmentation with the help of a region proposal algorithm and a fusion nonmaximum suppression algorithm. [ABSTRACT FROM AUTHOR]

The guided filter is a novel explicit image filtering method, which implements a smoothing filter on "flat patch" regions and ensures edge preserving on "high variance" regions. Recently, the guided filter has been successfully incorporated into the process of fuzzy c-means (FCM) to boost the clustering results of noisy images. However, the adaptability of the existing guided filter-based FCM methods to different images is deteriorated, as the factor ε of the guided filter is fixed to a scalar. To solve this issue, this paper proposes a new guided filter-based FCM method (IFCM_GF), in which the guidance image of the guided filter is adjusted by a newly defined influence factor ρ. By dynamically changing the impact factor ρ , the IFCM_GF acquires excellent segmentation results on various noisy images. Furthermore, to promote the segmentation accuracy of images with heavy noise and simplify the selection of the influence factor ρ , we further propose a morphological reconstruction-based improved FCM clustering algorithm with guided filter (MRIFCM_GF). In this approach, the original noisy image is reconstructed by the morphological reconstruction (MR) before clustering, and the IFCM_GF is performed on the reconstructed image by utilizing the adjusted guidance image. Due to the efficiency of the MR to remove noise, the MRIFCM_GF achieves better segmentation results than the IFCM_GF on images with heavy noise and the selection of the influence factor for the MRIFCM_GF is simple. Experiments demonstrate the effectiveness of the presented methods. [ABSTRACT FROM AUTHOR]

Tujia brocades are important carriers of Chinese Tujia national culture and art. It records the most detailed and real cultural history of Tujia nationality and is one of the National Intangible Cultural Heritage. Classic graphic elements are separated from Tujia brocade patterns to establish the Tujia brocade graphic element database, which is used for the protection and inheritance of traditional national culture. Tujia brocade dataset collected a total of more than 200 clear Tujia brocade patterns and was divided into seven categories, according to traditional meanings. The weave texture of a Tujia brocade is coarse, and the textural features of the background are obvious, so classical segmentation algorithms cannot achieve good segmentation effects. At the same time, deep learning technology cannot be used because there is no standard Tujia brocade dataset. Based on the above problems, this study proposes a method based on an unsupervised clustering algorithm for the segmentation of Tujia brocades. First, the cluster number K is calculated by fusing local binary patterns (LBP) and gray-level co-occurrence matrix (GLCM) characteristic values. Second, clustering and segmentation are conducted on each input Tujia brocade image by adopting a Gaussian mixture model (GMM) to obtain a preliminary segmentation image, wherein the image yielded after preliminary segmentation is rough. Then, a method based on voting optimization and dense conditional random field (DenseCRF) (CRF denotes conditional random filtering) is adopted to optimize the image after preliminary segmentation and obtain the image segmentation results. Finally, the desired graphic element contour is extracted through interactive cutting. The contributions of this study include: (1) a calculation method for the cluster number K wherein the experimental results show that the effect of the clustering number K chosen in this paper is ideal; (2) an optimization method for the noise points of Tujia brocade patterns based on voting, which can effectively eliminate isolated noise points from brocade patterns. [ABSTRACT FROM AUTHOR]

Due to its successful application image processing or computer vision system, image segmentation plays a significant role and has become a hot research hotspot. In this paper, we propose an improved teaching–learning-based optimization (NFDR-TLBO) to segment grayscale images via multilevel thresholding. In the proposed teaching–learning-based optimization variant, the neighborhood topology is introduced into the original teaching–learning-based optimization algorithm to maintain the exploration ability of the population and the fitness-distance-ratio mechanism is introduced into the original teaching–learning-based optimization algorithm to improve its optimization performance on complex numerical optimization problems. Moreover, the experimental results on 18 typical benchmark functions with different characteristics verify the feasibility and effectiveness of the proposed algorithm. Furthermore, the proposed algorithm is used to optimize Kapur entropy function in order to find the optimal threshold for image segmentation. Finally, the experimental simulations on different benchmark images show that the proposed algorithm is effective and efficient in improving the image segmentation performance in terms of peak-signal-to-noise ratio, structure similarity index and feature similarity. [ABSTRACT FROM AUTHOR]

