Segmentation Database Research Articles

Automated system for classifying images of video streams for timely detection of fires

ABSTRACT Early detection of a fire during its development stage is highly desired to minimize human and material losses. The improvement of the efficiency of these monitoring systems through the analysis of fire video data drawn from unmanned aerial vehicles is considered . Classification of video images when monitoring a fire event on the territory is used based on their segmentation into identical rectangular segments of a given size and then assigning them to one of three classes: Smoke, Flame, and Indifferent classes. The Walsh-Hadamard transform was used to form descriptors for the Weak classifiers. Descriptors were calculated for three Weak classifiers; first, the Walsh-Hadamard transform was calculated for the window of the entire segment, and its spectral coefficients were used for the first Weak classifier. Next, the descriptors were calculated in windows whose sizes were half and a quarter of the original window size. The classifier consisted of three independently-trained neural networks. A simple ensemble averaging block was used to combine the outputs of those networks. A special software code has been developed to classify video images. The code allows us to create a database of segments of the ‘Smoke’ and ‘Flame’ classes, determine the two-dimensional Walsh-Hadamard spectrum of video image segments, train fully connected neural networks, conduct exploratory analysis and evaluate the relevance of the calculated two-dimensional spectral coefficients. For validation we used real data from Сlosed Circuit Television cameras presented in open databases. The experimental studies on the classification of video data containing flames and smoke showed that the proposed method has an average smoke detection accuracy of 86% and an average flame detection accuracy of 89.5%. Errors II in smoke detection and flame detection had the averages of 13% and 4.5%, respectively.

Revolutionizing comic coloring: Federated learning-based neural network for efficiency and privacy

The conventional method of coloring comics can be quite arduous and time-consuming, particularly within the realm of animation, where each frame necessitates individual coloring. Over the past few years, the advent of deep learning technology has introduced fresh prospects for comic coloring. Nonetheless, it's worth noting that deep learning typically demands a substantial volume of data to effectively train models, giving rise to legitimate concerns regarding data privacy. This study proposes an innovative approach that applies federated learning to comic coloring to improve efficiency and address privacy issues. In this research, an existing neural network European Conference on Computer Vision (ECCV16) model was employed using federated learning to partition and train the model on segmented databases. To better quantify the color differences between the original images and the colored images, this study designed a custom loss function. Experimental results demonstrate that this approach has been effective in comic coloring, demonstrating the feasibility of applying federated learning to the task of comic coloring.

Accurate artificial intelligence method for abnormality detection of CT liver images

Liver cancer is a leading cause of death worldwide and poses a significant challenge to physicians in terms of accurate diagnosis and treatment. AI-powered segmentation and classification algorithms can play a vital role in assisting physicians in detecting and diagnosing liver tumors. However, liver tumor classification is a difficult task due to factors such as noise, non-homogeneity, and significant appearance variations in cancerous tissue. In this study, we propose a novel approach to automatically segmenting and classifying liver tumors. Our proposed framework comprises three main components: a preprocessing unit to enhance picture contrast, a Masked Recurrent Convolutional Neural Network (RCNN) for liver segmentation, and a pixel-wise classification unit for identifying abnormalities in the liver. When our models are applied to the challenging MICCAI’2027 liver tumor segmentation (LITS) database, we achieve Dice similarity coefficients of 96% and 98% for liver segmentation and lesion identification, respectively. We also demonstrate the efficiency of our proposed framework by comparing it with similar strategies for tumor segmentations. The proposed approach achieved high accuracy, sensitivity, specificity, and F1 score parameters for liver segmentation and lesion identification. These results were evaluated using the Dice similarity coefficient and compared with similar strategies for tumor segmentation. Our approach holds promise for improving the accuracy and speed of liver tumor detection and diagnosis, which could have significant implications for patient outcomes.

