Statistical Segmentation Research Articles

OMS-SLAM: dynamic scene visual SLAM based on object detection with multiple geometric feature constraints and statistical threshold segmentation

Abstract SLAM technology is crucial to robot navigation. Despite the good performance of traditional SLAM algorithms in static environments, dynamic objects typically exist in realistic operating environments. These objects can lead to misassociated features, which in turn considerably impact the system’s localization accuracy and robustness. To better address this challenge, we have proposed the OMS-SLAM. In OMS-SLAM, we adopted the YOLOv8 target detection network to extract object information from environment and designed a dynamic probability propagation model that is coupled with target detection and multiple geometric constrains to determine the dynamic objects in the environment. For the identified dynamic objects, we have designed a foreground image segmentation algorithm based on depth image histogram statistics to extract the object contours and eliminate the feature points within these contours. We then use the GMS (Grid-based Motion Statistics) matching pair as the filtering strategy to enhance the quality of the feature points and use the enhanced feature points for tracking. This combined method can accurately identify dynamic objects and extract related feature points, significantly reducing its interference and consequently enhancing the system's robustness and localization accuracy. We also built static dense point cloud maps to support advanced tasks of robots. Finally, through testing on the high-speed dataset of TUM RGB-D, it was found that the root mean square error of the Absolute Trajectory Error (ATE) in this study decreased by an average of 97.10%, compared to ORB-SLAM2. Moreover, tests in real-world scenarios also confirmed the effectiveness of the OMS-SLAM algorithm in dynamic environments.

Statistical segmentation model for accurate electrode positioning in Parkinson's deep brain stimulation based on clinical low-resolution image data and electrophysiology.

Deep Brain Stimulation (DBS), applying chronic electrical stimulation of subcortical structures, is a clinical intervention applied in major neurologic disorders. In order to achieve a good clinical effect, accurate electrode placement is necessary. The primary localisation is typically based on presurgical MRI imaging, often followed by intra-operative electrophysiology recording to increase the accuracy and to compensate for brain shift, especially in cases where the surgical target is small, and there is low contrast: e.g., in Parkinson's disease (PD) and in its common target, the subthalamic nucleus (STN). We propose a novel, fully automatic method for intra-operative surgical navigation. First, the surgical target is segmented in presurgical MRI images using a statistical shape-intensity model. Next, automated alignment with intra-operatively recorded microelectrode recordings is performed using a probabilistic model of STN electrophysiology. We apply the method to a dataset of 120 PD patients with clinical T2 1.5T images, of which 48 also had available microelectrode recordings (MER). The proposed segmentation method achieved STN segmentation accuracy around dice = 0.60 compared to manual segmentation. This is comparable to the state-of-the-art on low-resolution clinical MRI data. When combined with electrophysiology-based alignment, we achieved an accuracy of 0.85 for correctly including recording sites of STN-labelled MERs in the final STN volume. The proposed method combines image-based segmentation of the subthalamic nucleus with microelectrode recordings to estimate their mutual location during the surgery in a fully automated process. Apart from its potential use in clinical targeting, the method can be used to map electrophysiological properties to specific parts of the basal ganglia structures and their vicinity.

An overview of statistical and neural-based line segmentation methods for offline handwriting recognition task

The object of the research is the line segmentation task. To recognize the handwritten text from the documents in image format offline handwriting recognition technology is used. The text recognizer module accepts input as separate lines, so one of the important preprocessing steps is the detection and splitting of all handwritten text into distinct lines. In this paper, the handwritten text line segmentation task, its requirements, problems, and challenges are examined. Two main approaches for this task that are used in modern recognition systems are reviewed. These approaches are statistical projection-based methods and neural-based methods. Multiple works and research papers for each type of approach are reviewed analyzing their strengths and weaknesses considering the described tasks, constraints, and input data peculiarities. Overall acquired results are formed in a single table for comparison. Based on the latest works that utilize deep neural networks the new possibilities of using these methods in recognition systems are described that were unavailable with traditional statistical segmentation approaches. The constructive conclusions are made based on the review, describing the main pros and cons of these two approaches for the line segmentation task. These results can be further used for the correct selection of suitable methods in handwriting recognition systems to improve their performance and quality, and for further research in this area.

Statistical word segmentation succeeds given the minimal amount of exposure.

