Figure 41 - uploaded by Klaus Mueller
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![The effect of applying combinational operations to the interior of volumetric CVG objects (from Chen]). From left to right, they are cylinder c, (fuzzy) sphere s, union(c,s), intersection(c,s), blend(c,s), diff(c,s), and diff(c,s). The geometry (i.e., opacity) and color of both cylinder and sphere are specified using simple scalar fields. For instance, the opacity field of the sphere is defined as (1-r) where r is the radius. Only the iso-surface at d=0.1 of each object is rendered. Each object is also subtracted by an additional spatial object to reveal its internal structure. (Images taken from [35].)](profile/Klaus-Mueller-13/publication/266248612/figure/fig4/AS:669409861451783@1536611156879/The-effect-of-applying-combinational-operations-to-the-interior-of-volumetric-CVG-objects.png)
The effect of applying combinational operations to the interior of volumetric CVG objects (from Chen]). From left to right, they are cylinder c, (fuzzy) sphere s, union(c,s), intersection(c,s), blend(c,s), diff(c,s), and diff(c,s). The geometry (i.e., opacity) and color of both cylinder and sphere are specified using simple scalar fields. For instance, the opacity field of the sphere is defined as (1-r) where r is the radius. Only the iso-surface at d=0.1 of each object is rendered. Each object is also subtracted by an additional spatial object to reveal its internal structure. (Images taken from [35].)
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... As early as the mid-1970s, with the development and maturity of medical imaging techniques such as tomographic projection (Computed Tomography, CT), nuclear magnetic resonance ((Magnetic Resonance Imaging, MRI) and ultrasonic detection, researches on volume rendering have been put forward continuously. (Kaufman, 1990;Kruger and Westermann, 2003). ...
The Three-Dimension visualization effect is limited by the performance of equipment and algorithm when dealing with high-dimensional and large-scale geological data. So it is very difficult to graph the data accurately in real-time. In this paper, an accurate and efficient real-time visualization method is studied, which combines the distribution characteristics of geological data in space and the physical law of multi-dimensional logging attributes. Firstly, the spatial data field is evenly divided into sampling intervals of the same size, and the dispersion degree of numerical distribution of geological data in different sampling intervals is counted, and the sampling interval is divided into different sampling density grades according to the degree of dispersion clustering. The logging data are sampled according to the sampling density level in each sampling interval to compress the data scale. Secondly, the Pearson coefficient is used to classify logging attributes to solve visual switching delay caused by multiple dimensions of data. Third, The basis vector and its coefficients are obtained through intra-class subspace learning, based on which the mapping model between attributes is established. When the attribute is switched, only the coefficient value in the mapping model needs to be changed to reduce the amount of data exchange and ensure real-time visualization. The experimental results show that real-time visualization of large-scale geological data can be realized using this method, which supports multi-attribute dynamic switching and has good rendering accuracy.
... Volume data can be captured by many technologies, such as CT or MRI scan technology. Volume visualization has been explored in many domains and it is often used in representing medical related data, such as functional MRI data from scans, data from confocal microscopy (Kaufman, 1996). ...
This chapter will first address data visualization and then discuss the relationship between data visualization and aesthetics. It discusses the definition of data and information and the forms and characteristics of traditional data visualization, emphasizes on understanding of meaning of data in effectiveness and efficiency. And then this chapter outlines some key data visualizations, which includes Trees, Scatter plots, Charts, Tables, Diagram, Graphic, Waveform, Simulation and Volume.
... Для остаточної чіткої достовірної діагностичної картини великого значення набувають методи подальшої обробки зображення. Якщо припустити, що у міру переходу від одного перерізу до іншого об'єкт не устигає змінитися, то створюваний сканером набір двовимірних дискретних зображень (дискретна тривимірна сцена) є тривимірною множиною елементарних об'ємів [1]. ...
... В результаті отримують тривимірну матрицю, за допомогою якої відновлюється тривимірне зображення. Залежно від призначення дискретні тривимірні сцени, що створюються сканерами, дають інформацію про структуру об'єкту і (або) про його функціональний стан [1][2]. ...
... The scanner was set-up to 100 kV, 100 μA with image acquisitions of 500 ms, and the X-ray beam was filtered with a 0.5 mm Cu filter. The scans were performed at a 35 μm resolution and reconstructed into a 3D model using GE Datos 2.1 reconstruction package by means of a filtered backprojection algorithm, and the data were visualized and analyzed using Volume Graphics VGStudio Max 3.1 (Kaufman and Mueller, 2003). ...