Multilevel thresholding for image segmentation has always been a popular issue and has attracted much attention. Traditional exhaustive search methods take considerable time to solve multilevel thresholding problems. However, heuristic search algorithms have potential advantages in terms of solving such multilevel thresholding problems. Based on this idea, in this paper, a novel adaptive gravitational search algorithm (AGSA) is proposed to solve the optimal multilevel image thresholding problem; this algorithm is more efficient than the traditional exhaustive search method for grayscale image segmentation. In the AGSA, an adaptive parameter optimization strategy is used to tune the gravitational constant and the inertia weight. To verify the performance of the proposed algorithm, a series of classic test images are used to perform several experiments. In addition, the standard GSA and some optimization algorithms are compared with the proposed algorithm. The experimental results show that the proposed algorithm is obviously better than the other six algorithms. These promising results suggest that the AGSA is more suitable than existing methods for multilevel image thresholding. [ABSTRACT FROM AUTHOR]

The high computational cost of the superpixel segmentation algorithms for hyperspectral remote sensing images makes them ideal candidates for parallel computation. The waterpixel algorithm, in particular, extracts segmentation regions called waterpixels and consists of four stages called vectorial gradient, spatial regularization, marker selection, and watershed transform. In this paper, an efficient version of a GPU algorithm for waterpixel segmentation using the Compute Unified Device Architecture (CUDA) is presented. The algorithm extracts all the spectral information available in the bands of the hyperspectral image through the vectorial gradient. A cellular automaton is selected for the computation of the watershed transform using a block-asynchronous implementation with 8-connectivity. The experimental analysis shows high speedup values for the resulting GPU algorithm when it is compared to a multicore OpenMP implementation using 8 threads. [ABSTRACT FROM AUTHOR]

