Image Fragments Research Articles

Badminton players’ trajectory under numerical calculation method

Abstract The trajectory tracking of badminton players’ arm shots can be used effectively to enhance the player’s shot quality. To track the image trajectory of the batting arm, it is necessary to calculate the body posture ratio and tightness of the target area of the batting arm, to filter the background interference of the image segment of the batting and to complete the efficient tracking of the trajectory of the arm batting image. The traditional method combines the adaptive threshold segmentation method to extract the hitting arm target from the background, but ignores filtering out the background interference of the hitting image fragment. This paper proposes a trajectory tracking method based on the morphological operator of the batter image of the arm stroke. The method consists of (1) differentially calculating the image sequence of the hitting arm during two consecutive shots, (2) estimating the Gaussian model parameters of the differential image of the hitting arm during the hitting process, (3) extracting the outline of the moving target of the hitting arm during the hitting process and then calculating the body posture ratio of the hitting target area and compactness, (4) filtering the background interference of the shot image fragment, (6) constructing a global matching approximation function of the moving target and (7) finally determining the motion trajectory of the badminton arm of the batter. Simulation results show that the proposed method can effectively track the target of the hitting arm during the hitting process and generate a continuous trajectory of the hitting arm.

Badminton players’ trajectory under numerical calculation method

Abstract The trajectory tracking of badminton players’ arm shots can be used effectively to enhance the player’s shot quality. To track the image trajectory of the batting arm, it is necessary to calculate the body posture ratio and tightness of the target area of the batting arm, to filter the background interference of the image segment of the batting and to complete the efficient tracking of the trajectory of the arm batting image. The traditional method combines the adaptive threshold segmentation method to extract the hitting arm target from the background, but ignores filtering out the background interference of the hitting image fragment. This paper proposes a trajectory tracking method based on the morphological operator of the batter image of the arm stroke. The method consists of (1) differentially calculating the image sequence of the hitting arm during two consecutive shots, (2) estimating the Gaussian model parameters of the differential image of the hitting arm during the hitting process, (3) extracting the outline of the moving target of the hitting arm during the hitting process and then calculating the body posture ratio of the hitting target area and compactness, (4) filtering the background interference of the shot image fragment, (6) constructing a global matching approximation function of the moving target and (7) finally determining the motion trajectory of the badminton arm of the batter. Simulation results show that the proposed method can effectively track the target of the hitting arm during the hitting process and generate a continuous trajectory of the hitting arm.

Methodology for eliminating imbalance of image data sets

The methodology of solving the problem of eliminating class imbalance in image data sets is presented. The proposed methodology includes the stages of image fragment extraction, fragment augmentation, feature extraction, duplication of minority objects, and is based on reinforcement learning technology. The degree of imbalance indicator was used as a measure to determine the imbalance of the data set. An experiment was performed using a set of images of the faces of patients with skin rashes, annotated according to the severity of acne. The main steps of the methodology implementation are considered. The results of the classification showed the feasibility of applying the proposed methodology. The accuracy of classification on test data was 85%, which is 5% higher than the result obtained without the use of the proposed methodology. Key words: class imbalance, unbalanced data set, image fragment extraction, augmentation.

Symbolism of the image of love in the poem “La steaua” by M. Eminescu as an object of linguo-cognitive research

This research is devoted to the study of the linguo-cognitive specifics of the linguistic nominations of manifestations of love in the poem “La steaua” by M. Eminescu. The study of linguistic representations of the concept of love as multifaceted and ambiguous from the point of view of its emotional nature of feelings on the material of a poetic work is interdisciplinary by its nature, being at the junction of such scientific disciplines as: cognitive linguistics, literature and psycholinguistics. The purpose of the study is to identify and study the verbalized fragments of the image “love” in the poem “La steaua” by M. Eminescu and to establish their associative-figurative potential. To achieve the above-mentioned goal, a number of tasks were outlined: to identify and classify the key conceptual dominants of the poem “La steaua” by M. Eminescu; to establish the structure and semantic content of the image of love in the poem by describing its frame structure; to determine the individual author’s specificity of the interpretation of the artistic image of love on the material of the analyzed poem. As a result of our research, we identify frame structures that are activated by verbal units and convey a specific stereotypical situation. We come to the conclusion that the features of the verbal embodiment of frame structures are directly related to the individual author’s perception of the image of love, his personal and emotional characteristics, as well as culturally determined associations with the concept of love.