A conspicuous paucity in gold resources in Malawi’s mineral inventory may be partially attributed to a general low resolution in the knowledge of Malawi’s geology, coupled with historical under-exploration for this important economic commodity. To address this knowledge gap, the present study focusses on developing a regional, local and microscopic understanding of the characteristics of gold occurrences in the mineralized Little Chisumbwiti river valley, located in the Kirk Range region which forms part of Southern Irumide mobile belt in southern Malawi. Regional scale interpretations are afforded by in-depth investigation of airborne geophysical data, which are then supported by ground geological mapping and by microscopic observations using optical and electron microscopy, and X-ray computed tomography (XCT). Structural mapping and regional geophysics indicate that the area has been affected by at least two periods of deformation (D1 to D2). The D1 deformation event is characterized by northwest – southeast directed crustal shortening (tentatively associated with the Southern Irumide orogeny) which produced northeast – southwest structures. Gold mineralization in the Little Chisumbwiti river valley is hosted in northeast – southwest trending quartz—sulphide vein sets, where it occurs as flakes ranging in size between 0.24 and 4 mm. The gold is associated with a paragenetically-late pyrite-chalcopyrite-sphalerite generation of sulphide precipitation, and to a lesser extent, with sericitised biotite schist wall rock. Exploration in the Kirk Range region should focus on the northeast-southwest structures, which represent potential conduits for fluid flow during the D1-related hypogene Au mineralization event, however the impacts of subsequent D2 deformation must also be considered. Locally, wall rock lithology (e.g., Fe (II)-rich biotite schists) may have an additional second order control on the siting of gold mineralization. The new insights reported in this study contribute meaningfully towards advancing the status of Malawi’s regional geology and should serve as a useful resource for future exploration efforts undertaken in the Kirk Range, and in other parts of south/central Malawi occurring within the Southern Irumide tectonic block.
... Th ese two techniques were compared by independent researchers who indicated advantages of surface rendering over volume rendering technique concerning speed in image performance, graphical appearance, and computer requirements (type of CPU, size of memory, disk space) necessary for processing and storing rendered data [21][22]. However, volume rendering convey more information than surface rendering images, but requires more eff ective algorithms for processing image data and longer time for performing 3D visualization [23]. Extracted iso-surfaces from the volume can be rendered as polygonal meshes (usually mesh of triangles which approximate object's surface). ...
This report provides a concise overview of the rendering and utilization of three-dimensional models in the field of anatomy. Anatomical three-dimensional virtual models are widely used for educational purposes, preoperative planning, and surgical simulations because they simply allow for interactive three-dimensional navigation across the human organs or entire body. Virtual three-dimensional models have been recently fabricated as accurate replicas of the anatomical structures thanks to advances in rapid prototyping technology.
... Scientific visualization is a method of scientific computing. It converts acquired symbolic data into a geometric form to convey silent information of underlying data and to see the unseen structure which is beneficial for comprehension, analysis, and interpretation [1,2]. The field of scientific visualization has been widely explored in the last three decades. ...
Volume visualization or 3D imaging filed is vibrant and one of the fastest growing filed in scientific visualization. This field is focused on creating high-quality 3D images from acquired volumetric datasets to gain insights into underlying data. Biomedical filed uses 3D scanning devices(such as computed tomography (CT), and magnetic resonance imaging (MRI) scanners for instance) to generate the volumetric data. Several volume visualization techniques are devised to create a 3D view from acquired volumetric datasets. The generated 3D view provides a better understanding of patient anatomy which interns help to analyze and diagnose the disease moreover to decide the most suitable treatment plan/process. Hence from last three decades many computer graphics programmers, biomedical engineers, and researchers are working hard to develop time and space efficient volume visualization techniques. The main aim of this review is to provide detailed information about the state of the art volume visualization techniques majorly applied in the biomedical field. In addition to this, recent advances in volume visualization techniques are discussed. Some commonly used tools and libraries used for volume visualization and several application areas of volume visualization in the biomedical field are explored.
... Scientific visualization is a method of scientific computing. It converts acquired symbolic data into a geometric form to convey silent information of underlying data and to see the unseen structure which is beneficial for comprehension, analysis, and interpretation [1,2]. The field of scientific visualization has been widely explored in the last three decades. ...
Volume visualization or 3D imaging filed is vibrant and one of the fastest growing filed in scientific visualization. This field is focused on creating high-quality 3D images from acquired volumetric datasets to gain insights into underlying data. Biomedical filed uses 3D scanning devices(such as computed tomography (CT), and magnetic resonance imaging (MRI) scanners for instance) to generate the volumetric data. Several volume visualization techniques are devised to create a 3D view from acquired volumetric datasets. The generated 3D view provides a better understanding of patient anatomy which interns help to analyze and diagnose the disease moreover to decide the most suitable treatment plan/process. Hence from last three decades many computer graphics programmers, biomedical engineers, and researchers are working hard to develop time and space efficient volume visualization techniques. The main aim of this review is to provide detailed information about the state of the art volume visualization techniques majorly applied in the biomedical field. In addition to this, recent advances in volume visualization techniques are discussed. Some commonly used tools and libraries used for volume visualization and several application areas of volume visualization in the biomedical field are explored.