One of the first tasks in language acquisition is word segmentation, a process to extract word forms from continuous speech streams. Statistical approaches to word segmentation have been shown to be a powerful mechanism, in which word boundaries are inferred from sequence statistics. This approach requires the learner to represent the frequency of units from syllable sequences, though accounts differ on how much statistical exposure is required. In this study, we examined the computational limit with which words can be extracted from continuous sequences. First, we discussed why two occurrences of a word in a continuous sequence is the computational lower limit for this word to be statistically defined. Next, we created shortsyllable sequences that contained certain words either two or four times. Learners were presented with these syllable sequences one at a time, immediately followed by a test of the novel words from these sequences. We found that, with the computationally minimal amount of two exposures, words were successfully segmented from continuous sequences. Moreover, longer syllable sequences providing four exposures to words generated more robust learning results. The implications of these results are discussed in terms of how learners segment and store the word candidates from continuous sequences.

Perceptual intake explains variability in statistical word segmentation

One of the first problems in language learning is to segment words from continuous speech. Both prosodic and distributional information can be useful, and it is an important question how the two types of information are integrated. In this paper, we propose that the distinction between input (the statistical properties of the syllable sequence), and intake (how learners perceptually represent the syllable sequence) is a useful framework to integrate different sources of information. We took a novel approach, observing how a large number of syllable sequences were segmented. These sequences had the same transitional probability information for finding word boundaries but different syllables in them. We found large variability in the performance of the segmentation task, suggesting that factors other than the statistical properties of sequences were at play. This variability was explored using the input/intake asymmetry framework, which predicted that factors that shaped the representation of different syllable sequences could explain the variability of learning. We examined two factors, the saliency of the rhythm in these syllable sequences and how familiar the novel word forms in the sequence were to the existing lexicon. Both factors explained the variance in the learnability of different sequences, suggesting that processing of the sequences shaped learning. The implications of these results to computational models of statistical learning and broader implications to language learning were discussed.

Unsupervised statistical image segmentation using bi-dimensional hidden Markov chains model with application to mammography images

Hidden Markov chain (HMC) models have been widely used in unsupervised image segmentation. In these models, there is a double process; a hidden one noted X and an observed one, which is often one-dimensional, noted Y. The latter is constituted by pixels of a noisy image after transforming its bi-dimensional form into a mono-dimensional sequence. In this context, these models run into a problem of relationships between pixels which is often solved by applying curves such as the Hilbert-Peano scan when modeling the image under study. We propose enriching the HMC model by introducing a second component to the observed process Y based on the average of two observations which are neighbors in the image but are not in the chain of each considered pixel. This gives a bi-dimensional HMC model which has the same structure as the classical model except for the two-dimensional case of the low modeling noise. The estimation of the parameters of this model is carried out by using a three-algorithm approach: Bayesian one based mainly on the Markov Chain Monte Carlo (MCMC) methods, Expectation–Maximization (EM), and Iterative Conditional Estimation (ICE). We apply the final Bayesian decision criteria Marginal Posterior Mode to come up with a final configuration of the result X. The proposed model is compared to the classical HMC model in combination with the Hilbert-Peano scan numerically through simulated data and visually through synthetic and mammogram images.

The cross‐sectional association between peripheral TNF‐∝, white matter hyperintensities, and cognitive outcomes in the HABS‐HD study

AbstractBackgroundLow‐grade inflammation appears with advance age, with the end result of an increase ratio of pro to anti‐inflammatory cytokines. Inflammatory cytokines are considered cerebrovascular disease markers, but the association of cytokine levels with poorer cognition is not conclusive. TNFα, a pro‐inflammatory cytokine, may have a role on neurodegenerative diseases, such as Alzheimer’s disease. The effect of peripheral TNFα on brain structure and cognitive outcomes in cognitively normal older adults is not well understood. A few studies have described this relationship in White cohorts, but there is a lack of research in minorities like Mexican Americans.MethodWe studied 510 cognitively normal Mexican Americans from the Health and Aging Brain Study (HABS‐HD) with signed consent, fasting blood samples, 3T MRI, clinical evaluation, and cognitive testing. TNFα was assayed via a multiple‐plex biomarker assay platform using electrochemiluminescence. White matter hyperintensities (WMH) volume was measured from FLAIR using the Statistical Parametric Mapping Lesion Segmentation Tool. Pearson correlations were used to assess the association between TNFα with WMH, global cognition (MMSE), memory (SEVLT), executive function and attention (Trails B and digit symbol substitution) measures. Linear regression was used to predict the effect of TNF‐α on WMH volume and cognitive measures. APOE4 status was assessed, and age, sex, systolic blood pressure and education were entered as covariates in the models.ResultSeventy percent of the sample were female. Sample means were 62.84 years of age, 9.94 years of education, and 137.33 for systolic blood pressure (SBP). There was not significant difference in TNFα and WMH values between APOE4 carriers and non‐carriers. TNFα had a positive significant correlation with age, education, SBP, WMH volume, and digit symbol substitution scores. Linear regression showed no association between TNFα value and WMH volume nor cognitive measures, with the exception of digit symbol substitution (B = ‐2.62; t = ‐2.03; p = 0.04).ConclusionThese results suggest TNFα peripheral values are not related to WMH volume, but were related to executive‐attention measures. Prospective studies are necessary to evaluate the use of TNFα as a potential biomarker of brain health in Mexican Americans.