Analysis of remote sensing images by methods of convolutional neural networks and marked random point fields

The methodology of remote sensing image analysis for detection of dependences in the process of development of biological species is proposed. Classification methods based on convolutional networks are applied to a set of fragments of the input image. In order to increase the accuracy of classification by increasing the training and test samples, an original method of data augmentation is proposed. For a series of images of one part of the landscape, the fragments of images are classified by their numbers, which coincide with the numbers of the previously classified image of the training and test samples which are created manually. This approach has improved the accuracy of classification compared to known methods of data augmentation. Numerous studies of various convolutional neural networks have shown the similarity of the classification results of the remote sensing images fragments with increasing learning time with the complication of the network structure. A set of image fragment centers of a particular class is considered as random point configuration, the class labels are used as a mark for every point. Marked point field is considered as consisting of several sub-point fields in each of which all points have the same qualitative marks. We perform the analysis of the bivariate point pattern to reveal relationships between points of different types, using the characteristics of marked random point fields. Such relationships can characterize dependences and relative degrees of dominance. A series of remote sensing images are studied to identify the relationships between point configurations that describe different classes to monitor their development.

ENCODING RASTER IMAGES BASED ON FRAGMENT SIMILARITY

This paper is devoted to image encoding based on determining the similarity of fragments by using neural networks to extract the features of fragments and machine learning algorithms to find similar fragments. In the modern world, the problem of image storage is quite relevant. Graphic data takes up quite a lot of disk space, while Internet users upload more and more pictures. Also, every year there is a development of photography and image quality is improving, respectively, and the size of graphic data is growing. Data warehouses of social networks, messengers, file sharers and other Internet resources are filled with tens of thousands of new pictures every day. Therefore, the question arises about reducing the size of graphic data. In general, it should be noted that one of the most important and defining aspects of both storage and transmission of information is its compression. The problem described above is solved by encoding and compressing images. With the help of coding, the size of graphic information is reduced, which saves storage space and, accordingly, the money spent. In view of this, it is important to develop a method and means of image coding. Many methods exist for compressing graphic information. For example, jpeg, webp, png and others. These methods usually use the removal of redundant information in the photo and work purely with the image itself, but none of the methods uses fragments of similar images. The article uses convolutional neural networks and KNN (k-Nearest Neighbors) classifier for image encoding. and compares the size of the encoded image with the input. In order to encode the image, you first need to fill the data warehouse with features of fragments of similar images, then for each fragment of the obtained images you need to select the features and write to the data warehouse. Once the snippet feature database is formed, you can encode new images using saved snippets.

Комбинированное использование изображений Landsat 8 и Sentinel 2А для улучшения пространственного разрешения температуры поверхности Земли

Underlying surface temperature is an important parameter of underlying surface thermal radiation and can be used in monitoring forest fires, coal fires, urban thermal radiation and developing climate models. Ground-based observations provide temperature information for small areas around weather stations and in fact cannot provide a high density of surface temperature data. Remote sensing technologies are promising in this respect. However, due to the low spatial resolution in the infrared channel, the surface temperature calculated from Landsat and Aster images does not always have the required detail needed when studying small areas. The results of images from Sentinel 2A and Landsat 8 satellites combination (joint digital processing) made in order to increase spatial resolution of underlying surface temperature are presented. Comparison of surface temperature extreme values shows that in spite of small difference in extreme values of temperature, the spatial field of temperature in case of combined images was more detailed and variable. This is evidenced by a significant increase in the variability of the temperature standard deviation. Direct visual observations of image fragments also confirm that combining Sentinel 2A and Landsat 8 images increases the spatial resolution of the surface temperature when compared to the Landsat 8 image

ОЦЕНКА ВЛИЯНИЯ РАЗОРВАННОЙ ОБЛАЧНОСТИ НА ВОССТАНОВЛЕНИЕ КОЭФФИЦИЕНТОВ ОТРАЖЕНИЯ БЕЗОБЛАЧНЫХ УЧАСТКОВ ЗЕМНОЙ ПОВЕРХНОСТИ ПО СНИМКАМ ПРИБОРА MODIS

