Search results for: pixel visualization

Authors: M. Afshin, A. Sohankar, M. Dehghan Manshadi, M. R. Daneshgar, G. R. Dehghan Kamaragi

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In this paper, the influence of an upstream structure on the flow pattern within and around the wind-catcher is experimentally investigated by smoke flow visualization techniques. Wind-catchers are an important part of natural ventilation in residential buildings or public places such as shopping centers, libraries, etc. Wind-catchers might be also used in places of high urban densities; hence their potential to provide natural ventilation in this case is dependent on the presence of upstream objects. In this study, the two-sided wind-catcher model was based on a real wind-catcher observed in the city of Yazd, Iran. The present study focuses on the flow patterns inside and outside the isolated two-sided wind-catcher, and on a two-sided wind-catcher in the presence of an upstream structure. The results show that the presence of an upstream structure influences the airflow pattern force and direction. Placing a high upstream object reverses the airflow direction inside the wind-catcher.

Keywords: natural ventilation, smoke flow visualization, two-sided wind-catcher, flow patterns

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Authors: Bansal Ashwani

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Cartographic depiction and visualization of wetland changes is an important tool to map spatial-temporal information about the wetland dynamics effectively and to comprehend the response of these water bodies in maintaining the groundwater and surrounding ecosystem. This is true for the states of North Western India, i.e., J&K, Himachal, Punjab, and Haryana that are bestowed upon with several natural wetlands in the flood plains or on the courses of its rivers. Thus, the present study documents, analyses and reconstructs the lost wetlands, which existed in the flood plains of the major river basins of these states, i.e., Chenab, Jhelum, Satluj, Beas, Ravi, and Ghagar, in the beginning of the 20th century. To achieve the objective, the study has used multi-temporal datasets since the 1960s using high to medium resolution satellite datasets, e.g., Corona (1960s/70s), Landsat (1990s-2017) and Sentinel (2017). The Sentinel (2017) satellite image has been used for making the wetland inventory owing to its comparatively higher spatial resolution with multi-spectral bands. In addition, historical records, repeated photographs, historical maps, field observations including geomorphological evidence were also used. The water index techniques, i.e., band rationing, normalized difference water index (NDWI), modified NDWI (MNDWI) have been compared and used to map the wetlands. The wetland types found in the north-western states have been categorized under 19 classes suggested by Space Application Centre, India. These enable the researcher to provide with the wetlands inventory and a series of cartographic representation that includes overlaying multiple temporal wetlands extent vectors. A preliminary result shows the general state of wetland shrinkage since the 1960s with varying area shrinkage rate from one wetland to another. In addition, it is observed that majority of wetlands have not been documented so far and even do not have names. Moreover, the purpose is to emphasize their elimination in addition to establishing a baseline dataset that can be a tool for wetland planning and management. Finally, the applicability of cartographic depiction and visualization, historical map sources, repeated photographs and remote sensing data for reconstruction of long term wetlands fluctuations, especially in the northern part of India, will be addressed.

Keywords: cartographic depiction and visualization, wetland changes, NDWI/MDWI, geomorphological evidence and remote sensing

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Authors: Benyelles Zakaria, Yousfi Djaafar, Karoui Moussa Sofiane

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Agriculture is fundamental and remains an important objective in the Algerian economy, based on traditional techniques and structures, it generally has a purpose of consumption. Collection of agricultural statistics in Algeria is done using traditional methods, which consists of investigating the use of land through survey and field survey. These statistics suffer from problems such as poor data quality, the long delay between collection of their last final availability and high cost compared to their limited use. The objective of this work is to develop a processing chain for a reliable inventory of agricultural land by trying to develop and implement a new method of extracting information. Indeed, this methodology allowed us to combine data from remote sensing and field data to collect statistics on areas of different land. The contribution of remote sensing in the improvement of agricultural statistics, in terms of area, has been studied in the wilaya of Sidi Bel Abbes. It is in this context that we applied a method for extracting information from satellite images. This method is called the non-negative matrix factorization, which does not consider the pixel as a single entity, but will look for components the pixel itself. The results obtained by the application of the MNF were compared with field data and the results obtained by the method of maximum likelihood. We have seen a rapprochement between the most important results of the FMN and those of field data. We believe that this method of extracting information from satellite data leads to interesting results of different types of land uses.

Keywords: blind source separation, hyper-spectral image, non-negative matrix factorization, remote sensing

Procedia PDF Downloads 391

Authors: Mohamed A. Almoghalis, Osman M. Hegazy, Ibrahim F. Imam, Ali H. Elbastawessy

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This paper presents a new hyperspectral image segmentation scheme with respect to both spatial and spectral contexts. The scheme uses the 8-pixels spatial pattern to build a weight structure that holds the number of outlier bands for each pixel among its neighborhood windows in different directions. The number of outlier bands for a pixel is obtained using bands quartile box plots profile among spatial 8-pixels pattern windows. The quartile box plot weight structure represents the spatial-spectral context in the image. Instead of starting segmentation process by single pixels, the proposed methodology starts by pixels groups that proved to share the same spectral features with respect to their spatial context. As a result, the segmentation scheme starts with Jigsaw pieces that build a mosaic image. The following step builds a model for each Jigsaw piece in the mosaic image. Each Jigsaw piece will be merged with another Jigsaw piece using KNN applied to their bands' quartile box plots profiles. The scheme iterates till required number of segments reached. Experiments use two data sets obtained from Earth Observer 1 (EO-1) sensor for Egypt and France. Initial results qualitative analysis showed encouraging results compared with ground truth. Quantitative analysis for the results will be included in the final paper.

