Image Formation Research Articles

Image Formation Algorithms for Low-Cost Freehand Ultrasound Scanner Based on Ego-Motion Estimation and Unsupervised Clustering

This paper describes the application of unsupervised learning techniques to improve ego-motion estimation for a low-cost freehand ultrasound probe. Echo decorrelation measurements, which are used to estimate the lateral velocity of a scanning probe as it is passed over the skin, are found to be sensitive to varying tissue types and echogenicity in the imaged scene, and this can impact the geometric accuracy of the generated images. Here, we investigate algorithms to cluster the collated 1D echo data into regions of different echogenicity by applying a Gaussian mixture model (GMM), spatial fuzzy c-means (SFCM) or k-means clustering techniques, after which the decorrelation measurements can focus on the regions that yield the most accurate velocity estimates. A specially designed mechanical rig is used to provide the ground truth for the quantitative analysis of probe position estimation on phantom and in vivo data using different clustering techniques. It is concluded that the GMM is the most effective in classifying regions of echo data, leading to the reconstruction of the most geometrically correct 2D B-mode ultrasound image.

Armed violence in the system of state image formation

The purpose of the article was to analyze the impact of armed violence on shaping the image of democracy, the image of foreign policy and the image of information policy of Ukraine. In the context of Russia’s aggression against Ukraine, a real fact is the transformation of Ukraine’s image in the perception of the world during the processes of European integration. Methodologically, dialectical, systematic, structural-functional, institutional, conflictological, political-cultural and axiological approaches were used in the study. It is concluded that the definition of state image presents many variants, since each scholar interprets this concept according to his or her subjective experience. However, the study of the image of a state in the context of international relations makes it possible to assume that the image of a state may arise under the influence of spontaneous (unconscious) factors, armed violence, geopolitical position and other characteristics; as well as a result of the selective influence of the mass media. A positive image of the state is formed through the proper functioning of three components: internal image, foreign policy image and information image.

Using domain adaptation for classification of healthy and disease conditions from mobile-captured images of standard 12-lead electrocardiograms

12-lead electrocardiogram (ECG) recordings can be collected in any clinic and the interpretation is performed by a clinician. Modern machine learning tools may make them automatable. However, a large fraction of 12-lead ECG data is still available in printed paper or image only and comes in various formats. To digitize the data, smartphone cameras can be used. Nevertheless, this approach may introduce various artifacts and occlusions into the obtained images. Here we overcome the challenges of automating 12-lead ECG analysis using mobile-captured images and a deep neural network that is trained using a domain adversarial approach. The net achieved an average 0.91 receiver operating characteristic curve on tested images captured by a mobile device. Assessment on image from unseen 12-lead ECG formats that the network was not trained on achieved high accuracy. We further show that the network accuracy can be improved by including a small number of unlabeled samples from unknown formats in the training data. Finally, our models also achieve high accuracy using signals as input rather than images. Using a domain adaptation approach, we successfully classified cardiac conditions on images acquired by a mobile device and showed the generalizability of the classification using various unseen image formats.

Специфика построения бренд-коммуникаций и бренд-имиджа горнолыжных курортов России и Австрии

Introduction. The article is devoted to identifying the features of the formation of brand communications and brand image of ski resorts in Russia and Austria.
 Aim. After analyzing current trends in the formation of brand communications and the brand image of ski resorts in Russia and Austria, to identify features and problems in their construction, development drivers. Methodology and methods. Theoretical and methodological basis of the study was chosen marketing, integrative and image-based approaches. The research methods were an analytical review of literature sources and the content of previous studies, participant observation, content analysis, feedback analysis, comparative analysis of geo-branding of Russian and Austrian ski resorts, synthesis, and generalization.
 Results. Based on a comparative analysis of the geo-branding of Russian and Austrian ski resorts, features, tools, problems and drivers for the development of brand communications were identified.
 Scientific novelty is to identify traditional and modern channels of brand communication and brand image, as well as digital and phygital tools for geo-branding of ski resorts in Russia and Austria.
 Practical significance. The results of the study can be useful for improving brand communications and brand image of ski resorts.