Image segmentation is a crucial stage in digital image processing used to obtain a more straightforward representation of images. Although classic bi-level segmentation is a relatively simple task, it only suffices to analyze rather simple images. More complex real-life scenarios such as medical imaging processing usually require multi-level segmentation to differentiate between the many regions of interest present in the original images. Traditional histogram-based approaches for multi-level segmentation tend to perform suboptimally, with the best performing being computationally expensive. This difficult compromise between performance and computational cost has led to the proposal of new approaches mixing a variety of optimization algorithms and statistical criteria. Despite the success of these new approaches, there is still room for improvement. It is under these circumstances that evolutionary algorithms like the cluster chaotic optimization (CCO) become relevant. The CCO takes advantage of the classification procedures of clustering techniques and the randomness of chaotic sequences for encouraging the search strategy. This paper proposes a novel method based on the CCO algorithm named minimum cross-entropy multi-level segmentation CCO (CEMS-CCO). The CEMS-CCO employs the cross-entropy as its fitness function and the CCO capabilities to deal with multimodal functions to search for the optimal solution to the multi-level segmentation problem. The CEMS-CCO shows competitive results for medical images multi-level segmentation regarding different quality metrics. Furthermore, its robustness and effectiveness are tested through the analysis of well-known benchmark images. Statistical analysis of the experimental results shows that the proposed CEMS-CCO technique outperforms state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Segmentation of image is a key step in image analysis and pre-processing. It consists of separating the pixels into different segments based on their intensity level according to threshold values. The most challenging job in segmentation is to select the optimum threshold values. Standard multilevel thresholding (MT) techniques are effective for bi-level thresholds due to their simplicity, robustness, decreased convergence time and precision. As the level of thresholds increases, computational complexity also increases exponentially. To mitigate these issues various metaheuristic algorithm are applied to this problem. In this manuscript, a new hybrid version of the Dragonfly algorithm (DA) and Firefly Algorithm (FA) is proposed. DA is an optimization algorithm recently suggested based on the dragonfly's static and dynamic swarming behavior. DA's worldwide search capability is great with randomization and static swarm behavior, local search capability is restricted, resulting in local optima trapping alternatives. The firefly algorithm (FA) is influenced by fireflies' social behavior in which they generate flashlights to attract their mates. The suggested technique combines the ability to explore DA and firefly Algorithm's ability to exploit to obtain ideal global solutions. In this paper, HDAFA is applied on ten standard test images having a diverse histogram, which are taken from Berkeley Segmentation Data Set 500 (BSDS500) benchmark image set for segmentation. The search capability of the algorithm is employed with OTSU and Kapur's entropy MT as an objective functions for image segmentation. The proposed approach is compared with the existing state-of-art optimization algorithms like MTEMO, GA, PSO, and BF for both OTSU and Kapur's entropy methods. Qualitative experimental outcomes demonstrate that HDAFA is highly efficient in terms of performance metric such as PSNR, mean, threshold values, number of iterations taken to converge and image segmentation quality. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) trained on the pixel-level annotated images have achieved improvements in semantic segmentation. Due to the high cost of labeling training data, their applications may have great limitation. However, weakly supervised segmentation approaches can significantly reduce human labeling efforts. In this paper, we introduce a new framework to generate high-quality initial pixel-level annotations. By using a hierarchical image segmentation algorithm to predict the boundary map, we select the optimal scale of high-quality hierarchies. In the initialization step, scribble annotations and the saliency map are combined to construct a graphic model over the optimal scale segmentation. By solving the minimal cut problem, it can spread information from scribbles to unmarked regions. In the training process, the segmentation network is trained by using the initial pixel-level annotations. To iteratively optimize the segmentation, we use a graphical model to refine segmentation masks and retrain the segmentation network to get more precise pixel-level annotations. The experimental results on Pascal VOC 2012 dataset demonstrate that the proposed framework outperforms most of weakly supervised semantic segmentation methods and achieves the state-of-the-art performance, which is 6 9. 3 % mIoU. [ABSTRACT FROM AUTHOR]

Multilevel image thresholding is an essential step in the image segmentation process. Expectation Maximization (EM) is a powerful technique to find thresholds but is sensitive to the initial points. Differential Evolution (DE) is a robust metaheuristic algorithm that can find thresholds rapidly. However, it may be trapped in the local optimums and premature convergence occurs. In this paper, we incorporate EM algorithm to DE and introduce a novel algorithm called EM+DE which overcomes these shortages and can segment images better than EM and DE algorithms. In the proposed method, EM estimates Gaussian Mixture Model (GMM) coefficients of the histogram and DE tries to provide good volunteer solutions to EM algorithm when EM converges in local areas. Finally, DE fits GMM parameters based on Root Mean Square Error (RMSE) to reach the fittest curve. Ten standard test images and six famous metaheuristic algorithms are considered and result on global fitness. PSNR, SSIM, FSIM criteria and the computational time are given. The experimental results prove that the proposed algorithm outperforms the EM and DE as well as EM+ other natural-inspired algorithms in terms of segmentation criteria. [ABSTRACT FROM AUTHOR]

Image segmentation is a prime operation to understand the content of images. Multilevel thresholding is applied in image segmentation because of its speed and accuracy. In this paper, a novel multilevel thresholding algorithm based on differential evolution (DE) search is introduced. One of the major drawbacks of metaheuristic algorithms is the stagnation phenomenon which leads to falling into local optimums and premature convergence. To overcome this shortcoming, the idea of Darwinian theory is incorporated with DE algorithm to increase the diversity and quality of the individuals without decreasing the convergence speed of DE algorithm. A policy of encouragement and punishment is considered to lead searching agents in the search space and reduce the computational time. The algorithm is implemented based on dividing the population into specified groups and each group tries to find a better location. Ten test images are selected to verify the ability of our algorithm using the famous energy curve method. Kapur entropy and Type 2 fuzzy entropy are employed to evaluate the capability of the introduced algorithm. Nine different metaheuristic algorithms with Darwinian modes are also implemented and compared with our method. Experimental results manifest that the proposed method is a powerful tool for multilevel thresholding and the obtained results outperform the DE algorithm and other methods. [ABSTRACT FROM AUTHOR]