RERN: Rich Edge Features Refinement Detection Network for Polycrystalline Solar Cell Defect Segmentation

High-performance defect segmentation techniques are essential for the high-quality manufacturing of polycrystalline solar cells. Edge detection is an effective technique to accurately locate the edge of defects. However, the existing methods ignore global channel information and the representation gap between multiscale features, inhibiting the ability of the network to aggregate discriminative features. In this paper, we propose a novel Rich Edge Features Refinement Detection Network (RERN) consisting of an encoder-decoder structure that captures rich discriminative edge feature representations by interactively exploring global spatial and channel context. To refine the dense global contextual information in the decoding layer, we propose a Bi-Directional Strip Refinement Attention (BSRA) adaptively capturing long-range spatial dependency with direction information and long-range channel dependency. BSRA is a lightweight and general module that can be easily inserted into the existing edge detection networks with a negligible computational burden. In addition, we release a polycrystalline solar cell defect edge (PSCDE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{1}$</tex-math></inline-formula> ) dataset that is the first high-quality segmentation database to advance the development of high-quality polycrystalline solar cell manufacturing. Our method achieves an ODS F-measure of 0.854 on the PSCDE dataset with strict evaluation criteria (maxDist=0.0015), outperforming existing state-of-the-art methods. To further verify the generalization ability of BSRA, we apply BSRA to other edge detection networks, and experiments show that the module further improves the accuracy of these methods on PSCDE.

Intelligent material classification of traditional Chinese medicine based on semantic analysis

A method for material classification of traditional Chinese medicines based on the physical properties of powder has been established by our research group. This method involves pre-treatment of traditional Chinese medicine decoction pieces, powder preparation, and determination of physical properties, being cumbersome. In this study, the word segmentation logic of semantic analysis was adopted to establish the thesaurus and local standardized semantic word segmentation database with the macroscopic and microscopic characteristics of 36 model traditional Chinese medicines as the basic data. The physical properties of these medicines have been determined and the classification of these medicines is clear in the cluster analysis. A total of 55 keywords for powdery, fibrous, sugary, oily, and brittle materials were screened by association rules and the set inclusion and exclusion criteria, and the weights of the keywords were calculated. Furthermore, the algorithms of the keyword matching scores and the computation rules of the single or multiple material classification were established for building the intelligent model of semantic analysis for the material classification. The semantic classification results of the other 35 TCMs except Pseudostellariae Radix(multi-material medicine) agreed with the clustering results based on the physical properties of the powder, with an agreement rate of 97.22%. In model validation, the prediction results of semantic classification of traditional Chinese medicines were consistent with the clustering results based on the physical properties of powder, with an agreement rate of 83.33%. The results showed that the method of material classification based on semantic analysis was feasible, which laid a foundation for the development of intelligent decision-making technology for personalized traditional Chinese medicine preparations.

Multi-scale convolution based breast cancer image segmentation with attention mechanism in conjunction with war search optimization

Numerous studies have explored different techniques for segmenting breast cancer images, in particular deep learning-based Computer-Aided Diagnosis (CAD) has recently netted attention. However, due to their down-and-out pursuance, the existing approaches like FCN (Fully Convolutional Network), PSPNet (Pyramid Scene Parsing Network), U-Net, and SegNet still required improvement for offering better semantic segmentation while identifying breast cancer. In this paper, the newly proposed breast cancer tumor segmentation method consists of four steps pre-processing, augmentation, segmenting image using multi-scale convolution and multi- attention mechanisms respectively. The proposed method utilizes the ResNet (Residual Network) backbone network with multi-scale convolution for feature map prediction. Also, the effectiveness of the multi-channel attention module with a pyramid dilated nodule is employed for semantic segmentation. Gated axial, position, and channel attention are combined to create a multi-channel attention mechanism. Additionally, War Search Optimization (WSO) algorithm is being utilized to enhance the accuracy of the segmented images. Experimentations are conducted on two datasets, viz., Breast Cancer Cell Segmentation Database and Breast Cancer Semantic Segmentation (BCSS) Database, with different existing networks. The effectiveness of the network is evaluated based on various criteria in terms of precision, accuracy, recall, (mean Intersection of Union), (Intersection of Union), etc.