Keywords: hyperspectral image segmentation, image processing, remote sensing, box plot

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Authors: Iwan Cony Setiadi, Aulia M. T. Nasution

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The multispectral imaging (MSI) technique has been used for skin analysis, especially for distant mapping of in-vivo skin chromophores by analyzing spectral data at each reflected image pixel. For ergonomic purpose, our multispectral imaging system is decomposed in two parts: a light source compartment based on LED with 11 different wavelenghts and a monochromatic 8-Bit CCD camera with C-Mount Objective Lens. The software based on GUI MATLAB to control the system was also developed. Our system provides 11 monoband images and is coupled with a software reconstructing hyperspectral cubes from these multispectral images. In this paper, we proposed a new method to build a hyperspectral reflectance cube based on artificial neural network algorithm. After preliminary corrections, a neural network is trained using the 32 natural color from X-Rite Color Checker Passport. The learning procedure involves acquisition, by a spectrophotometer. This neural network is then used to retrieve a megapixel multispectral cube between 380 and 880 nm with a 5 nm resolution from a low-spectral-resolution multispectral acquisition. As hyperspectral cubes contain spectra for each pixel; comparison should be done between the theoretical values from the spectrophotometer and the reconstructed spectrum. To evaluate the performance of reconstruction, we used the Goodness of Fit Coefficient (GFC) and Root Mean Squared Error (RMSE). To validate reconstruction, the set of 8 colour patches reconstructed by our MSI system and the one recorded by the spectrophotometer were compared. The average GFC was 0.9990 (standard deviation = 0.0010) and the average RMSE is 0.2167 (standard deviation = 0.064).

Keywords: multispectral imaging, reflectance cube, spectral reconstruction, artificial neural network

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Authors: Thusitha Mabotuwana, Christopher S. Hall

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Radiologists routinely make follow-up imaging recommendations, usually based on established clinical practice guidelines, such as the Fleischner Society guidelines for managing lung nodules. In order to ensure optimal care, it is important to make guideline-compliant recommendations, and also for patients to follow-up on these imaging recommendations in a timely manner. However, determining such compliance rates after a specific finding has been observed usually requires many time-consuming manual steps. To address some of these limitations with current approaches, in this paper we discuss a methodology to automatically detect finding-specific follow-up recommendations from radiology reports and create a visualization for relevant subsequent exams showing the modality transitions. Nearly 5% of patients who had a lung related follow-up recommendation continued to have at least eight subsequent outpatient CT exams during a seven year period following the recommendation. Radiologist and section chiefs can use the proposed tool to better understand how a specific patient population is being managed, identify possible deviations from established guideline recommendations and have a patient-specific graphical representation of the imaging pathways for an abstract view of the overall treatment path thus far.

Keywords: follow-up recommendations, follow-up tracking, care pathways, imaging pathway visualization

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Authors: Abd Al-Salam Al-Masgari, Haylay Tsegab, Ismailalwali Babikir, Monera A. Shoieb

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We present a study of Malaysia's Central Luconia region, which is an essential deposit of Miocene carbonate reservoirs. This study aims to identify and map areas of selected carbonate platforms, develop high-resolution statistical karst models, and generate comprehensive karst geobody models for selected carbonate fields. This study uses seismic characterization and advanced geophysical surveys to identify karst signatures in Miocene carbonate reservoirs. The results highlight the use of variance, RMS, RGB colour blending, and 3D visualization Prop seismic sequence stratigraphy seismic attributes to visualize the karstified areas across the F23 field of Central Luconia. The offshore karst model serves as a powerful visualization tool to reveal the karstization of carbonate sediments of interest. The results of this study contribute to a better understanding of the karst distribution of Miocene carbonate reservoirs in Central Luconia, which are essential for hydrocarbon exploration and production. This is because these features significantly impact the reservoir geometry, flow path and characteristics.

Keywords: karst, central Luconia, seismic attributes, Miocene carbonate build-ups

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Saahith M. S., Sivakami R.

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In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

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Authors: Nizar Karim Uribe-Orihuela, Fernando Brambila-Paz, Ivette Caldelas, Rodrigo Montufar-Chaveznava

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In this paper, we present the advances corresponding to the implementation of a strategy to visualize geographical data using a Software Development Kit (SDK) for video games. We use multispectral images from Landsat 7 platform and Laser Imaging Detection and Ranging (LIDAR) data from The National Institute of Geography and Statistics of Mexican (INEGI). We select a place of interest to visualize from Landsat platform and make some processing to the image (rotations, atmospheric correction and enhancement). The resulting image will be our gray scale color-map to fusion with the LIDAR data, which was selected using the same coordinates than in Landsat. The LIDAR data is translated to 8-bit raw data. Both images are fused in a software developed using Unity (an SDK employed for video games). The resulting image is then displayed and can be explored moving around. The idea is the software could be used for students of geology and geophysics at the Engineering School of the National University of Mexico. They will download the software and images corresponding to a geological place of interest to a smartphone and could virtually visit and explore the site with a virtual reality visor such as Google cardboard.