Представительство СССР на XVI Венецианской Биеннале 1928 г.: к истории советско-итальянских культурных связей

The article analyzes the participation of the Soviet delegation in the XVI International Art Exhibition in Venice in 1928. The successful presentation of Soviet art at the XIV Biennale in 1924 noticeably strengthened the USSR’s position in the development of cultural ties with Italy and contributed to the formation of an attractive image of the Soviet man among ordinary Italians. In 1926, the difficult economic situation did not allow the Soviet dele-gation to participate, but two years later, as part of the XVI Biennale in the Russian Pavilion in Venice, the public could once again see the work of Soviet masters. With the success of the 1928 Biennale, Soviet art’s position as a relay of the new state idea was consolidated and a massive response was generated. The official Italian press, despite many negative reviews, noted the timeliness and fullness of Soviet culture, and the system of state order made the strongest impression. The article is based on a large number of documents from the State Archive of the Russian Federation, the Russian Archive of Literature and Art, periodicals and memoirs of the events’ participants.

Deep Learning within a DICOM WSI Viewer for Histopathology

Microscopy scanners and artificial intelligence (AI) techniques have facilitated remarkable advancements in biomedicine. Incorporating these advancements into clinical practice is, however, hampered by the variety of digital file formats used, which poses a significant challenge for data processing. Open-source and commercial software solutions have attempted to address proprietary formats, but they fall short of providing comprehensive access to vital clinical information beyond image pixel data. The proliferation of competing proprietary formats makes the lack of interoperability even worse. DICOM stands out as a standard that transcends internal image formats via metadata-driven image exchange in this context. DICOM defines imaging workflow information objects for images, patients’ studies, reports, etc. DICOM promises standards-based pathology imaging, but its clinical use is limited. No FDA-approved digital pathology system natively generates DICOM, and only one high-performance whole slide images (WSI) device has been approved for diagnostic use in Asia and Europe. In a recent series of Digital Pathology Connectathons, the interoperability of our solution was demonstrated by integrating DICOM digital pathology imaging, i.e., WSI, into PACs and enabling their visualisation. However, no system that incorporates state-of-the-art AI methods and directly applies them to DICOM images has been presented. In this paper, we present the first web viewer system that employs WSI DICOM images and AI models. This approach aims to bridge the gap by integrating AI methods with DICOM images in a seamless manner, marking a significant step towards more effective CAD WSI processing tasks. Within this innovative framework, convolutional neural networks, including well-known architectures such as AlexNet and VGG, have been successfully integrated and evaluated.

CATACHRESIS IN THE IMAGERY SPACE OF ENGLISH POETIC DRAMA: A COGNITIVE PERSPECTIVE

CATACHRESIS IN THE IMAGERY SPACE OF ENGLISH POETIC DRAMA: A COGNITIVE PERSPECTIVE

High-Definition Image Formation Using Multi-stage Cycle Generative Adversarial Network with Applications in Image Forensic

High-Definition Image Formation Using Multi-stage Cycle Generative Adversarial Network with Applications in Image Forensic

Learning Moore-Penrose based residuals for robust non-blind image deconvolution

This paper proposes a deep learning-based method for image restoration given an inaccurate knowledge of the degradation. We first show how the impulse response of a Wiener filter can approximate the Moore-Penrose pseudo-inverse of the blur convolution operator. The deconvolution problem is then cast as the learning of a residual in the null space of the blur kernel, which, when added to the Wiener restoration, will satisfy the image formation model. This approach is expected to make the network capable of dealing with different blurs since only residuals associated with the Wiener filter have to be learned. Artifacts caused by inaccuracies in the blur estimation and other image formation model inconsistencies are removed by a Dynamic Filter Network. The extensive experiments carried out on several synthetic and real image datasets assert the proposed method's performance and robustness and demonstrate the advantage of the proposed method over existing ones.

THE SPECIFICITY OF THE IMAGE OF THE CITY IN THE LINGUISTIC CONSCIOUSNESS OF ITS RESIDENTS AS A BASIS FOR SOLIDING SUSTAINABLEREGIONAL COMPONENTS

The article presents an analysis of the associative responses of residents of Chelyabinsk and guests of the city, associated with the perception of the urban environment, urban objects, well-known places, famous personalities, the image of the city and its residents in general. It is important to find out the associative core and peripheral meanings of the city image because it can be used to solve the problems of socio-cultural adaptation when introduced into the school educational system. Acquaintance with famous places and people, knowledge of historical facts and unique features of the city and region can have a positive impact on the formation of the image of a Russian citizen in the linguistic consciousness of the younger generation. The material presented in this study shows a complex, contrasting attitude towards the city, primarily due to the fact that current generations do not perceive Chelyabinsk as a city of labour merit, as one of the key elements of the stronghold of the country, but note serious environmental problems and an unfavorable external environment for human life (two sides of the same coin). This study can form the basis for identifying specific unique regional lexical components associated with Chelyabinsk and the Chelyabinsk Region, which can contribute to the formation of a positive image of this location.