An algorithm for estimating the range of influence of broken cloudiness on the results of retrieving reflectance of cloudless Earth surface regions is proposed for satellite observations through gaps in a cloud field. The algorithm is based on the Monte Carlo method of simulation of radiation transfer in broken stochastic cloudiness with averaging over the cloud field. Two models of cloud fields are considered: the field shapes as parallelepipeds and paraboloids. The method is tested for two fragments of the real MODIS image with the coordinates of 53.4°-56.4° N, 109°-115° E and 49.0°-51.0° N, 121°-123° E. The results of computations show that the effect of cloudiness is significant at distances up to 25 km for the first image fragment and up to 5-7 km for the second one.

Correlation Properties of Sea Surface Images on Video Stream Frames

The work is devoted to the characteristic properties analysis of the sea surface image on video stream frames, which make it possible to make a decision about the detection of object images on them in the absence of a priori information about objects, which is important in the development of navigation systems, security and surveillance systems. In this paper, it is proposed to analyze the values of the normalized cross-correlation coefficients of a given image fragment with higher dimension fragments on sequential video stream frames to identify the distinctive properties of agitated sea surface and floating objects images. The computational experiments were carried out and have shown that the analysis of the results of calculating the frame fragments cross-correlations allows us to estimate the amount of displacement and distortion of a given image fragment. The results of computational experiments demonstrate the presence of differences in the values of the corresponding cross-correlation coefficients for the sea surface images with different agitation degrees, containing and not containing an image of the object. Based on the analysis of the proposed correlation properties of the sea surface images, a decisive rule for selecting fragments of frames containing an image of an object is formulated, the use of which, in many cases, allows to detect fragments of object images correctly.

COMPLEX OF MATHEMATICAL MODELS AND METHODS TO CALCULATE PRESSURE EFFECT ON SULFIDE DISTRIBUTION IN STEEL

Primary objective is to develop computational method to analyze digital pictures of sulfide prints, helping obtain qualitative image characteristics, and to formulate mathematical model of the distribution of sulphide inclusions to determine specific features of the pressure effect on the macrostructure formation of carbon steel castings flooded into the uncooled mold.
 The research was carried out using images of sulfide prints of templates cut of steel cylindrical castings; L500 steel was applied. The castings result from industrial tests of a method of gas-dynamic effect on the fusion in the foundry forms under the conditions of a casthouse of Dnipropetrovsk aggregate plant PJSC. Digital pictures of sulfide prints, obtained in terms of the increased rate of gas pressure and maximum pressure, were binarized; defective fra gments were removed; and zoning took place. The developed computational method has been applied for fragments of images, representing different zones; data arrays have been received containing sizes and amounts of inclusions in the fragment.
 The developed computational method to analyze digital images of sulfide prints has been implemented. ASImprints software support has helped obtain qualitative characteristics of images; namely, distribution of amount of the certain-size sulfide inclusions. 
 The computational method to analyze digital images of sulfide prints has made it possible to study the set of patterns of sulfide prints. The dependences have been obtained, describing specific features of sulfide inclusion distribution while varying gas-dynamic pressure method in terms of fusion in the casting form. It has been demonstrated that the distribution describes effectively the power-series distribution to compare with the exponential one. Mathematical model of the power-series distribution parameter dependence upon pressure has been developed. Deviation of the distribution parameters in terms of the experimental values and the model values has been evaluated. 
 The research demonstrates the ways to apply an algorithm of simple recursive casting for quantitative analysis of digital images of sulfide prints. Use of ASImprints, being software implementation of the computational method to analyze digital images of sulfide prints making it possible to obtain qualitative characteristics of images, has helped identify that the increased pressure within a casting-device for gas injection system results in the increased specific amount of inclusions and the decreased specific zone of sulfide inclusions respectively. It has been defined that exponential function describes reliably the nature of sulfide inclusion distribution in the digital image of sulfide print. The research has demonstrated that fragments of a sulfide print, belonging to one zone, are statistically homogeneous. Thus, it is possible to analyze quantitively digital image zone of a sulfide print on its fragment. Mathematical model of dependence of sulfide inclusion distribution in carbon-steel castings in terms of gas-dynamic effect on fusion solidifying in a mold has been developed. The model may be applied to predict sulfide inclusion distribution within the selected zones of cross section of the cylindrical castings solidifying in the uncooled mold in terms of the preset mode of gas-dynamic effect.
 Keywords: gas-dynamic effect, pressure, solidification, casting, steel, 35 Л, mold, macrostructure, sulfides, inclusions, template, sulfide print, distribution, polynomial, parameters, prediction, software implementation, ASImprints