Keywords: virtual reality, interactive technologies, geographical data visualization, video games technologies, educational material

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Authors: Larissa Negris de Souza, Ana Regina Mizrahy Cuperschmid, Daniel de Carvalho Moreira

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Augmented Reality (AR) alters the elaboration of the architectural project, which relates to project cognition: representation, visualization, and perception of information. Understanding these features from the earliest stages of the design can facilitate the study of relationships, zoning, and overall dimensions of the forms. This paper’s goal was to explore a new approach for information visualization during the early stages of architectural design using Augmented Reality (AR). A three-dimensional marker inspired by the Rubik’s Cube was developed, and its performance, evaluated. This investigation interwovens the acquired knowledge of traditional briefing methods and contemporary technology. We considered the concept of patterns (Alexander et al. 1977) to outline geometric forms and associations using visual programming. The Design Science Research was applied to develop the study. An SDK was used in a game engine to generate the AR app. The tool's functionality was assessed by verifying the readability and precision of the reconfigurable 3D marker. The results indicated an inconsistent response. To use AR in the early stages of architectural design the system must provide consistent information and appropriate feedback. Nevertheless, we conclude that our framework sets the ground for looking deep into AR tools for briefing design.

Keywords: augmented reality, cuboid marker, early design stages, graphic representation, patterns

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Authors: Roghieh Eskandari, Ali Motamedi

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There is a significant demand for renovation as-built assets are aging. To plan for a desirable and comfortable indoor environment, stakeholders use simulation technics to assess potential renovation scenarios with the innovative designs. Diminished Reality (DR), which is a technique of visually removing unwanted objects from the real-world scene in real-time, can contribute to the renovation design visualization for stakeholders by removing existing structures and assets from the scene. Using DR, the objects to be demolished or changed will be visually removed from the scene for a better understanding of the intended design scenarios for stakeholders. This research proposes an integrated system for renovation plan visualization using Building Information Modelling (BIM) data and mixed reality (MR) technologies. It presents a BIM-based DR method that utilizes a textured BIM model of the environment to accurately register the virtual model of the occluded background to the physical world in real-time. This system can facilitate the simulation of the renovation plan by visually diminishing building elements in an indoor environment.

Keywords: diminished reality, building information modelling, mixed reality, stock renovation

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Authors: Saeed Vahedikamal, Ian Hepburn

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Microwave Kinetic Inductance Detectors (MKIDs) are considered as one of the most promising photon detectors of the future in many Astronomical applications such as exoplanet detections. The MKID advantages stem from their single photon sensitivity (ranging from UV to optical and near infrared), photon energy resolution and high temporal capability (~microseconds). There has been substantial progress in the development of these detectors and MKIDs with Megapixel arrays is now possible. The unique capability of recording an incident photon and its energy (or wavelength) while also registering its time of arrival to within a microsecond enables an array of MKIDs to produce a four-dimensional data block of x, y, z and t comprising x, y spatial, z axis per pixel spectral and t axis per pixel which is temporal. This offers the possibility that the spectrum and brightness variation for any detected piece of space debris as a function of time might offer a unique identifier or fingerprint. Such a fingerprint signal from any object identified in multiple detections by different observers has the potential to determine the orbital features of the object and be used for their tracking. Modelling performed so far shows that with a 20 cm telescope located at an Astronomical observatory (e.g. La Palma, Canary Islands) we could detect sub cm objects at GEO. By considering a Lambertian sphere with a 10 % reflectivity (albedo of the Moon) we anticipate the following for a GEO object: 10 cm object imaged in a 1 second image capture; 1.2 cm object for a 70 second image integration or 0.65 cm object for a 4 minute image integration. We present details of our modelling and the potential instrument for a dedicated GEO surveillance system.

Keywords: space debris, orbital debris, detection system, observation, microwave kinetic inductance detectors, MKID

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Authors: Sugeng Rianto, P.W. Arinto Yudi, Soemarno Muhammad Nurhuda