Pattern recognition is a key issue in the field of textile computer aided analysis and design. Focusing on the non-stationarity of the fabric texture structure, a hybrid method combining two-dimensional mode decomposition with mean shift segmentation is proposed in this paper. Two-dimensional empirical mode decomposition is an adaptive image analysing technique, which uses intrinsic spatial scales instead of predefined basis functions to decompose a given image into a number of intrinsic modes. The first intrinsic mode preserves multiple patterns for an underlying fabric image, which is subsequently submitted to the mean shift clustering process in this paper. Simulation results have proved that the proposed mean shift algorithm in empirical mode domain is in accordance with human visual effect, and possesses the capability of clustering diverse patterns inherent in the textile in a fully unsupervised way. [ABSTRACT FROM AUTHOR]

Interactive image segmentation refers to bringing in a small number of user guidelines to segment the target object during the segmentation process and is one of the most fundamental tasks in image processing. Existing methods usually require the construction of non-quadratic energy functions and generally suffer from the lack of unique solutions and low segmentation accuracy. To further improve the segmentation quality, this paper proposed an interactive image segmentation algorithm combining local linear embedding and seed information（SILLE). The algorithm toke into account the local and a priori information of the pixel points and incorporated the information of the labeled seed points into the newly constructed energy function to obtain a unique and optimal solution of the energy function with an efficient and fast minimization scheme, thus obtaining more accurate segmentation results. Finally, comparison of different methods on different datasets verifies the effectiveness and feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

Fuzzy clustering is widely used in image segmentation because of its ability to describe the uncertain information presented in images. However, traditional fuzzy clustering ignores the spatial contextual information in the image, which makes it poor in processing images corrupted by high noise. In this paper, we present a novel fuzzy clustering algorithm called PFLWCM-CIM algorithm for noise image segmentation by introducing the image patches, the correntropy induced metric (CIM), and a fuzzy local weighted factor. Firstly, we use image patches as samples in clustering. Compared with individual pixels, image patches can preserve the local geometry of images by considering comprehensive features. Secondly, a new distance measure is developed on the basis of image patches and CIM to describe the relationship between samples and cluster centers. The CIM is more robust to noise than the traditional L 2 -norm. Next, a novel weighting method to characterize the similarity between two pixels named local weights is proposed. The local weights combine the spatial location relationship and the pixel value relationship of two pixels simultaneously, which describes the relationship between pixels from a more reasonable perspective. Furthermore, a new fuzzy local weighted factor is put forward by integrating the new distance measure and the local weights, then the PFLWCM-CIM algorithm is proposed based on the new factor and the idea of image patches. Several commonly used fuzzy clustering algorithms are incorporated into the experiments in segmenting images polluted by various types of noise. Experimental results demonstrate that our algorithm has reached state-of-the-art results in several metrics. [ABSTRACT FROM AUTHOR]

Picture processing is applied in all kind of fields, such as space science research, medical imaging, photography art. Because the human vision system is a complex nonlinear dynamic system, the traditional image enhancement methods often can not meet the requirements of real-time in practical applications. In the face of a large number of images, it is of great practical significance to obtain practical information from these data. With the rapid development of computer CnTech, image compression coding CnTech has also made great progress. And through the Mask R-CNN algorithm for effective segmentation and image recognition, can help people face massive image information, to their own needs as the goal, to achieve efficient retrieval, analysis, induction. The traditional methods of image classification by manually selecting features or manually extracting template matching have some defects. In this paper, a Mask R-CNN image optimization processing CnTech is proposed, which can accurately identify images and has higher accuracy for image feature extraction. It is meaningful to achieve automatic and accurate segmentation of microscopic images. Through training on COCO data set, it is verified through experiments that there are problems in image segmentation and recognition. In Mask R-CNN model, resource consumption is reduced and the efficiency of image segmentation and recognition is improved. Compared with the current cutting-edge algorithms, the image object detection on the strength of Mask R-CNN model has significant advantages. [ABSTRACT FROM AUTHOR]