Creative metaphors and non-propositional effects

Abstract Over the last decade there has been growing relevance-theoretic interest in the interpretation of creative metaphors. Much of this interest has focused on non-propositional aspects of interpretation: mental image effects/emotive effects. Central to this enquiry is the following question: are non-propositional effects essential to the metaphorical interpretation process? The implications of answering this question are important, since, if the answer is positive, then the delivery of metaphorical interpretation depends, not only on utterance processing, but also on the hearer’s formation of mental images as well as emotive experience. Relevance-theoretic studies argue that mental images do not fulfill an essential role in the metaphorical interpretation process. While the supporting evidence is solid, it requires experimental substantiation. The current paper responds to this requirement, taking on board emotive effects, too, apart from mental images. Ultimately, the current work concludes that the role of non-propositional effects in metaphorical interpretation is not essential.

Measuring the Image of the Russia’s Future Among Russian Youth: Cognitive Science and Cybermetrics for the Political Study

The article presents methodological problems of studying the image of the future of the country among Youth. The unsuitability of the mono-design of empirical analysis, which is limited to the traditional survey method for measuring the image of the future of the state among younger generation, is substantiated. The heuristics of instrumental and projective cognitive science and its possibilities in compensating for the shortcomings of traditional sociology are analyzed. The principles of verification of cognitive science data by methods of automated socio-media analysis of representation in the digital space of the image of the future of Russia are considered. The potential of combining cognitive and psychosemantic methods: eye tracking, polygraphology, cardiometry (instrumental cognitivistics) and associative and sand modeling (projective cognitivistics) in the identification of integration images reflecting in the minds of Russian youth symbols of the positive future of Russia is investigated. The results of the experiment are analyzed, which made it possible to establish visual images associated by Russian youth with the archetypes of “Consent”, “Trust” and “Pride” as the basis for a positive perception by young people of the image of the future of the Russian. Federation. The heuristics of combining the traditional survey with the methods of instrumental and projective cognitive science in clustering young people according to the criteria of activity, awareness of responsibility for their fate and the future of their country, self-generation of goals, demonstration of readiness to achieve them, willingness to negotiate with partners, opponents, self-organization and skills to involve other people in the implementation of their plans are shown. The possibilities of validating the data of projective cognitive science by means of automated social media analysis are considered. The possibilities of scaling the semantic core of the rhetoric of young people — representatives of various strategies for perceiving the image of the future of Russia on the information flows of social media on the Runet are revealed. In conclusion, the general conclusions on the implementation of the methodology and methodology of projective and instrumental cognitive science in the study of the image of the future of the country in the minds of Russian youth are presented. Monitoring the representation of the future of the Russian Federation in the minds of the younger generation can become a basis for practical recommendations to authorities, relevant ministries and state structures on the formation of a positive image of the future of the Russian Federation among young Russians.

The Teacher Image Interpretation in Student Teachers: A Linguistic Anthropology Approach

The study aims to determine whether the images of a teacher in the normative document and the minds of student teachers coincide, as well as to identify the differences. A total of 236 students of the Faculty of Philology and History participated in the associative experiment. The words “ability”, “readiness”, and “possession” are the most common words used in the normative documents of the Russian Federation in the context of higher education. The concept of “teacher” is the main stimulus word in the associative experiment. The results of the associative experiment revealed a group of the most frequent associative words. In addition, based on the quantitative indicators of the associative experiment, nine thematic groups of associative words were formed. A comparative analysis of the indicators of an associative experiment involving students from different countries can make it possible to compare the features of national education systems and show their effect on the formation of a teacher image in the minds of student teachers.