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A computation of a 3D compressible fluid flow for virtual environment with haptic interaction can be a non-trivial issue. This is especially how to reach good performances and balancing between visualization, tactile feedback interaction, and computations. In this paper, we describe our approach of computation methods based on parallel programming on a GPU. The 3D fluid flow solvers have been developed for smoke dispersion simulation by using combinations of the cubic interpolated propagation (CIP) based fluid flow solvers and the advantages of the parallelism and programmability of the GPU. The fluid flow solver is generated in the GPU-CPU message passing scheme to get rapid development of haptic feedback modes for fluid dynamic data. A rapid solution in fluid flow solvers is developed by applying cubic interpolated propagation (CIP) fluid flow solvers. From this scheme, multiphase fluid flow equations can be solved simultaneously. To get more acceleration in the computation, the Navier-Stoke Equations (NSEs) is packed into channels of texel, where computation models are performed on pixels that can be considered to be a grid of cells. Therefore, despite of the complexity of the obstacle geometry, processing on multiple vertices and pixels can be done simultaneously in parallel. The data are also shared in global memory for CPU to control the haptic in providing kinaesthetic interaction and felling. The results show that GPU based parallel computation approaches provide effective simulation of compressible fluid flow model for real-time interaction in 3D computer graphic for PC platform. This report has shown the feasibility of a new approach of solving the compressible fluid flow equations on the GPU. The experimental tests proved that the compressible fluid flowing on various obstacles with haptic interactions on the few model obstacles can be effectively and efficiently simulated on the reasonable frame rate with a realistic visualization. These results confirm that good performances and balancing between visualization, tactile feedback interaction, and computations can be applied successfully.

Keywords: CIP, compressible fluid, GPU programming, parallel computation, real-time visualisation

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Authors: Chun Chen Yea, Wen Huei Chou

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Many chemical changes in the atmosphere and the ocean are invisible to the naked eye, but they have profound impacts. These changes not only confirm the phenomenon of global carbon pollution, but also forewarn that more changes are coming. The carbon dioxide gases emitted from the burning of fossil fuels dissolve into the ocean and chemically react with seawater to form carbonic acid, which increases the acidity of the originally alkaline seawater. This gradual acidification is occurring at an unprecedented rate and will affect the effective formation of carapace of some marine organisms such as corals and crustaceans, which are almost entirely composed of calcium carbonate. The carapace of these organisms will become more dissoluble. Acidified seawater not only threatens the survival of marine life, but also negatively impacts the global ecosystem via the food chain. Faced with the threat of ocean acidification, all humans are duty-bound. The industrial sector outputs the highest level of carbon dioxide emissions in Taiwan, and the petrochemical industry is the major contributor. Ever since the construction of Formosa Plastics Group's No. 6 Naphtha Cracker Plant in Yunlin County, there have been many environmental concerns such as air pollution and carbon dioxide emission. The marine life along the coast of Yunlin is directly affected by ocean acidification arising from the carbon emissions. Societal change demands our willingness to act, which is what social advocacy promotes. This study uses digital storytelling for social advocacy and ocean acidification as the subject of a visual narrative in visualization to demonstrate the subsequent promotion of social advocacy. Storytelling can transform dull knowledge into an engaging narrative of the crisis faced by marine life. Digital dissemination is an effective social-work practice. The visualization promoting awareness on ocean acidification disseminated via social media platforms, such as Facebook and Instagram. Social media enables users to compose their own messages and share information across different platforms, which helps disseminate the core message of social advocacy.

Keywords: digital storytelling, visualization, ocean acidification, social advocacy

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Authors: Monisha Pattanaik

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In scenarios where users have to work with large amounts of hierarchical data structures combined with visualizations (For example, Construction 3d Models, Manufacturing equipment's models, Gantt charts, Building Plans), the data structures have a high density in terms of consisting multiple parent nodes up to 50 levels and their siblings to descendants, therefore convey an immediate feeling of complexity. With customers moving to consumer-grade enterprise software, it is crucial to make sophisticated features made available to touch devices or smaller screen sizes. This paper evaluates the UI component that allows users to scroll through all deep density levels using a slider overlay on top of the hierarchy table, performing several actions to focus on one set of objects at any point in time. This overlay component also solves the problem of excessive horizontal scrolling of the entire table on a fixed pane for a hierarchical table. This component can be customized to navigate through parents, only siblings, or a specific component of the hierarchy only. The evaluation of the UI component was done by End Users of application and Human-Computer Interaction (HCI) experts to test the UI component's usability with statistical results and recommendations to handle complex hierarchical data visualizations.

Keywords: building information modeling, digital twin, navigation, UI component, user interface, usability, visualization

Procedia PDF Downloads 111

Authors: Leonardo A. Ambrosio, Carlos. H. Silva Santos, Ivan E. L. Rodrigues, Ayumi K. de Campos, Leandro A. Machado

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In this work, we propose an algorithm developed under Python language for the modeling of ordinary scalar Bessel beams and their discrete superpositions and subsequent calculation of optical forces exerted over dielectric spherical particles. The mathematical formalism, based on the generalized Lorenz-Mie theory, is implemented in Python for its large number of free mathematical (as SciPy and NumPy), data visualization (Matplotlib and PyJamas) and multiprocessing libraries. We also propose an approach, provided by a synchronized Software as Service (SaaS) in cloud computing, to develop a user interface embedded on a mobile application, thus providing users with the necessary means to easily introduce desired unknowns and parameters and see the graphical outcomes of the simulations right at their mobile devices. Initially proposed as a free Android-based application, such an App enables data post-processing in cloud-based architectures and visualization of results, figures and numerical tables.