Statistical word segmentation: Anchoring learning across contexts.

The present experiments were designed to assess infants' abilities to use syllable co-occurrence regularities to segment fluent speech across contexts. Specifically, we investigated whether 9-month-old infants could use statistical regularities in one speech context to support speech segmentation in a second context. Contexts were defined by different word sets representing contextual differences that might occur across conversations or utterances. This mimics the integration of information across multiple interactions within a single language, which is critical for language acquisition. In particular, we performed two experiments to assess whether a statistically segmented word could be used to anchor segmentation in a second, more challenging context, namely speech with variable word lengths. The results of Experiment 1 were consistent with past work suggesting that statistical learning may be hindered by speech with word-length variability, which is inherent to infants' natural speech environments. In Experiment 2, we found that infants could use a previously statistically segmented word to support word segmentation in a novel, challenging context. We also present findings suggesting that this ability was associated with infants' early word knowledge but not their performance on a cognitive development assessment.

When language-general and language-specific processes are in conflict: The case of sub-syllabic word segmentation in toddlers.

Infants use statistics-based word segmentation strategies from the preverbal stage. Statistical segmentation is, however, constrained by the Onset Bias, a language-universal principle that disfavors segmentation that harms syllable integrity. Children eventually learn language-specific exceptions to this principle. For instance, sub-syllabic parsing occurs for vowel-initial words in French liaison contexts, that is, when a word's final consonant surfaces as the following word's syllabic onset (e.g., /n/ in un /n/éléphant). In past research, French-learning 24-month-olds succeeded in parsing a vowel-initial pseudo-word surfacing with variable liaison consonants. This study further investigated infants' liaison representation, its potential impacts on parsing, and its interaction with the Onset Bias. In Experiments 1 and 2, French-learning 24-month-olds were familiarized with pseudo-words with variable liaison-like versus nonliaison-like onset consonants, preceded by words that cannot trigger those onsets (e.g., un zonche; un gonche). We found no mis-segmentation as vowel-initial and successful segmentation as consonant-initial. In Experiment 3, when the preceding words could trigger a liaison consonant that matched the onset of the following word (e.g., un nonche), infants showed a vowel-initial mis-interpretation, against the Onset Bias, revealing an effect of liaison knowledge. These results demonstrate that toddlers balance their use of language-general principles/strategies and language-specific knowledge during early acquisition.

Locating and categorizing inefficient communication patterns in HPC systems using inter-process communication traces

High Performance Computing (HPC) systems are used in a variety of industrial and research sectors to solve complex problems that require powerful computing platforms. For these systems to remain reliable, we should be able to debug and analyze their behavior in order to detect root causes of potential poor performance. Execution traces hold important information regarding the events and interactions among communicating processes, which are essential for the debugging of inter-process communication. Traces, however, tend to be considerably large, hindering their applicability. In previous work, we presented an approach for automatically detecting communication patterns and segmenting large HPC traces into execution phases. The goal is to reduce the effort of analyzing traces by allowing software analysts to focus on smaller parts of interest. In this paper, we propose an approach for detecting and localizing inefficient communication patterns using statistical and trace segmentation methods. In addition, we use the Analytic Hierarchy Process to categorize slow communication patterns based on their severity and complexity levels. Using our approach, an analyst can quickly locate slow communication patterns that may be the cause of important performance problems. We show the effectiveness of our approach by applying it to large traces from three HPC systems.

Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications.

Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system, characterized by the appearance of focal lesions in the white and gray matter that topographically correlate with an individual patient's neurological symptoms and signs. Magnetic resonance imaging (MRI) provides detailed in-vivo structural information, permitting the quantification and categorization of MS lesions that critically inform disease management. Traditionally, MS lesions have been manually annotated on 2D MRI slices, a process that is inefficient and prone to inter-/intra-observer errors. Recently, automated statistical imaging analysis techniques have been proposed to detect and segment MS lesions based on MRI voxel intensity. However, their effectiveness is limited by the heterogeneity of both MRI data acquisition techniques and the appearance of MS lesions. By learning complex lesion representations directly from images, deep learning techniques have achieved remarkable breakthroughs in the MS lesion segmentation task. Here, we provide a comprehensive review of state-of-the-art automatic statistical and deep-learning MS segmentation methods and discuss current and future clinical applications. Further, we review technical strategies, such as domain adaptation, to enhance MS lesion segmentation in real-world clinical settings.

IMPACT OF BANKING SECTOR CREDITS ON NET SME FORMATION IN TURKEY

Purpose: This study examines the impact of banking sector credit on the growth of small and medium enterprises in Turkey. The main objective of the study is to investigate whether state-equity bank group or private-equity bank group credits have significant impact on the growth of net Small and Medium Enterprise (SME) formation a regional, urban and sectoral basis. Methodology: As part of the methodology, quarterly data between 2010-2019 was collected and used in the study for Panel Cointegration Model. Findings: The formulated hypotheses reveal that private-equity bank credits have significant impact on the growth of small and medium enterprises in Turkey. For statistical region segmentation, city segmentation and sectoral segmentation, private equity bank group credit coefficient is higher than state equity bank group. LPCT (private equity bank group city based logarithmic total credits) variable has a higher coefficient than LSCT (state equity bank group city based logarithmic total credits) variable. Originality: During literature review, no work with these variables could be found. This work is expected to fill a gap in the literature.

(Retracted) Infrared image filtering and enhancement processing method based upon image processing technology

Due to the processing method used in many existing image enhancement algorithms, some inherent noise in the image is amplified when the image is enhanced globally or locally. The quality of infrared images directly affects the wide application of infrared imaging technology, yet the quality of infrared image depends largely on the advanced nature and correct application of infrared image processing technology (IPT). This research mainly discusses the infrared image filtering enhancement processing method based on IPT. We briefly summarize the research significance, application fields and research status of the image registration and enhancement. We summarize the commonly used methods. We use a contrast saliency filter to obtain the region of interest and perform statistical segmentation on the region to make it more suitable for observation and subsequent processing. No matter what the background environment is, the image detail enhancement algorithm enhances all the details in the image in a balanced manner. Therefore, we adopt the scheme of separating the background and the details to avoid the influence of the background difference on the detail display effect in other enhancement methods. Objective experiments have also proved that it is difficult to achieve consistency between subjective and objective evaluation of image quality. Therefore, the evaluation of processed image quality still needs to combine subjective visual effects and objective evaluation. We use optimized hierarchical differential expression theory to solve the acquired saliency region and obtain the differential vector that realizes the gray difference amplification of this region. The dynamic range of the original image is very narrow, roughly concentrated in [0, 50]. The gray range of the image processed by the enhancement algorithm has been well expanded, basically covering [0, 255]. The proposed infrared image enhancement method achieves a better visual enhancement effect.

Combination of Digital Image Processing and Statistical Data Segmentation to Enhance SPR and SPRi Sensor Responses

The work reports the combination of basic digital image processing (DIP) techniques and statistical segmentation strategy (SDS) to improve surface plasmon resonance curve (SPRc) and SPR imaging (SPRi) sensors' performance. The SPR image is used for sensing and monitoring biological events in the so-called SPR imaging process. In the traditional SPR process based on the attenuated total reflection (ATR) method, the image is used to create the SPR curve, and the curve features tracking is employed on sensing applications. The SPR curve features are enhanced after the pixels of the SPR image have been processed with low-complexity filters in the spatial domain (brightness, contrast, threshold, and morphological). The bootstrap was used as a statistical processing approach, selecting lines and columns from the image that was less affected by imperfections and noises in the image detector, and consequently reducing the SPR sensor instrumentation disturbances. Experimental tests with reversible binding water-mixture were performed, and both image and statistical processing were reported. The combination of DIP and SDS approaches improves the extraction of the curve features, increasing the performance in terms of resonance position sensitivity to 81%.