CITRA TUBUH DAN FAKTOR PEMBENTUK CITRA TUBUH PEREMPUAN EMERGING ADULT YANG MENGALAMI KENAIKAN BERAT BADAN SIGNIFIKAN KETIKA REMAJA

Women often receive comments about their bodies as they gain weight, which increases the risk of developing a negative body image and damage the identity formation process during adolescence. These obstacles will impact the emerging adulthood developmental stages. This study aims to describe body image and body image formation factors in emerging adult women who experienced significant weight gain from being underweight during adolescence. Qualitative research was conducted, involving three adult female participants aged 18-25 years, who were classified as underweight during adolescence, experienced significant weight gain, had either underweight or normal Body Mass Index (BMI), and had a negative body image based on the Multidimensional Body-Self Relational Questionnaire-Appearance Scales (MBSRQ-AS). The three participants were selected through purposive criterion sampling. Data was collected through semi structured interviews, which were then analyzed using thematic analysis. The results showed that all three participants were dissatisfied with specific body parts due to the desire to achieve their ideal body weight. This dissatisfaction affected theparticipants’ efforts to present themselves and control their weight. These views were formed from individual experiences during adolescence, which persisted into emerging adulthood and were greatly influenced by interpersonal factors and cultural socialization factors.

АРХИТЕКТУРНЫЕ И ХУДОЖЕСТВЕННЫЕ ПРИЕМЫ ФОРМИРОВАНИЯ ИННОВАЦИОННОГО ТЕАТРАЛЬНОГО ПРОСТРАНСТВА С ПРИМЕНЕНИЕМ ЦИФРОВЫХ ТЕХНОЛОГИЙ

In this study, the authors pay attention to digitalization in theatrical art, and also raise the question of what changes theater spaces undergo under the influence of high technologies. The features of the influence of cultural globalization on modern theater are considered. Historical and modern examples of experience in designing theatrical spaces of new digital aesthetics prove the relevance and active development of this topic at present. The study of world and domestic experience in organizing theater spaces made it possible to identify the main trends in the development of the theater of a new order. The study defines the types of technologies that are used to transform the theater space, such as: VR technology, AR technology, projection 3D video, etc. The study defines the main techniques for the formation of new visual images of the theater's media architecture, considers interior and exterior aspects, determines the correspondence of types of digital technologies and theater spaces. The revealed techniques and principles of shaping are tested at the experimental project of the cultural center with a multi-purpose hall in Samara.

The Role of Translation in Advertising Discourse (With Examples From English and Russian)

This article explores the role of translation in advertising discourse, a field that plays a crucial role in shaping and promoting brands, products, and services. Using English and Russian as examples, the author analyzes how translation influences the effectiveness of advertising campaigns and brand image formation. The article sheds light on the challenges faced by translators in adapting advertising texts, such as wordplay and cultural nuances. It also discusses the importance of selecting the right words and expressive means to convey the emotional power of the original text. The author highlights the relevance of this topic in the context of globalization and business internationalization, as well as the practical significance of the article for translators, marketers, and advertisers. Overall, the article provides useful recommendations and examples that help understand the role of translation in advertising discourse and enhance the quality of translations for successful advertising communication.

Factual to Virtual Visualization

The purpose of this study is to explore the formation of image output in computer system from the user input information. The study seeks to answer the research question How does the image displayed on the computer screen and how appearance of image changed? The goal is to analyze transformation of real world picture to computerized output image in a very simple method by explaining all major components involved in this process in detail with diagrams. Further by using some mathematical concepts algorithms are derived and implemented to transform the images to change their shapes, sizes etc As there is a very near context between computer science and mathematics. Systems works completely based on the algorithms written to perform the particular task, algorithms are designed based on the mathematical concepts and are implemented by the programming languages in computer. Paper concentrates on the graphic capabilities and potentials of the digital computer. It presents an abstract idea of system that, how computer works internally to create and represent the simple graphical elements and images which human can see and understand. Paper presents all the components and related concepts with suitable examples from input information to graphical output produced by computer. After by getting the concept of image formation the next step is to know that how transformation of image takes place by changing original image. This paper illustrates four types of transformations with example translation, rotation, scaling, and shearing. Paper explores about display devices, different types of display methods, programming tools and finally drawing of a simple line in computer screen using a simple line drawing algorithm. Further it elaborates the concept of pixels, resolution, aspect ratio, persistency, different types of transformations etc.,

A novel comparison of image semantic segmentation techniques for detecting dust in photovoltaic panels using machine learning and deep learning