Keywords: Bessel Beams and Frozen Waves, Generalized Lorenz-Mie Theory, Numerical Methods, optical forces

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Authors: Keerthilatha Pai, Sakshi Kamra

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Objectives: Determining the position of the mandibular canal prior to implant placement and surgeries of the posterior mandible are important to avoid the nerve injury. The visibility of the mandibular canal varies according to the imaging modality. Although panoramic radiography is the most common, slowly cone beam computed tomography is replacing it. This study was conducted with an aim to determine and compare the visibility of superior and inferior borders of the mandibular canal in digital panoramic radiograph, reformatted panoramic radiograph and cross-sectional images of cone beam computed tomography. Study design: digital panoramic, reformatted panoramic radiograph and cross sectional CBCT images of 25 human mandibles were evaluated for the visibility of the superior and inferior borders of the mandibular canal according to a 5 point scoring criteria. Also, the canal was evaluated as completely visible, partially visible and not visible. The mean scores and visibility percentage of all the imaging modalities were determined and compared. The interobserver and intraobserver agreement in the visualization of the superior and inferior borders of the mandibular canal were determined. Results: The superior and inferior borders of the mandibular canal were completely visible in 47% of the samples in digital panoramic, 63% in reformatted panoramic and 75.6% in CBCT cross-sectional images. The mandibular canal was invisible in 24% of samples in digital panoramic, 19% in reformatted panoramic and 2% in cross-sectional CBCT images. Maximum visibility was seen in Zone 5 and least visibility in Zone 1. On comparison of all the imaging modalities, CBCT cross-sectional images showed better visibility of superior border in Zones 2,3,4,6 and inferior border in Zones 2,3,4,6. The difference was statistically significant. Conclusion: CBCT cross-sectional images were much superior in the visualization of the mandibular canal in comparison to reformatted and digital panoramic radiographs. The inferior border was better visualized in comparison to the superior border in digital panoramic imaging. The mandibular canal was maximumly visible in posterior one-third region of the mandible and the visibility decreased towards the mental foramen.

Keywords: cone beam computed tomography, mandibular canal, reformatted panoramic radiograph, visualization

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Authors: Aoqi Zhang, Changgil Lee Lee, Seunghee Park

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A non-contact nondestructive technique using laser-induced ultrasonic wave generation method was applied to visualize corrosion damage at aluminum alloy plate structures. The ultrasonic waves were generated by a Nd:YAG pulse laser, and a galvanometer-based laser scanner was used to scan specific area at a target structure. At the same time, wave responses were measured at a piezoelectric sensor which was attached on the target structure. The visualization of structural damage was achieved by calculating logarithmic values of root mean square (RMS). Damage-sensitive feature was defined as the scattering characteristics of the waves that encounter corrosion damage. The corroded damage was artificially formed by hydrochloric acid. To observe the effect of the location where the corrosion was formed, the both sides of the plate were scanned with same scanning area. Also, the effect on the depth of the corrosion was considered as well as the effect on the size of the corrosion. The results indicated that the damages were successfully visualized for almost cases, whether the damages were formed at the front or back side. However, the damage could not be clearly detected because the depth of the corrosion was shallow. In the future works, it needs to develop signal processing algorithm to more clearly visualize the damage by improving signal-to-noise ratio.

Keywords: non-destructive testing, corrosion, pulsed laser scanning, ultrasonic waves, plate structure

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Authors: Anthony D. Rhodes, Manan Goel

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We provide a high fidelity deep learning algorithm (HyperSeg) for interactive video segmentation tasks using a dense convolutional network with context-aware skip connections and compressed, 'hypercolumn' image features combined with a convolutional tessellation procedure. In order to maintain high output fidelity, our model crucially processes and renders all image features in high resolution, without utilizing downsampling or pooling procedures. We maintain this consistent, high grade fidelity efficiently in our model chiefly through two means: (1) we use a statistically-principled, tensor decomposition procedure to modulate the number of hypercolumn features and (2) we render these features in their native resolution using a convolutional tessellation technique. For improved pixel-level segmentation results, we introduce a boundary loss function; for improved temporal coherence in video data, we include temporal image information in our model. Through experiments, we demonstrate the improved accuracy of our model against baseline models for interactive segmentation tasks using high resolution video data. We also introduce a benchmark video segmentation dataset, the VFX Segmentation Dataset, which contains over 27,046 high resolution video frames, including green screen and various composited scenes with corresponding, hand-crafted, pixel-level segmentations. Our work presents a improves state of the art segmentation fidelity with high resolution data and can be used across a broad range of application domains, including VFX pipelines and medical imaging disciplines.