TECHNOLOGY FOR THE DEVELOPMENT OF SOFTWARE ALGORITHMS FOR OPTOELECTRONIC MONITORING SYSTEMS FOR REMOTE OBJECTS

The issues of constructing algorithms for obtaining coordinate and non-coordinate information used to solve the problem of multiplexing images obtained from several optoelectronic systems are considered. A unique mathematical method for finding the corresponding points in images, based on algorithms for continuous wavelet transform of the brightness structure of an image, is proposed. The technology of development of algorithms intended for multi-position optoelectronic systems for monitoring remote objects based on software from National Instruments is considered. A technique for constructing software for obtaining information is proposed, which ensures high accuracy in determining the coordinates of the corresponding fragments in images. It is shown that the parallel use of several methods makes it possible to assess the reliability of the information obtained under conditions of changing observation parameters. The use of several methods makes it possible to assess the reliability of the information obtained under conditions of changing observation parameters. Computational experiments have confirmed that a more accurate search for image alignment regions is provided by the double wavelet transform method by increasing the number of extrema of the curves of the continuous wavelet transform coefficients, expanding the area from localization and additional filtering. An example of the practical implementation of the developed algorithms in a two-channel optoelectronic system is presented.

Finds of ornitomorphic images of the End of the Early Iron Age in the Lower Angara region

The paper concerns the ornithomorphic images found at the Pinchuga VI burial ground. The site is located in the lower course of the river Angara (Middle Siberia). The chronological boundaries of the study span the second quarter of the 1st millennium AD (end of the Early Iron Age). All burials at the burial ground were performed ac-cording to the rite of cremation outside the cemetery. Two intact objects and fragments of the third image of a bird were found at the necropolis. Figures were found in different contexts. One of them was found in the filling of a grave pit, the second item within an assemblage of various articles in the inter-grave space. The third item was broken and lost as the result of illegal excavations. All articles share similar characteristics. These are realistic images of diurnal birds of prey “frozen” in a diving flight; the images are shown en face, with a high-relief head, with the tucked wings and feet pulled up with talons. A geometric decor conveys their feather, and a stylized mask is present on the chest of one item. The images are slightly convex, their front side is polished. The closest analogies to the Angara images of birds are known in Western Siberia, including the Tomsk burial ground, the Kholmogory treasure, the Ishim collection, and materials from the Parabel cult place. All this makes it possible to attribute the analyzed items to the Kholmogory stylistic group of the Kulai cult casting. Products of this group be-came widespread in Western Siberia in the second quarter of the 1st millennium AD. The ornithomorphic images found at the Pinchuga VI cemetery extend the geographical range of the items of this style to the territory of Mid-dle Siberia. Apart from the figurines of birds, the necropolis also yields other items of the Western Siberian cult casting, including disks with concentric ornaments, a hollow image of a fish head, and a bird-head belt applique. Bronze items were imported, and in the course of exchange they were spreading over considerable distances. This proves the existence of established cultural ties between the populations of the Lower Angara region and Western Siberia at the End of the Early Iron Age.

Fine-Grained Cross-Modal Retrieval for Cultural Items with Focal Attention and Hierarchical Encodings

In this paper, we target the tasks of fine-grained image–text alignment and cross-modal retrieval in the cultural heritage domain as follows: (1) given an image fragment of an artwork, we retrieve the noun phrases that describe it; (2) given a noun phrase artifact attribute, we retrieve the corresponding image fragment it specifies. To this end, we propose a weakly supervised alignment model where the correspondence between the input training visual and textual fragments is not known but their corresponding units that refer to the same artwork are treated as a positive pair. The model exploits the latent alignment between fragments across modalities using attention mechanisms by first projecting them into a shared common semantic space; the model is then trained by increasing the image–text similarity of the positive pair in the common space. During this process, we encode the inputs of our model with hierarchical encodings and remove irrelevant fragments with different indicator functions. We also study techniques to augment the limited training data with synthetic relevant textual fragments and transformed image fragments. The model is later fine-tuned by a limited set of small-scale image–text fragment pairs. We rank the test image fragments and noun phrases by their intermodal similarity in the learned common space. Extensive experiments demonstrate that our proposed models outperform two state-of-the-art methods adapted to fine-grained cross-modal retrieval of cultural items for two benchmark datasets.