(Retracted) Deep belief network-based image processing for local directional segmentation in brain tumor detection

Brain tumor data have recently been analyzed using deep learning techniques. Segmentation and classification of brain tumors and distinguishing tumorous and nontumorous cells are fascinating when it comes to distinguishing brain cell with tumorous and without tumorous and differentiate the tumorous cells to find their class label. For this purpose, segmentation is an appropriate method for classifying the brain image and it is commonly employed by researchers. To achieve accurate classification, it is necessary to begin with the extraction of relevant features. In this work, the probabilistic Fuzzy C-means (FCM) algorithm, is utilized to further refine the segmentation process. This analysis makes it possible to distinguish the regions of interest for magnetic resonance imaging (MRI) scan of the brain revealed, which provides a framework for reducing the dimensionality of MRI brain image. Local directional pattern (LDP) is applied to the segments after they have been segmented to extract the significant regions of features that have been identified by the segmentation method. Next to deep belief network, the features are provided, which determines whether the images are normal or abnormal, and whether MRI can be used to detect or rule out the presence of tumors. Experimentation is conducted with the help of the proposed method and brain tumor segmentation database; the accuracy has been assessed in relation to the highest percentage of 95.78% is obtained.

An Adaptive Self-Reduction Type-2 Fuzzy Clustering Algorithm for Pattern Recognition

Decisions in real-life can be adversely affected by various uncertainty-sources such as perception-diversity, data-structure and analytical tools. Fuzzy clustering can successfully handle the uncertainties while recognizing patterns in any given data. Nevertheless, type-1 fuzzy clustering techniques has uncertainties on account of precise-nature of primary memberships. Type-2 fuzzy clustering are preferred by many researchers to manage uncertainty in its type-1 version. In type-2 fuzzy clustering, order of fuzziness (fuzzifier) is obtained by interval-valued or general type-2 fuzzy sets. Interval type-2 fuzzy clustering can reduce the computational complexity of type-2 fuzzy set mathematics. However, general type-2 fuzzy clustering scrutinizes uncertainty in fuzzifier using linguistic sets. Interval and general type-2 fuzzy clustering algorithms include type-reduction approaches to obtain type-1 fuzzy sets. Besides, full type-2 fuzzy c-means can be used as a foundation approach in type-2 fuzzy inferences. Although this algorithm includes precise-fuzzifier, it gives a point of view to practically calculate secondary memberships. In this paper, an adaptive type-2 fuzzy clustering algorithm is proposed to manage the uncertainty-sources with a self-reduction procedure. Several numerical results and comparisons are given to demonstrate the achievement of this proposed algorithm. The performance of the proposed algorithm is compared with type-1 and type-2 versions for various multi-dimensional pattern sets from UCI-patterns, Berkeley segmentation database and a real-life application related to sustainable supplier selection in an automotive industry. Consequently, the proposed algorithm reveals fast, convenient and precise results.

An Efficient Syllable-Based Speech Segmentation Model Using Fuzzy and Threshold-Based Boundary Detection

To develop a high-quality TTS system, an appropriate segmentation of continuous speech into the syllabic units plays a vital role. The significant objective of this research work involves the implementation of an automatic syllable-based speech segmentation technique for continuous speech of the Hindi language. Here, the parameters involved in the segmentation process are optimized to segment the speech syllables. In addition to this, the proposed iterative splitting process containing the optimum parameters minimizes the deletion errors. Thus, the optimized iterative incorporation can discard more insertions without merging the frequent non-iterative incorporation. The mixture of optimized iterative and iterative incorporation provides the best accuracy with the least insertion and deletion errors. The segmentation output based on different text signals for the proposed approach and other techniques namely GA, PSO and SOM is accurately segmented. The average accuracy obtained for the proposed approach is high with 97.5% than GA, PSO and SOM. The performance of the proposed algorithm is also analyzed and gives better-segmented accuracy when compared with other state-of-the-art methods. Here, the syllable-based segmented database is suitable for the speech technology system for Hindi in the travel domain.