Keywords: computer vision, object segmentation, interactive segmentation, model compression

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Authors: Sheng-Po Tseng, Che-Hua Yang

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Motivation and Objective: Ultrasonic guided waves become an important tool for nondestructive evaluation of structures and components. Guided waves are used for the purpose of identifying defects or evaluating material properties in a nondestructive way. While guided waves are applied for evaluating material properties, instead of knowing the properties directly, preliminary signals such as time domain signals or frequency domain spectra are first revealed. With the measured ultrasound data, inversion calculation can be further employed to obtain the desired mechanical properties. Methods: This research is development of high speed inversion calculation technique for obtaining full-field mechanical properties from the quantitative laser ultrasound visualization system (QLUVS). The quantitative laser ultrasound visualization system (QLUVS) employs a mirror-controlled scanning pulsed laser to generate guided acoustic waves traveling in a two-dimensional target. Guided waves are detected with a piezoelectric transducer located at a fixed location. With a gyro-scanning of the generation source, the QLUVS has the advantage of fast, full-field, and quantitative inspection. Results and Discussions: This research introduces two important tools to improve the computation efficiency. Firstly, graphic procession unit (GPU) with large amount of cores are introduced. Furthermore, combining the CPU and GPU cores, parallel procession scheme is developed for the inversion of full-field mechanical properties based on the QLUVS data. The newly developed inversion scheme is applied to investigate the computation efficiency for single-layered and double-layered plate-like samples. The computation efficiency is shown to be 80 times faster than unparalleled computation scheme. Conclusions: This research demonstrates a high-speed inversion technique for the characterization of full-field material properties based on quantitative laser ultrasound visualization system. Significant computation efficiency is shown, however not reaching the limit yet. Further improvement can be reached by improving the parallel computation. Utilizing the development of the full-field mechanical property inspection technology, full-field mechanical property measured by non-destructive, high-speed and high-precision measurements can be obtained in qualitative and quantitative results. The developed high speed computation scheme is ready for applications where full-field mechanical properties are needed in a nondestructive and nearly real-time way.

Keywords: guided waves, material characterization, nondestructive evaluation, parallel processing

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Authors: Ismail Elkhrachy

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Determination of land use changing is an important component of regional planning for applications ranging from urban fringe change detection to monitoring change detection of land use. This data are very useful for natural resources management.On the other hand, the technologies and methods of change detection also have evolved dramatically during past 20 years. So it has been well recognized that the change detection had become the best methods for researching dynamic change of land use by multi-temporal remotely-sensed data. The objective of this paper is to assess, evaluate and monitor land use change surrounding the area of Najran city, Kingdom of Saudi Arabia (KSA) using Landsat images (June 23, 2009) and ETM+ image(June. 21, 2014). The post-classification change detection technique was applied. At last,two-time subset images of Najran city are compared on a pixel-by-pixel basis using the post-classification comparison method and the from-to change matrix is produced, the land use change information obtained.Three classes were obtained, urban, bare land and agricultural land from unsupervised classification method by using Erdas Imagine and ArcGIS software. Accuracy assessment of classification has been performed before calculating change detection for study area. The obtained accuracy is between 61% to 87% percent for all the classes. Change detection analysis shows that rapid growth in urban area has been increased by 73.2%, the agricultural area has been decreased by 10.5 % and barren area reduced by 7% between 2009 and 2014. The quantitative study indicated that the area of urban class has unchanged by 58.2 km〗^2, gained 70.3 〖km〗^2 and lost 16 〖km〗^2. For bare land class 586.4〖km〗^2 has unchanged, 53.2〖km〗^2 has gained and 101.5〖km〗^2 has lost. While agriculture area class, 20.2〖km〗^2 has unchanged, 31.2〖km〗^2 has gained and 37.2〖km〗^2 has lost.

Keywords: land use, remote sensing, change detection, satellite images, image classification

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Authors: Donatella Giuliani

Abstract:

In this research, we propose an unsupervised grayscale image segmentation method based on a combination of the Firefly Algorithm and the Gaussian Mixture Model. Firstly, the Firefly Algorithm has been applied in a histogram-based research of cluster means. The Firefly Algorithm is a stochastic global optimization technique, centered on the flashing characteristics of fireflies. In this context it has been performed to determine the number of clusters and the related cluster means in a histogram-based segmentation approach. Successively these means are used in the initialization step for the parameter estimation of a Gaussian Mixture Model. The parametric probability density function of a Gaussian Mixture Model is represented as a weighted sum of Gaussian component densities, whose parameters are evaluated applying the iterative Expectation-Maximization technique. The coefficients of the linear super-position of Gaussians can be thought as prior probabilities of each component. Applying the Bayes rule, the posterior probabilities of the grayscale intensities have been evaluated, therefore their maxima are used to assign each pixel to the clusters, according to their gray-level values. The proposed approach appears fairly solid and reliable when applied even to complex grayscale images. The validation has been performed by using different standard measures, more precisely: the Root Mean Square Error (RMSE), the Structural Content (SC), the Normalized Correlation Coefficient (NK) and the Davies-Bouldin (DB) index. The achieved results have strongly confirmed the robustness of this gray scale segmentation method based on a metaheuristic algorithm. Another noteworthy advantage of this methodology is due to the use of maxima of responsibilities for the pixel assignment that implies a consistent reduction of the computational costs.