An experimental investigation of shear strength behavior of a welded bimrock by meso-scale direct shear tests

Bimrocks have been rarely studied due to their inhomogeneous properties, challenges related to specimen preparation and tedious in-situ tests. This study was undertaken in an attempt to determine the shear strength parameters as well as the deformation behavior of a highly welded bimrock using laboratory meso-scale direct shear tests. In addition digital image processing analyses (DIP) and fragmentation fractal dimension (DF) calculations were made to quantify the geometrical and structural properties of the bimrock. It was concluded that the block size distribution of Sille bimrock is scale-invariant and shows fractal fragmentation. Moreover the block sizes have power-law distributions and there is evident skewness in normal distribution. The test results demonstrated that with the increase of volumetric block proportions (VBP), the internal friction angle (ϕ) of the bimrock increases whereas its cohesion (c) decreases. Strain softening behavior was observed after attaining the peak shear stress for all normal stresses and the VBP values. It is revealed that the normal stress is more effective than the VBP on the development of dilatation/contraction behavior. Lastly, the negative correlation between the DF and the peak shear stress indicated that the shear strength of the bimrock increases while the proportion of relatively small blocks in the specimen decreases.

Driver Drowsiness Detection Using Convolutional Neural Networks

Accidents are now increasingly increasing as more cases are caused by driver drowsiness. To reduce these situations we were working on something that could reduce numbers and get accidents early. Seeing a drowsy driver behind the steering wheel once and warning him could reduce road accidents. In this case drowsiness is detected using an automatic camera, where, based on the captured image, the neural network detects whether the driver is awake or tired. Convolutional Neural Network Technology (CNN) has been used as part of a neural network, where each framework is examined separately and the average of the last 20 frames are tested, corresponding for about one second to a set of training and test data. We analyse image segmentation methods, construct a model based on convolutional neural networks. Using a detailed database of more than 2000 image fragments we are training and analysing the segmentation network to extract the emotional state of the driver in images.

Stop moving! Pain, body and trauma in pictures and narratives of patients, suffering with a somatoform disorder

AbstractSomatoform pain disorders are strongly associated with adverse, mostly traumatic childhood experiences. Little is known, however, about the psychodynamic background, for example, with regard to defence, affect regulation, and traumatic representations. As part of a qualitative study in which 13 patients with somatoform disorder were investigated, we compared the interviews and drawings by the two patients which featured the most pictorial codes for deaffectualization as a defence of the autistoid type of early disorders with the two patients that displayed the fewest codes for deaffectualization. These patients suffered from a more mature, non‐autistoid disorder. The drawings were analyzed according to the Visual Grounded Theory, the interviews according to Content Analysis. We used the qualitative atlas.ti‐software. In the two patients with a high evidence of pictorial deaffectualization, a correspondence was found between the deaffectualization and the type of pain sensation: Physical movements led to an increase in pain. The affective as well as the physical rigidity may have been intended to avoid the body image's fragmentation. The drawings by the more mature patients showed strong, partly violent interactions. In the narratives, the patients tended more to report feelings of agitation; generally, more physical movements could be allowed. In all four patients, a considerable correspondence was identified between the drawings and the narratives about physical pain. The dimension of the imaginary was determined both physically (on the level of the body image) and pictorially (on the level of the mental image) through the regulation of traumatic affects.