Supplier inventory leanness and financial performance

AbstractNumerous studies have examined the relationship between inventory management and financial performance. However, the focus of such empirical work has primarily been on how a firm's own inventory characteristics affect its performance. Our objective is to extend this body of literature beyond the firm‐level. We draw on inventory theory and resource‐based theories to hypothesize about the effect of supplier inventory leanness on a focal firm's financial performance and how supplier and focal firm inventory leanness interact to affect such outcomes. We test our hypotheses using a large panel dataset of supplier‐focal firm relationships obtained from Compustat's Customer Segment database and aggregated to the focal firm‐quarter level, as well as firm financial information from Compustat's Fundamentals Quarterly database. The econometric analyses provide evidence that supplier inventory leanness influences focal firm financial performance indirectly through the interaction with the firm's own inventory leanness. In particular, our estimation results detail how supplier inventory leanness affects the non‐linearity of the focal firm's inventory leanness‐financial performance relationship and its optimal inventory leanness level. The findings broaden the scope of empirical inventory literature and highlight supplier inventory leanness as an important consideration in firm‐level inventory decision making.

Uncertainty‐guided symmetric multilevel supervision network for 3D left atrium segmentation in late gadolinium‐enhanced MRI

Atrial fibrillation (AF) is a common arrhythmia and requires volumetric imaging to guide the therapy procedure. Late gadolinium-enhanced magnetic resonance imaging (LGE MRI) is an efficient noninvasive technology for imaging the diseased heart. Three-dimensional segmentation of the left atrium (LA) in LGE MRI is a fundamental step for guiding the therapy of patients with AF. However, the low contrast and fuzzy surface of the LA in LGE MRI make accurate and objective LA segmentation a challenge. The purpose of this study is to propose an automatic and efficient LA segmentation model based on a convolutional neural network to obtain a more accurate predicted surface and improve the LA segmentationresults. In this study, we proposed an uncertainty-guided symmetric multilevel supervision (SML) network for 3D LA segmentation in LGE MRI. First, we constructed an SML structure to combine the corresponding features from the encoding and decoding stages to learn the multiscale representation of LA. Second, we formulated the discrepancy of predictions of our model as model uncertainty. Then we proposed an uncertainty-guided objective function to further increase the segmentation accuracy on thesurface. We evaluated our proposed model on the public LA segmentation database using four universal metrics. The proposed model achieved Hausdorff Distance (HD) of 11.68 mm, average symmetric surface distance of 0.92 mm, Dice score of 0.92, and Jaccard of 0.85. Compared with state-of-the-art models, our model achieved the best HD that is sensitive to surface accuracy. For the other three metrics, our model also achieved better or comparableperformance. We proposed an efficient automatic LA segmentation model that consisted of an SML structure and an uncertainty-guided objective function. Compared to other models, we designed an additional supervision branch in the encoding stage to learn more detailed representations of LA while learning global context information through the multilevel structure of each supervision branch. To address the fuzzy surface challenge of LA segmentation in LGE MRI, we leveraged the model uncertainty to enhance the distinguishing ability of the model on the surface, thereby the predicted accuracy of the LA surface can be further increased. We conducted extensive ablation and comparative experiments with state-of-the-art models. The experiment results demonstrated that our proposed model could handle the complex structure of LA and had superior advantages in improving the segmentation performance on thesurface.

Multi-scanner and multi-modal lumbar vertebral body and intervertebral disc segmentation database

Magnetic resonance imaging (MRI) is widely utilized for diagnosing and monitoring of spinal disorders. For a number of applications, particularly those related to quantitative MRI, an essential step towards achieving reliable and objective measurements is the segmentation of the examined structures. Performed manually, such process is time-consuming and prone to errors, posing a bottleneck to its clinical applicability. A more efficient analysis would be achieved by automating a segmentation process. However, routine spine MRI acquisitions pose several challenges for achieving robust and accurate segmentations, due to varying MRI acquisition characteristics occurring in data acquired from different sites. Moreover, heterogeneous annotated datasets, collected from multiple scanners with different pulse sequence protocols, are limited. Thus, we present a manually segmented lumbar spine MRI database containing a wide range of data obtained from multiple scanners and pulse sequences, with segmentations of lumbar vertebral bodies and intervertebral discs. The database is intended for the use in developing and testing of automated lumbar spine segmentation algorithms in multi-domain scenarios.