Keywords: clustering images, firefly algorithm, Gaussian mixture model, meta heuristic algorithm, image segmentation

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Authors: Xingdu Wang, Charlie Gere, Emma Rose, Yuxuan Zhao

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The study introduces a fresh perspective on the digital realm through an examination of the Chinese concept of Xiang, elucidating how it can build an understanding of pixels and textures on screens as digital trigrams. This concept attempts to offer an outlook on the intersection of digital technology and the natural world, thereby contributing to discussions about the harmonious relationship between humans and technology. The study looks for the ancient Chinese theory of Xiang as a key to establishing the theories and practices to respond to the problem of Contemporary Chinese technics. Xiang is a Chinese method of understanding the essentials of things through appearances, which differs from the method of science in the Westen. Xiang, the basement of Chinese visual art, is rooted in ancient Chinese philosophy and connected to the eight trigrams. The discussion of Xiang connects art, philosophy, and technology. This paper connects the meaning of Xiang with the 'truth appearing' philosophically through the analysis of the concepts of phenomenon and noumenon and the unique Chinese way of observing. Hereafter, the historical interconnection between ancient painting and writing in China emphasizes their relationship between technical craftsmanship and artistic expression. In digital, the paper blurs the traditional boundaries between images and text on digital screens in theory. Lastly, this study identified an ensemble concept relating to pixels and textures in computer vision, drawing inspiration from AI image recognition in Chinese paintings. In art practice, by presenting a fluid visual experience in the form of pixels, which mimics the flow of lines in traditional calligraphy and painting, it is hoped that the viewer will be brought back to the process of the truth appearing as defined by the 'Xiang’.

Keywords: Chinese cosmotechnics, computer vision, contemporary Neo-Confucianism, texture and pixel, Xiang

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Authors: Alessandro Evangelista, Vito M. Manghisi, Michele Gattullo, Enricoandrea Laviola

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In this work, we propose a custom visualization technique for Out-Of-View-Objects in Virtual and Augmented Reality applications using Head Mounted Displays. In the last two decades, Augmented Reality (AR) and Virtual Reality (VR) technologies experienced a remarkable growth of applications for navigation, interaction, and collaboration in different types of environments, real or virtual. Both environments can be potentially very complex, as they can include many virtual objects located in different places. Given the natural limitation of the human Field of View (about 210° horizontal and 150° vertical), humans cannot perceive objects outside this angular range. Moreover, despite recent technological advances in AR e VR Head-Mounted Displays (HMDs), these devices still suffer from a limited Field of View, especially regarding Optical See-Through displays, thus greatly amplifying the challenge of visualizing out-of-view objects. This problem is not negligible when the user needs to be aware of the number and the position of the out-of-view objects in the environment. For instance, during a maintenance operation on a construction site where virtual objects serve to improve the dangers' awareness. Providing such information can enhance the comprehension of the scene, enable fast navigation and focused search, and improve users' safety. In our research, we investigated how to represent out-of-view-objects in HMD User Interfaces (UI). Inspired by commercial video games such as Call of Duty Modern Warfare, we designed a customized Compass. By exploiting the Unity 3D graphics engine, we implemented our custom solution that can be used both in AR and VR environments. The Compass Bar consists of a graduated bar (in degrees) at the top center of the UI. The values of the bar range from -180 (far left) to +180 (far right), the zero is placed in front of the user. Two vertical lines on the bar show the amplitude of the user's field of view. Every virtual object within the scene is represented onto the compass bar as a specific color-coded proxy icon (a circular ring with a colored dot at its center). To provide the user with information about the distance, we implemented a specific algorithm that increases the size of the inner dot as the user approaches the virtual object (i.e., when the user reaches the object, the dot fills the ring). This visualization technique for out-of-view objects has some advantages. It allows users to be quickly aware of the number and the position of the virtual objects in the environment. For instance, if the compass bar displays the proxy icon at about +90, users will immediately know that the virtual object is to their right and so on. Furthermore, by having qualitative information about the distance, users can optimize their speed, thus gaining effectiveness in their work. Given the small size and position of the Compass Bar, our solution also helps lessening the occlusion problem thus increasing user acceptance and engagement. As soon as the lockdown measures will allow, we will carry out user-tests comparing this solution with other state-of-the-art existing ones such as 3D Radar, SidebARs and EyeSee360.

Keywords: augmented reality, situation awareness, virtual reality, visualization design

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Authors: Evridiki Papachristou, Nikolaos Bilalis

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Imagine an apparel industry where fashion design does not begin with a paper-and-pen drawing which is then translated into pattern and later to a 3D model where the designer tries out different fabrics, colours and contrasts. Instead, imagine a fashion designer in the future who produces that initial fashion drawing in a three-dimensional space and won’t leave that environment until the product is done, communicating his/her ideas with the entire development team in true to life 3D. Three-dimensional (3D) technology - while well established in many other industrial sectors like automotive, aerospace, architecture and industrial design, has only just started to open up a whole range of new opportunities for apparel designers. The paper will discuss the process of 3D simulation technology enhanced by high quality visualization of data and its capability to ensure a massive competitiveness in the market. Secondly, it will underline the most frequent problems & challenges that occur in the process chain when various partners in the production of textiles and apparel are working together. Finally, it will offer a perspective of how the Virtual Prototyping Technology will make the global textile and apparel industry change to a level where designs will be visualized on a computer and various scenarios modeled without even having to produce a physical prototype. This state-of-the-art 3D technology has been described as transformative and“disruptive”comparing to the process of the way apparel companies develop their fashion products today. It provides the benefit of virtual sampling not only for quick testing of design ideas, but also reducing process steps and having more visibility.A so called“digital asset” that can be used for other purposes such as merchandising or marketing.