Intention to vaccinate children for COVID-19: A segmentation analysis among Medicaid parents in Florida

COVID-19 vaccines have been granted emergency use authorization for children ages 5 years and older. To understand how racially and ethnically diverse parents of young children enrolled in Medicaid feel about a prospective COVID-19 vaccine for their children, we administered an online survey that included both close-ended and open-ended items to a statewide sample in Florida (n = 1951). We used quantitative responses to conduct a statistical audience segmentation analysis that identified five distinct sub-groups that varied widely in the likelihood that they would get a COVID-19 vaccine for their child. Qualitative responses were used to illustrate differences between the groups. The youngest Black and White mothers were least likely to vaccinate their child (24%), followed by Black and White mothers in their early 30s (36%), younger Hispanic and mixed-race or other race parents (45%), older mothers (48%) and older fathers (71%). Unique challenges to building vaccine confidence emerged for each group. The youngest Black and White mothers were more likely to report their lives being worse during the COVID-19 pandemic, were far more negative and less positive about a COVID-19 vaccine, and were more concerned about paying bills than preventing COVID-19. Younger Hispanic and mixed-race parents were less negative, but more likely to use emotional language (e.g., scared, nervous, worried) talking about a COVID-19 vaccine, and more likely to report that protecting their child's health was their top concern. Recommendations are made for applying the insights gained in outreach and education efforts.

Tensor based statistical segmentation of green vegetation canopy images

The increase of developments of electronic and computer has been resulted with the commonly use of these technologies in everyday life in many applications. One of these applications is emerged in agricultural applications as precision agriculture studies. The use of technology in agriculture has lots of benefits such as energy saving, yield increase, time saving and etc. In this study, a novel learning based, pixel-wise segmentation method which uses Common Vector Approach (CVA) for the first time in the literature for segmentation is proposed. In the proposed method, first of all color regions belong to both vegetation and soil are manually cropped and then three different color space representations such as HSV, Lab and Luv of RGB images for each color region are encoded as 3rd color tensors. Then by unfolding the color tensor in the mode-3 direction, 2-D color matrix is obtained. The columns of this 2-D color matrix are the dimensional vectors that include (H, S a, b, u, v) components of HSV, Lab and Luv color spaces of an image pixel, and each column vector is accepted as an object. By applying CVA in object space consisting of column vectors of 2-D color matrix, a common color vector which represents common properties of that color region is obtained and used for segmentation purposes. In the experimental studies two different datasets proposed for open computer vision tasks in precision agriculture before in the literature are used. Three different experimental studies are performed for different dataset combinations in terms of training set and the test set. The performance of the proposed method has been compared with the performance of a deep learning method, Convolutional Neural Networks (CNN) based semantic segmentation method. In all of the three experimental studies proposed method achieves extremely high performance according to CNN, especially in the second and in the third experimental studies where dataset combinations include the two of the datasets.

A Baseline Statistical Method for Robust User-Assisted Multiple Segmentation

Recently, several image segmentation methods that welcome and leverage different types of user assistance have been developed. In these methods, the user inputs can be provided by drawing bounding boxes over image objects, drawing scribbles or planting seeds that help to differentiate between image boundaries or by interactively refining the missegmented image regions. Due to the variety in the types and the amounts of these inputs, relative assessment of different segmentation methods becomes difficult. As a possible solution, we propose a simple yet effective, statistical segmentation method that can handle and utilize different input types and amounts. The proposed method is based on robust hypothesis testing, specifically the DGL test, and can be implemented with time complexity that is linear in the number of pixels and quadratic in the number of image regions. Therefore, it is suitable to be used as a baseline method for quick benchmarking and assessing the relative performance improvements of different types of user-assisted segmentation algorithms. We provide a mathematical analysis on the operation of the proposed method, discuss its capabilities and limitations, provide design guidelines and present simulations that validate its operation.