Brain tumours and strokes are increasingly common, even from a young age. For this reason preliminary screenings, such as MRI scans, are crucial steps in detecting the abnormalities and finding a treatment. Given the increasing number of people developing different brain problems, there is a need in developing automated segmentation systems. Regardless the various methods presented in the literature, the lack of ground truth images determines the researchers to move towards unsupervised techniques. This paper presents an modified graph-based unsupervised brain segmentation method that uses a minimal spanning tree to segment brain from non-brain tissue. The original method uses human intervention for node selection. The aim is to eliminate human intervention and reduce the computational time required for brain segmentation. The presented method was compared with the original one in two ways: by resizing and scaling the images, and using images without any pre-processing step. Experimental results were obtained on the NFBS dataset and in both experiments the proposed approach delivers better results, both visually and numerically. Performance measures have improved, leading to better overall segmentation. The new method increased the precision and dice coefficient by 20%, resulting in a more accurate segmentation. [ABSTRACT FROM AUTHOR]

Infrared thermography has been used as a key means for the identification of overheating defects in power cable accessories. At present, analysis of thermal imaging pictures relies on human visual inspections, which is time-consuming and laborious and requires engineering expertise. In order to realize intelligent, autonomous recognition of infrared images taken from electrical equipment, previous studies reported preliminary work in preprocessing of infrared images and in the extraction of key feature parameters, which were then used to train neural networks. However, the key features required manual selection, and previous reports showed no practical implementations. In this contribution, an autonomous diagnosis method, which is based on the Faster RCNN network and the Mean-Shift algorithm, is proposed. Firstly, the Faster RCNN network is trained to implement the autonomous identification and positioning of the objects to be diagnosed in the infrared images. Then, the Mean-Shift algorithm is used for image segmentation to extract the area of overheating. Next, the parameters determining the temperature of the overheating parts of cable accessories are calculated, based on which the diagnosis are then made by following the relevant cable condition assessment criteria. Case studies are carried out in the paper, and results show that the cable accessories and their overheating regions can be located and assessed at different camera angles and under various background conditions via the autonomous processing and diagnosis methods proposed in the paper. [ABSTRACT FROM AUTHOR]

Spectral clustering is a popular clustering algorithm, which has a large number of applications in image segmentation tasks. However, its applicability becomes difficult for high-resolution images due to high computational complexity. In this paper, we first propose a novel Fast Spectral Clustering algorithm based on quad-tree decomposition. The algorithm focuses on the spectral clustering at the superpixel level and its time complexity is O (n log n) + O (m) + O (m 3 2 ) , its space complexity is O(m), where n and m represent the number of pixels and superpixels in an image, respectively. Then we propose Multiscale Fast Spectral Clustering by improving Fast Spectral Clustering, which is a bottom-up method based on the quad-tree and the related hierarchical structure. In this method, according to the hierarchical structure generated by the quad-tree, treating the child node's segmentation result as the superpixel of the parent node, the child nodes at the fine level can be merged into the parent nodes at the coarse level. The time complexity of Multiscale Fast Spectral Clustering is O (n log n) and its space complexity is O(m). Extensive experiments on both the Weizmann and BSDS500 segmentation datasets demonstrate that Multiscale Fast Spectral Clustering outperforms Normalized cut, a classical spectral clustering method, in terms of lower computational complexity and memory cost, with comparable clustering accuracy. [ABSTRACT FROM AUTHOR]