Automatic tunnel lining crack evaluation and measurement using deep learning

A tunnel is an imperative underground passageway that supports fast and uninterrupted transportation. Over time, various factors, such as ageing, topographical changes, and excessive force, slowly affect the tunnel's internal structure, which causes tunnel defects that can reduce the structure's stability and eventually lead to enormous damage. Therefore, the tunnels need to be checked regularly to detect and fix the cracks promptly. Earlier inspection approaches mainly relied on the operators who directly observed videos to detect the cracks and determine their seriousness, which is laborious, error-prone, and tedious. This research suggests a deep learning-based tunnel lining crack segmentation framework for tunnel images taken by high-resolution cameras. The primary contributions are (1) a lining crack segmentation framework, which is motivated by U-Net architecture, where the encoder is replaced by a ResNet-152 model, (2) the automated measurement of the segmented cracks, which include length, thickness, and type, and (3) a huge lining crack segmentation database. The experimental results showed that the framework obtained comparable performance compared to existing crack segmentation models and supported the automated measurement of the segmented cracks.

Glaucoma Detection Using Image Processing and Supervised Learning for Classification.

A difficult challenge in the realm of biomedical engineering is the detection of physiological changes occurring inside the human body, which is a difficult undertaking. At the moment, these irregularities are graded manually, which is very difficult, time-consuming, and tiresome due to the many complexities associated with the methods involved in their identification. In order to identify illnesses at an early stage, the use of computer-assisted diagnostics has acquired increased attention as a result of the requirement of a disease detection system. The major goal of this proposed work is to build a computer-aided design (CAD) system to help in the early identification of glaucoma as well as the screening and treatment of the disease. The fundus camera is the most affordable image analysis modality available, and it meets the financial needs of the general public. The extraction of structural characteristics from the segmented optic disc and the segmented optic cup may be used to characterize glaucoma and determine its severity. For this study, the primary goal is to estimate the potential of the image analysis model for the early identification and diagnosis of glaucoma, as well as for the evaluation of ocular disorders. The suggested CAD system would aid the ophthalmologist in the diagnosis of ocular illnesses by providing a second opinion as a judgment made by human specialists in a controlled environment. An ensemble-based deep learning model for the identification and diagnosis of glaucoma is in its early stages now. This method's initial module is an ensemble-based deep learning model for glaucoma diagnosis, which is the first of its kind ever developed. It was decided to use three pretrained convolutional neural networks for the categorization of glaucoma. These networks included the residual network (ResNet), the visual geometry group network (VGGNet), and the GoogLeNet. It was necessary to use five different data sets in order to determine how well the proposed algorithm performed. These data sets included the DRISHTI-GS, the Optic Nerve Segmentation Database (DRIONS-DB), and the High-Resolution Fundus (HRF). Accuracy of 91.11% for the PSGIMSR data set and the sensitivity of 85.55% and specificity of 95.20% for the suggested ensemble architecture on the PSGIMSR data set were achieved. Similarly, accuracy rates of 95.63%, 98.67%, 95.64%, and 88.96% were achieved using the DRIONS-DB, HRF, DRISHTI-GS, and combined data sets, respectively.