The reduction in photovoltaic (PV) panel efficiency is a significant concern, especially for the photovoltaic power stations (PPS) near different soil types and a high wind presence. A relevant interest has emerged in developing systems capable of recognizing and evaluating the state of PV panels without human intervention. This work analyzes three different approaches to address this problem using semantic segmentation. The first approach employs unsupervised learning, while the second utilizes supervised learning, applying Machine Learning techniques such as K-means, Gaussian Mixture Models, Random Forest, and Light GBM, as well as more rudimentary options like histogram segmentation and color spaces. The final approach utilizes Deep Learning models, testing different versions of the U-net architecture primarily designed for image segmentation tasks. The results showcase the model's performance in terms of accuracy, processing and training time, F1 Score, and Intersection over Union. It was observed that supervised models with Machine Learning algorithms achieved a perfect balance between performance and speed. On the other hand, the Deep Learning approach proved more effective when the input was not standardized and the image format was poorly defined.

HemiPy: A Python module for automated estimation of forest biophysical variables and uncertainties from digital hemispherical photographs

Abstract Digital hemispherical photography (DHP) is widely used to derive forest biophysical variables including leaf, plant, and green area index (LAI, PAI and GAI), the fraction of intercepted photosynthetically active radiation (FIPAR), and the fraction of vegetation cover (FCOVER). However, the majority of software packages for processing DHP data are based on a graphical user interface, making programmatic analysis difficult. Meanwhile, few natively support analysis of RAW image formats, while none incorporate the propagation or provision of uncertainties. To address these limitations, we present HemiPy, an open‐source Python module for deriving forest biophysical variables and uncertainties from DHP images in an automated manner. We assess HemiPy using simulated hemispherical images, in addition to multiannual time‐series and litterfall data from several forested National Ecological Observatory Network (NEON) sites, as well as comparison against the CAN‐EYE software package. Multiannual time‐series of PAI, FIPAR and FCOVER demonstrate HemiPy's outputs realistically represent expected temporal patterns. Comparison against litterfall data reveals reasonable accuracies are achievable, with RMSE values close to the error of ~1 unit typically attributed to optical LAI measurement approaches. HemiPy's PAI, FIPAR and FCOVER outputs demonstrate good agreement with CAN‐EYE. Consistent with previous studies, when compared to simulated hemispherical images, better agreement is observed for PAI derived using gap fraction near the hinge angle of 57.5° only, as opposed to values derived using gap fraction over a wider range of zenith angles. HemiPy should prove a useful tool for processing DHP images, and its open‐source nature means that it can be adopted, extended and further refined by the user community.

A Novel Approach to Quantitative Characterization and Visualization of Color Fading

Color fading naturally occurs with time under light illumination. It is triggered by the high photon energy of light. The rate of color fading and darkening depends on the substance, lighting condition, and storage conditions. Color fading is only observed after some time has passed. The current color of objects of interest can only be compared with old photographs or the observer’s perception at the time of reference. Color fading and color darkening rates between two or more points in time in the past can only be determined using photographic images from the past. For objective characterization of color difference between two or more different times, quantification of color in either digital or printed photographs is required. A newly developed image analysis and comparison software (PicMan) has been used for color quantification and pixel-by-pixel color difference mapping in this study. Images of two copies of Japanese wood-block prints with and without color fading have been selected for the exemplary study of quantitative characterization of color fading and color darkening. The fading occurred during a long period of exposure to light. Pixel-by-pixel, line-by-line, and area-by-area comparisons of color fading and darkening between two images were very effective in quantifying color change and visualization of the phenomena. RGB, HSV, CIE L*a*b* values between images and their differences of a single pixel to areas of interest in any shape can be quantified. Color fading and darkening analysis results were presented in numerical, graphical, and image formats for completeness. All formats have their own advantages and disadvantages over the other formats in terms of data size, complexity, readability, and communication among parties of interest. This paper demonstrates various display options for color analysis, a summary of color fading, or color difference among images of interest for practical artistic, cultural heritage conservation, and museum applications. Color simulation for various moments in time was proposed and demonstrated by interpolation or extrapolation of color change between images, with and without color fading, using PicMan. The degree of color fading and color darkening over the various moments in time (past and future) can be simulated and visualized for decision-making in public display, storage, and restoration planning.