Keywords: 3D visualization, apparel, virtual prototyping, prototyping technology

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Authors: Jake Gonzalez, Tommy Dang

Abstract:

This paper proposes a temporal graph approach to visualize and analyze the evolution of gene relationships and nomenclature over time. An interactive web-based tool implements this temporal graph, enabling researchers to traverse a timeline and observe coupled dynamics in network topology and naming conventions. Analysis of a real human genomic dataset reveals the emergence of densely interconnected functional modules over time, representing groups of genes involved in key biological processes. For example, the antimicrobial peptide DEFA1A3 shows increased connections to related alpha-defensins involved in infection response. Tracking degree and betweenness centrality shifts over timeline iterations also quantitatively highlight the reprioritization of certain genes’ topological importance as knowledge advances. Examination of the CNR1 gene encoding the cannabinoid receptor CB1 demonstrates changing synonymous relationships and consolidating naming patterns over time, reflecting its unique functional role discovery. The integrated framework interconnecting these topological and nomenclature dynamics provides richer contextual insights compared to isolated analysis methods. Overall, this temporal graph approach enables a more holistic study of knowledge evolution to elucidate complex biology.

Keywords: temporal graph, gene relationships, nomenclature evolution, interactive visualization, biological insights

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Authors: Young-Hyun Seo, Hosuk Shin, Eun-Hak Lee, Seung-Young Kho

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This study analyzed and visualized the rental and return data of the public bike sharing system in Seoul, Ttareungyi, from September 2015 to October 2017. With the surge of system users, the number of times of collection and distribution in 2017 increased by three times compared to 2016. The city plans to deploy about 20,000 public bicycles by the end of 2017 to expand the system. Based on about 3.3 million historical data, we calculated the average trip time and the number of trips from one station to another station. The mobility patterns between stations are graphically displayed using R and Tableau. Demand for public bike sharing system is heavily influenced by day and weather. As a result of plotting the number of rentals and returns of some stations on weekdays and weekends at intervals of one hour, there was a difference in rental patterns. As a result of analysis of the rental and return patterns by time of day, there were a lot of returns at the morning peak and more rentals at the afternoon peak at the center of the city. It means that stock of bikes varies largely in the time zone and public bikes should be rebalanced timely. The result of this study can be applied as a primary data to construct the demand forecasting function of the station when establishing the rebalancing strategy of the public bicycle.

Keywords: demand forecasting, mobility patterns, public bike sharing system, visualization

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Authors: Kyung Bae Park, Sung Ho Ha

Abstract:

Online user-generated contents (UGC) significantly change the way customers behave (e.g., shop, travel), and a pressing need to handle the overwhelmingly plethora amount of various UGC is one of the paramount issues for management. However, a current approach (e.g., sentiment analysis) is often ineffective for leveraging textual information to detect the problems or issues that a certain management suffers from. In this paper, we employ text mining of Latent Dirichlet Allocation (LDA) on a popular online review site dedicated to complaint from users. We find that the employed LDA efficiently detects customer complaints, and a further inspection with the visualization technique is effective to categorize the problems or issues. As such, management can identify the issues at stake and prioritize them accordingly in a timely manner given the limited amount of resources. The findings provide managerial insights into how analytics on social media can help maintain and improve their reputation management. Our interdisciplinary approach also highlights several insights by applying machine learning techniques in marketing research domain. On a broader technical note, this paper illustrates the details of how to implement LDA in R program from a beginning (data collection in R) to an end (LDA analysis in R) since the instruction is still largely undocumented. In this regard, it will help lower the boundary for interdisciplinary researcher to conduct related research.

Keywords: latent dirichlet allocation, R program, text mining, topic model, user generated contents, visualization

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Authors: Shao Qi

Abstract:

The article uses the CiteSpace knowledge graph analysis tool to sort and visualize the journal literature on urban plants and habitats in the Web of Science and China National Knowledge Infrastructure databases. Based on a comprehensive interpretation of the visualization results of various data sources and the description of the intrinsic relationship between high-frequency keywords using knowledge mapping, the research hotspots, processes and evolution trends in this field are analyzed. Relevant case studies are also conducted for the hotspot contents to explore the means of landscape intervention and synthesize the understanding of research theories. The results show that (1) from 1999 to 2022, the research direction of urban plants and habitats gradually changed from focusing on plant and animal extinction and biological invasion to the field of human urban habitat creation, ecological restoration, and ecosystem services. (2) The results of keyword emergence and keyword growth trend analysis show that habitat creation research has shown a rapid and stable growth trend since 2017, and ecological restoration has gained long-term sustained attention since 2004. The hotspots of future research on urban plants and habitats in China may focus on habitat creation and ecological restoration.

Keywords: research trends, visual analysis, habitat creation, ecological restoration

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