Survey on Artificial Intelligence based Image Processing and Image Segmentation analysis

This paper critically surveys the aspect of digitalized image processing and segmentation with central focus on artificial intelligence. A digitalized image is composed of numerous elements that must be "analyzed," since better “phrasing”, and the "research" done on such elements might disclose a lot of strange data. This data may assist in solving a variety of business challenges, which is one of the numerous end objectives associated with image processing. Digital image analysis of image processing is a collection of methodologies applied to process computerized images. It integrates accomplishing basis assignments such noise minimization and more complex assignments such as image classification, fault diagnostics, emotional response, image fragmentation and image segmentation. Image enhancement applying neural networks has, over the past few decades, been applied widely in the clinical setting due to the advent of advanced computing ecosystems and algorithms. Medicine, industries, police departments, agriculture, defense, finance and security are some of the additional domains where the concept of image processing and segmentation can be applied.

1인 미디어 영상 [이자칠](李子柒) 연구

The success of Liziqi, a food content based on a personal media platform, highlights the importance of a “slow life” that seeks emotional stability and self-healing. Liziqi provides a new means of establishing a relationship between humans and nature for stressed-out modern people who are weary of their fast pace of life. In addition, Liziqi is presented as fragments of various images in a personal media platform and can be interpreted as a schizophrenic image. This method constantly overthrows the existing meaning system of life and overturns established categories, which are barriers to humans and the world. This also allows us to find new meaning in our lives as they exist. Liziqi, who came from a rural village in Sachuan Province of China, shows aesthetic ambivalence with regard to enjoying the leisurely state of the ancient agricultural society in China, allowing the viewer to experience it. In fact, the format of these contents is similar to that of “Slow TV” which started in Norway in 2009, and the content has the aspect of recreating the state of rural life in ancient Chinese culture. Liziqi is a reinterpretation of the aesthetics of the past as fresh, not retro. Through this method, it can be assessed that it has captured the emotions of personal media audiences, centered on the younger generation. For modern people who have feelings of anxiety and fatigue in a rapidly changing era, Liziqi provides psychological well-being to human subjects in nature considering the culture of the past. Accordingly, this study attempts to interpret the success factors of Liziqi through the ‘property of a personal media platform’, the concept of ‘Slow TV’, and the ‘viewpoint of Eun-il culture’.

기억의 시각화를 위한 인터랙티브 콜라주 시스템 디자인 - 본인 작품을 중심으로

This study begins by looking at the existence of “space” as the basis of our lives as an object that can communicate psychologically. In particular, it is based on the perspective that it can be perceived as a dimension of sensory and emotional experience rather than as a physical object, focusing on 'architectural spaces' among numerous spaces. Experience in space arises in the relationships associated with mutual communication that cannot be achieved through contemplation. Because we live in relationships with various spaces, memory cannot exist except in space. Experiences based on space form our memories, and the original memories are not continuous but are fragmented and constantly reconstructed. I want to visualize the interior of this memory, which has such a discontinuous and dynamic structure, through my work. Based on this, I designed the ‘Structure of Structures’ series of works, combining fragmented spatial images into one screen through contingency and overlap, and I designed “metaCollage I,” an interactive digital collage system. Users can use this system to compose image pieces on a screen and fine-tune the properties of each piece to create a collage. Through this work, image fragments are combined on the screen to shape the shape of memory visually, and by creating new relationships and meanings between the fragments of memory, the intent here is to represent the meta-realization of memories and memories composed of fragments of memory.

Algorithmic support of the system of external observation and routing of autonomous mobile robots

This article presents the algorithmic support of the external monitoring and routing system of autonomous mobile robots. In some cases, the practical usage of mobile robots is related to the solution of navigation problems. In particular, the position of ground robots can be secured using unmanned aerial vehicles. In the proposed approach based on the video image obtained from an external video camera located above the working area of mobile robots, the location of both robots and nearby obstacles is recognized. The optimal route to the target point of the selected robot is built, and changes in its working area are monitored. Information about the allowed routes of the robot is transmitted to third-party applications via network communication channels. Primary image processing from the camera includes distortion correction, contouring and binarization, which allows to separate image fragments containing robots and obstacles from background surfaces and objects. Recognition of robots in a video frame is based on the use of a SURF detector. This technology extracts key points in the video frame and compares them with key points of reference images of robots. Trajectory planning is implemented using Dijkstra’s algorithm. The discreteness of the trajectories obtained using the algorithm for finding a path on the graph can be compensated for on board autonomous mobile robots by using spline approximation. Experimental studies have confirmed the efficiency of the proposed approach both in the problem of recognition and localization of mobile robots and in the problem of planning safe trajectories.