Generating a sufficient number of regions with high accuracy is an important objective in the region proposal generation techniques. This paper presents a new, robust, and effective approach, which is based on the bottom-up segmentation, to produce a pool of well-quality regions. After image segmentation, the segmented candidates are expanded into the surrounding regions. The suggested algorithm produces some enlarged regions, which better cover objects and stuff. The proposed process can be applied in three different modes, namely fixed_mode, all_mode, and efficient_mode. The fixed_mode extends each region into parts of all the adjacent regions using an extension controller, which considers adjacent sequential pixels for each point on the region boundary. In all_mode, the current region is merged with all the adjacent regions to generate a larger region. The efficient_mode is then implemented using the accumulation of the results from both the fixed_mode and all_mode. Besides, the algorithm can be repeated in the fixed_mode and all_mode by considering a variety of values for the extension controller factor. No features are required to be extracted in the proposed algorithm, except for the image segmentation stage. In this study, four challenging datasets known as MSRC, VOC2007, VOC2012, and COCO 2017 are used to compare the proposed algorithm with other segmentation and region proposal algorithms. As a significant advantage compared to well-known region proposal algorithms, our approach achieves a greater Recall with the desirable number of regions. Furthermore, the algorithm shows a good improvementin extraction of small, medium, and large objects. [ABSTRACT FROM AUTHOR]

3D mesh segmentation is a challenging problem in computer graphics, computer vision, and multimedia. In this paper, we cast mesh segmentation as a L0 minimization problem using random walks and L0 norm. In random walks method, the probabilities of random walks change smoothly over the whole model, which may lead to inaccurate segmentation boundaries. To attain a perception-aware result, the changes of probabilities should comply with mesh geometry. That is, the changes of probabilities near region boundaries should be more drastic than those inside the regions. Therefore, we introduce a L0 constraint to reflect the sparsity of probability changes, and identify region boundaries more precisely. Experimental results show that the proposed algorithm is effective, robust, and outperforms the state-of-the-art methods on various 3D meshes. [ABSTRACT FROM AUTHOR]

Aiming at the problem of digital inheritance and innovation of batik fabric patterns, a method for extracting batik fabric pattern and element was proposed. First, performing morphological preprocessing on the obtained digital image of batik fabric, including morphological gradient, reconstruction and local maximum image acquisition and adjustment, not only effectively remove the texture and noise in the image, but also get a binary image that can clearly reflect the overall characteristics of the pattern and elements. Then, for the complete elements in the image, according to the labeled number, the element is reserved, and other elements are set as the background to extract each element in the image, and generate an independent elements image. Finally, the canny algorithm was used to extract the overall contour of the batik fabric pattern and elements to obtain pattern contour and elements contour that can be read and edited independently. Experimental results for different batik fabric images and image segmentation methods proved the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Active contour models have been widely used in image segmentation, and the level set method (LSM) is the most popular approach for solving the models, via implicitly representing the contour by a level set function. However, the LSM suffers from high computational burden and numerical instability, requiring additional regularization terms or reinitialization techniques. In this paper, we use characteristic functions to implicitly represent the contours, propose a new representation to the geodesic active contours, and derive an efficient algorithm termed the iterative convolutionthresholding method (ICTM). Compared to the LSM, the ICTM is simpler and much more efficient. In addition, the ICTM enjoys most desired features of the level set--based methods. Extensive experiments, on two-dimensional (2D) synthetic, 2D ultrasound, 3D computed tomography, and 3D magnetic resonance images for nodule, organ, and lesion segmentation demonstrate that the proposed method not only obtains comparable or even better segmentation results (compared to the LSM) but also achieves significant acceleration. [ABSTRACT FROM AUTHOR]