... FDS data and 3D rendering technology were used to realistically and effectively simulate the smoke in the actual fire scenes. FDS data has been verified to be able to provide accurate fire dynamic results [19], and 3D rendering technology has also been verified to be very suitable for integration with FDS grid data [20]. Therefore the combination of FDS data and 3D rendering technology makes a perfect tool for simulating accurate and realistic smoke. ...
... In FDS, the continuous time is divided into several time segments, and the smoke density of each grid is obtained in any time segment [12]. In smoke simulation, a 3D grid voxel that is volume element used in 3D rendering [20] and is created in the VR platform following FDS grid. The opacity value of a voxel can be obtained by the normalized soot density values in the corresponding grid, which is used to accurately represent the low visibility caused by smoke. ...
... In volume visualization [95], for example, large volumetric data sets need to be processed and rendered. Direct volume visualization techniques [47,190,176] use the texture acceleration capabilities of early computer graphics hardware to achieve interactive frame rates. ...
Räumlich-zeitliche Daten sind Daten, welche sowohl einen Raum- als auch einen Zeitbezug aufweisen. So können beispielsweise Zeitreihen von Geodaten, thematische Karten die sich über die Zeit verändern, oder Bewegungsaufzeichnungen von sich bewegenden Objekten als räumlich-zeitliche Daten aufgefasst werden. In der heutigen automatisierten Welt gibt es eine wachsende Anzahl von Datenquellen, die beständig räumlich-zeitliche Daten generieren. Hierzu gehören beispielsweise Verkehrsüberwachungssysteme, die Bewegungsdaten von Menschen oder Fahrzeugen aufzeichnen, Fernerkundungssysteme, welche regelmäßig unsere Umgebung scannen und digitale Abbilder wie z.B. Stadt- und Landschaftsmodelle erzeugen, sowie Sensornetzwerke in unterschiedlichsten Anwendungsgebieten, wie z.B. der Logistik, der Verhaltensforschung von Tieren, oder der Klimaforschung. Zur Analyse räumlich-zeitlicher Daten werden neben der automatischen Analyse mittels statistischer Methoden und Data-Mining auch explorative Methoden angewendet, welche auf der interaktiven Visualisierung der Daten beruhen. Diese Methode der Analyse basiert darauf, dass Anwender in Form interaktiver Visualisierung die Daten explorieren können, wodurch die menschliche Wahrnehmung sowie das Wissen der User genutzt werden, um Muster zu erkennen und dadurch einen Einblick in die Daten zu erlangen. Diese Arbeit beschreibt ein Software-Framework für die Visualisierung räumlich-zeitlicher Daten, welches GPU-basierte Techniken beinhaltet, um eine interaktive Visualisierung und Exploration großer räumlich-zeitlicher Datensätze zu ermöglichen. Die entwickelten Techniken umfassen Datenhaltung, Prozessierung und Rendering und ermöglichen es, große Datenmengen in Echtzeit zu prozessieren und zu visualisieren. Die Hauptbeiträge der Arbeit umfassen: - Konzept und Implementierung einer GPU-zentrierten Visualisierungspipeline. Die beschriebenen Techniken basieren auf dem Konzept einer GPU-zentrierten Visualisierungspipeline, in welcher alle Stufen -- Prozessierung,Mapping, Rendering -- auf der GPU ausgeführt werden. Bei diesem Konzept werden die räumlich-zeitlichen Daten direkt im GPU-Speicher abgelegt. Während des Rendering-Prozesses werden dann mittels Shader-Programmen die Daten prozessiert, gefiltert, ein Mapping auf visuelle Attribute vorgenommen, und schließlich die Geometrien für die Visualisierung erzeugt. Datenprozessierung, Filtering und Mapping können daher in Echtzeit ausgeführt werden. Dies ermöglicht es Usern, die Mapping-Parameter sowie den gesamten Visualisierungsprozess interaktiv zu steuern und zu kontrollieren. - Interaktive Visualisierung attributierter 3D-Trajektorien. Es wurde eine Visualisierungsmethode für die interaktive Exploration einer großen Anzahl von 3D Bewegungstrajektorien entwickelt. Die Trajektorien werden dabei innerhalb einer virtuellen geographischen Umgebung in Form von einfachen Geometrien, wie Linien, Bändern, Kugeln oder Röhren dargestellt. Durch interaktives Mapping können Attributwerte der Trajektorien oder einzelner Messpunkte auf visuelle Eigenschaften abgebildet werden. Hierzu stehen Form, Höhe, Größe, Farbe, Textur, sowie Animation zur Verfügung. Mithilfe