Deep Learning Framework to Detect Ischemic Stroke Lesion in Brain MRI Slices of Flair/DW/T1 Modalities

Ischemic stroke lesion (ISL) is a brain abnormality. Studies proved that early detection and treatment could reduce the disease impact. This research aimed to develop a deep learning (DL) framework to detect the ISL in multi-modality magnetic resonance image (MRI) slices. It proposed a convolutional neural network (CNN)-supported segmentation and classification to execute a consistent disease detection framework. The developed framework consisted of the following phases; (i) visual geometry group (VGG) developed VGG16 scheme supported SegNet (VGG-SegNet)-based ISL mining, (ii) handcrafted feature extraction, (iii) deep feature extraction using the chosen DL scheme, (iv) feature ranking and serial feature concatenation, and (v) classification using binary classifiers. Fivefold cross-validation was employed in this work, and the best feature was selected as the final result. The attained results were separately examined for (i) segmentation; (ii) deep-feature-based classification, and (iii) concatenated feature-based classification. The experimental investigation is presented using the Ischemic Stroke Lesion Segmentation (ISLES2015) database. The attained result confirms that the proposed ISL detection framework gives better segmentation and classification results. The VGG16 scheme helped to obtain a better result with deep features (accuracy &gt; 97%) and concatenated features (accuracy &gt; 98%).

Triple-Input-Unsupervised neural Networks for deformable image registration

• The TIUNet is the first medical image registration CNN with triple-input structure. • Three kinds of loss functions are conducted to supporting the novel structure. • The deformation consistency of two registration results is novelly considered. • The training data could be naturally augmented with the triple-input structure. Deep-learning frameworks have been widely used in deformable image registration(DIR), which is a fundamental step in medical image analysis. In traditional deep-learning-based DIR methods, pairwise registration is conducted for each image, cooperated with a dual-input structure. They only considered that the registered images need to be as similar as possible to the target image, while ignored that all registered images to the same target image should have similar appearance too. Therefore, in this paper we add the deformation constraints between different registered images to the registration network in order to enhance the appearance consistency of the registered images and improve the registration accuracy. Specifically, a Triple-Input-Unsupervised neural Network (TIUNet) is proposed to register two source images to a common target image at the same time. Then, a novel deformation-strengthened loss is designed to simulate the deformation constraint between the two warped source images. Finally, the deformation constraints between the source images and the target image are jointly optimized to obtain the deformation field model. The experimental results on the available tissue segmentation database demonstrate that our proposed TIUNet method shows a superior performance of deformation field model over several other advanced deformable image registration algorithms. Moreover, benefited from the triple-input structure, our proposed TIUNet method could expand the available training sets naturally and is suitable for the registration task with fewer training data.

Nuclei Segmentation on Histopathology Images of Breast Carcinoma.

With the use of computer-aided diagnostic systems, the automatic detection and segmentation of the cell nuclei have become essential in pathology due to cellular nuclei counting and nuclear pleomorphism analysis are critical for the classification and grading of breast cancer histopathology. This work describes a methodology for automatic detection and segmentation of cellular nuclei in breast cancer histopathology images obtained from the BreakHis database, the Standford tissue microarray database, and the Breast Cancer Cell Segmentation database. The proposed scheme is based on the characterization of Hematoxylin and Eosin (H&E) staining, size, and shape features. In addition, we use the information obtained from morphological transformations and adaptive intensity adjustments to detect and separate each cell nucleus detected in the image. The segmentation was carried out by testing the proposed methodology in a histological breast cancer database that provides the associated groundtruth segmentation. Subsequently, the Sørensen-Dice similarity coefficient was calculated to analyze the suitability of the results.Clinical relevance- In this work, the detection and segmentation of cell nuclei in breast cancer histological images are carried out automatically. The method can identify cell nuclei regardless of variations in the level of staining and image magnification. Moreover, a granulometric analysis of the components allows identifying cell clumps and segment them into individual cell nuclei. Improved identification of cell nuclei under different image conditions was demonstrated to reach a sensitivity average of 0.76 ± 0.12. The results provide a base for further and complex processes such as cell counting, feature analysis, and nuclear pleomorphism, which are relevant tasks in the evaluation and diagnostic performed by the expert pathologist.

Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI.