Image segmentation is an important research in image processing and machine vision in which automated driving can be seen the main application scene of image segmentation algorithms. Due to the many constraints of power supply and communication in in-vehicle systems, the vast majority of current image segmentation algorithms are implemented based on the deep learning model. Despite the ultrahigh segmentation accuracy, the problem of mesh artifacts and segmentation being too severe is obvious, and the high cost, computational, and power consumption devices required are difficult to apply in real-world scenarios. It is the focus of this paper to construct a road scene segmentation model with simple structure and no need of large computing power under the premise of certain accuracy. In this paper, the ESPNet (Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation) model is introduced in detail. On this basis, an improved ESPNet model is proposed based on ESPNet. Firstly, the network structure of the ESPNet model is optimized, and then, the model is optimized by using a small amount of weakly labeled and unlabeled scene sample data. Finally, the new model is applied to video image segmentation based on dash cam. It is verified on Cityscape, PASCAL VOC 2012, and other datasets that the algorithm proposed in this paper is faster, and the amount of parameters required is less than 1% of other algorithms, so it is suitable for mobile terminals. [ABSTRACT FROM AUTHOR]

In this study, image segmentation technology is utilized for segmentation of tea leaves and tender buds and deep learning technology is introduced for tea bud classification. Watershed algorithm has good robustness in the field of image segmentation under complex backgrounds, and the key of the algorithm is to determine the image segmentation threshold, which directly affects the accuracy of segmentation. "Maximum Between-Class Variance Method" (Otsu) as a great algorithm that can obtain the global optimal threshold is applied creatively to traditional watershed algorithm in this paper, which we call "Otsu Watershed Algorithm". Then the structure of the "Squeeze-and-Excitation" (SE) block is adjusted appropriately to improve the feature presentation ability of the network by embedding into several common deep learning models. Extensive experiments demonstrate that this new SE block has superior accuracy and integration capability on challenging dataset and our tea bud dataset. [ABSTRACT FROM AUTHOR]

As a branch of the field of machine learning, deep learning technology is abrupt in various computer vision tasks with its powerful functional learning functions. The deep learning method can extract the required features from the original data and dynamically adjust and update the parameters of the neural network through the backpropagation algorithm so as to achieve the purpose of automatically learning features. Compared with the method of extracting features manually, the recognition accuracy is improved, and it can be used for the segmentation of copperplate printing images. This article mainly introduces the research on the key algorithm of the copperplate printing image segmentation based on deep learning and intends to provide some ideas and directions for improving the copperplate printing image segmentation technology. This paper introduces the related principles, watershed algorithm, and guided filtering algorithm of copperplate printing image synthesis process and establishes an image segmentation model. As a result, a deep learning-based optimization algorithm mechanism for the segmentation of copper engraving printing images is proposed, and experimental steps such as main color extraction in the segmentation of copper engraving printing images, adaptive main color extraction based on fuzzy set 2, and main color extraction based on fuzzy set 2 are proposed. Experimental results show that the average processing time of each image segmentation model in this paper is 0.39 seconds, which is relatively short. [ABSTRACT FROM AUTHOR]

To address the problem of segmenting an image into sizeable homogeneous regions, this paper proposes an efficient agglomerative algorithm on the basis of modularity optimization. Given an oversegmented image that consists of many small regions, our algorithm automatically merges those neighboring regions that produce the largest increase in modularity index. When the modularity of the segmented image is maximized, the algorithm stops merging and produces the final segmented image. To preserve the repetitive patterns in a homogeneous region, we propose a feature on the basis of the histogram of states of image gradients and use it together with the color feature to characterize the similarity of two regions. By constructing the similarity matrix in an adaptive manner, the oversegmentation problem can be effectively avoided. Our algorithm is tested on the publicly available Berkeley Segmentation Data Set as well as the semantic segmentation data set and compared with other popular algorithms. Experimental results have demonstrated that our algorithm produces sizable segmentation, preserves repetitive patterns with appealing time complexity, and achieves object-level segmentation to some extent. [ABSTRACT FROM AUTHOR]