Cognitive mechanisms of statistical learning and segmentation of continuous sensory input

Two classes of cognitive mechanisms have been proposed to explain segmentation of continuous sensory input into discrete recurrent constituents: clustering and boundary-finding mechanisms. Clustering mechanisms are based on identifying frequently co-occurring elements and merging them together as parts that form a single constituent. Bracketing (or boundary-finding) mechanisms work by identifying rarely co-occurring elements that correspond to the boundaries between discrete constituents. In a series of behavioral experiments, I tested which mechanisms are at play in the visual modality both during segmentation of a continuous syllabic sequence into discrete word-like constituents and during recognition of segmented constituents. Additionally, I explored conscious awareness of the products of statistical learning—whole constituents versus merged clusters of smaller subunits. My results suggest that both online segmentation and offline recognition of extracted constituents rely on detecting frequently co-occurring elements, a process likely based on associative memory. However, people are more aware of having learnt whole tokens than of recurrent composite clusters.

Segmenting children’s active school travel behaviour: insights on caregivers’ perceived risks and social norms

PurposeActive school travel (AST) programmes aim to change commuting behaviour to improve children's physical and mental health. However, very limited health education programmes for children use segmentation to create tailored solutions that understand the specific characteristics of each group of children and their caregivers in order to yield better results. The aim of this study is to use a statistical segmentation analysis (two-step cluster analysis) to gain insights on the examination of specific groups to design future health education interventions and campaigns that can improve children's health.Design/methodology/approachGuided by the Ecological and Cognitive Active Commuting (ECAC) framework, a market segmentation analysis was performed. An online survey was designed to collect data from caregivers of children between 5 and 12 years attending school and responsible for taking the child to and/or from school in Victoria and Queensland, Australia. Using 3,082 responses collected from Australian caregivers of primary school children, a two-step cluster analysis was performed.FindingsAnalysis revealed the most important variables for group formation were previous child walking behaviour, distance from school and caregiver income. Perceived risk of the physical environment was the most important psychographic segmentation variable for group formation, followed by social norms. Four distinct groups with different characteristics were identified from the analysis.Originality/valueThis is the first study that applies the ECAC framework to perform market segmentation in the AST context. Results revealed four market segments that demand different tailored solutions. Findings shed light on how to better design AST interventions and campaigns to promote children's health using segmentation techniques.

THE ROLE OF TRADE BARRIERS IN INTERNATIONAL TRADE IN METALLURGICAL INDUSTRY OF UKRAINE

The purpose of the paper is to analyze and present the role of the metallurgical industry for the economy of Ukraine and evaluate the impact of trade restrictions on the international activity of the metallurgical industry of Ukraine. As a result, this study evaluated the risks of international activities of metallurgical enterprises of Ukraine with key partner countries. Methodology. Assessment of the role of the metallurgical industry for the Ukrainian economy and the extent of the impact of trade barriers on international activities is based on several statistical data and a few methods and rules for their analysis. Their use will allow managers of metallurgical companies to form a strategy for the development of the enterprise in international markets and quickly respond to changes in the external market situation. These methods include statistical analysis, strategic segmentation, deductive and inductive methods, data comparison method, forecasting and risk assessment. Results. Per capita steel consumption growth rates have shown positive dynamics since 2010. Volatility levels of per capita steel consumption of the population is significantly lower than the growth of world GDP. Against the backdrop of slowing global GDP growth rate of steel consumption continues to grow. According to the level of consumption of steel per capita, the author singled out three stages in the development of countries. The share of exports of products of the metallurgical industry of Ukraine in 2020 amounted to 18.3% in monetary terms. This indicator is the second after the volume of exports of agricultural products. Among the total export of steel products in Ukraine 95% are engaged in export of ferrous metals, which is a raw product. The trade balance of Ukraine in the trade of metallurgical products has been decreasing since 2018 amid a decrease in export volumes and has not yet reached the level of 2016. According to the World Trade Organization, at the end of 2020, there were 3,208 non-tariff trade barriers in the world regarding trade in metals and metal products. In terms of the number of anti-dumping measures, the market of metals and metal products ranks first – 843 measures out of 2160 measures for all product groups. This tool is used by countries to protect the domestic market and as a tool in international competition. As a result of a matrix analysis of the rates and volumes of growth of Ukraine's metallurgical products in the context of partner countries and the number of introduced trade barriers, the countries with the highest risk in international activities and the most promising sales markets were identified. The Russian Federation and the United States are identified as the countries with the highest risk of the impact of trade barriers on international activities for Ukrainian metallurgical companies. The most promising sales markets are Saudi Arabia and Italy. Practical implications. With the help of the results of this paper, managers of metallurgical enterprises in Ukraine can form and adjust the strategic directions for the development of companies in the frame-work of international activities.