Assessment of cardiovascular disease (CVD) with cine magnetic resonance imaging (MRI) has been used to non-invasively evaluate detailed cardiac structure and function. Accurate segmentation of cardiac structures from cine MRI is a crucial step for early diagnosis and prognosis of CVD, and has been greatly improved with convolutional neural networks (CNN). There, however, are a number of limitations identified in CNN models, such as limited interpretability and high complexity, thus limiting their use in clinical practice. In this work, to address the limitations, we propose a lightweight and interpretable machine learning model, successive subspace learning with the subspace approximation with adjusted bias (Saab) transform, for accurate and efficient segmentation from cine MRI. Specifically, our segmentation framework is comprised of the following steps: (1) sequential expansion of near-to-far neighborhood at different resolutions; (2) channel-wise subspace approximation using the Saab transform for unsupervised dimension reduction; (3) class-wise entropy guided feature selection for supervised dimension reduction; (4) concatenation of features and pixel-wise classification with gradient boost; and (5) conditional random field for post-processing. Experimental results on the ACDC 2017 segmentation database, showed that our framework performed better than state-of-the-art U-Net models with 200× fewer parameters in delineating the left ventricle, right ventricle, and myocardium, thus showing its potential to be used in clinical practice.Clinical relevance- Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac MR images is a common clinical task to establish diagnosis and prognosis of CVD.

STAGES OF CREATING PARALLEL CORPUS OF ENGLISH-UZBEK SIMILES

In recent years, corpus linguistics has been mentioned in the scientific literature as the main tool for the elaboration of dictionaries and grammar manuals, the creation of corpus analysis, the practical use of corpus, as well as the statistical study of linguistic analysis using corpus. Articles, textbooks, manuals on the creation of general and special electronic corpus have been published, but as the Uzbek language is a relatively new field of corpus linguistics, there is a need for research on some issues. The development of corpus linguistics and the increasing focus on statistical methods of processing linguistic materials have led to the development of a number of techniques related to the usage of parallel or similar texts in different languages. This article is devoted to the stages of creating bilingual parallel corpus and explains the peculiarities of creating parallel corpus of texts. The creation of parallel corpus also has its own research stages. Statistical research in linguistics has a long history, especially since the advent of computers in the fifties of the twentieth century, research in this area has grown rapidly. The purpose of the study is to analyze the scientific views on the creation of the linguistic supply of the program of translation of similes on the basis of parallel texts (English-Uzbek, Uzbek-English), to study the linguistic basis and to study the lexical-semantic relationship of similes in bilingual vocabulary. This article deals with the creation of a parallel corpus of literary texts with English and Uzbek similes, analysis of lexical-semantic relations of similes, formation of a database of text segments based on translation alternatives of similes, scientific proof of linguistic models of English similes in translation and the effect of mentality is scientifically substantiated in the example of the translation of similes. A bilingual simile dictionary also plays an important role in creating a parallel corpus of similes. There is a great need for electronic dictionaries in the field of literary translation. The creation of bilingual and multilingual dictionaries, which embody the subtle nuances of the word, serves as a very important resource for translators, writers, poets, and language users.

Edge Detection in Digital Images Using Guided L0 Smoothen Filter and Fuzzy Logic

Image segmentation is an important process in computer vision. Recently fuzzy logic based edge detection is heavily investigated as by changing the number of rules edge detection can be improved. However, due to large colour variations in the images false edges are detected and even using fuzzy rules they cannot be reduced significantly. These falsely detected edges can be controlled by using smoothen filter while controlling the degree of smoothness. This paper, presents fuzzy logic based edge detection mechanism while using Guided L0 smoothen filter for the smoothening of image under various degree of smoothens. Simulation results for edge detection is presented for Canny, Sobel, Fuzzy logic based edge detection and finally fuzzy logic edge detection with inclusion of L0 smoothen filter. The results are compared with classical and modern methods. Simulation is performed on Berkley Segmentation Database (BSD) and USC-SIPI Image Database while considering more than 100 images. The obtained F-measure is as high as 0.848.