dieses dynamischen Mappings wurden außerdem verschiedene Visualisierungsmethoden implementiert, wie z.B. eine Focus+Context-Visualisierung von Trajektorien mithilfe von interaktiven Dichtekarten, sowie einer Space-Time-Cube-Visualisierung zur Darstellung des zeitlichen Ablaufs einzelner Bewegungen. - Interaktive Visualisierung geographischer Netzwerke. Es wurde eine Visualisierungsmethode zur interaktiven Exploration geo-referenzierter Netzwerke entwickelt, welche die Visualisierung von Netzwerken mit einer großen Anzahl von Knoten und Kanten ermöglicht. Um die Analyse von Netzwerken verschiedener Größen und in unterschiedlichen Kontexten zu ermöglichen, stehen mehrere virtuelle geographische Umgebungen zur Verfügung, wie bspw. ein virtueller 3D-Globus, als auch 2D-Karten mit unterschiedlichen geographischen Projektionen. Zur interaktiven Analyse dieser Netzwerke stehen interaktive Tools wie Filterung, Mapping und Selektion zur Verfügung. Des weiteren wurden Visualisierungsmethoden für verschiedene Arten von Netzwerken, wie z.B. 3D-Netzwerke und zeitlich veränderliche Netzwerke, implementiert. Zur Demonstration des Konzeptes wurden interaktive Tools für zwei unterschiedliche Anwendungsfälle entwickelt. Das erste beinhaltet die Visualisierung attributierter 3D-Trajektorien, welche die Bewegungen von Flugzeugen um einen Flughafen beschreiben. Es ermöglicht Nutzern, die Trajektorien von ankommenden und startenden Flugzeugen über den Zeitraum eines Monats interaktiv zu explorieren und zu analysieren. Durch Verwendung der interaktiven Visualisierungsmethoden für 3D-Trajektorien und interaktiven Dichtekarten können Einblicke in die Daten gewonnen werden, wie beispielsweise häufig genutzte Flugkorridore, typische sowie untypische Bewegungsmuster, oder ungewöhnliche Vorkommnisse wie Fehlanflüge. Der zweite Anwendungsfall beinhaltet die Visualisierung von Klimanetzwerken, welche geographischen Netzwerken in der Klimaforschung darstellen. Klimanetzwerke repräsentieren die Dynamiken im Klimasystem durch eine Netzwerkstruktur, die die statistische Beziehungen zwischen Orten beschreiben. Das entwickelte Tool ermöglicht es Analysten, diese großen Netzwerke interaktiv zu explorieren und dadurch die Struktur des Netzwerks zu analysieren und mit den geographischen Daten in Beziehung zu setzen. Interaktive Filterung und Selektion ermöglichen es, Muster in den Daten zu identifizieren, und so bspw. Cluster in der Netzwerkstruktur oder Strömungsmuster zu erkennen.
... Voxels, or volumetric pixels, were first developed for medical imagery (Kaufman, 1990;Levoy, 1988). The 3-D space is divided into voxels (1 cm 3 ), which either contain points representing laser returns (1) or are empty (0). ...
Sagebrush (Artemisia tridentata), a dominant shrub species in the sagebrush-steppe ecosystem of the western US, is declining from its historical distribution due to feedbacks between climate and land use change, fire, and invasive species. Quantifying aboveground biomass of sagebrush is important for assessing carbon storage and monitoring the presence and distribution of this rapidly changing dryland ecosystem. Models of shrub canopy volume, derived from terrestrial laser scanning (TLS) point clouds, were used to accurately estimate aboveground sagebrush biomass. Ninety-one sagebrush plants were scanned and sampled across three study sites in the Great Basin, USA. Half of the plants were scanned and destructively sampled in the spring (n = 46), while the other half were scanned again in the fall before destructive sampling (n = 45). The latter set of sagebrush plants was scanned during both spring and fall to further test the ability of the TLS to quantify seasonal changes in green biomass. Sagebrush biomass was estimated using both a voxel and a 3-D convex hull approach applied to TLS point cloud data. The 3-D convex hull model estimated total and green biomass more accurately (R2 = 0.92 and R2 = 0.83, respectively) than the voxel-based method (R2 = 0.86 and R2 = 0.73, respectively). Seasonal differences in TLS-predicted green biomass were detected at two of the sites (p < 0.001 and p = 0.029), elucidating the amount of ephemeral leaf loss in the face of summer drought. The methods presented herein are directly transferable to other dryland shrubs, and implementation of the convex hull model with similar sagebrush species is straightforward.
