Visualization with stylized line primitives

Gumhold, Stefan; Seidel, Hans‐Peter

doi:10.1109/vis.2005.124

Cited by 33 publications

(34 citation statements)

References 14 publications

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“…2(a)). Particularly related to our work are scalable, self-orienting surface techniques [23,24,33,34] which create shaded, view-aligned strips to visualize 3D vector fields. In contrast, our goal is to create high-resolution black-and-white visualizations for dense datasets using illustration principles.…”

Section: Line Data Visualization Techniquesmentioning

confidence: 99%

Depth-Dependent Halos: Illustrative Rendering of Dense Line Data

Everts

Bekker

Roerdink

et al. 2009

IEEE Trans. Visual. Comput. Graphics

109

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Abstract-We present a technique for the illustrative rendering of 3D line data at interactive frame rates. We create depth-dependent halos around lines to emphasize tight line bundles while less structured lines are de-emphasized. Moreover, the depth-dependent halos combined with depth cueing via line width attenuation increase depth perception, extending techniques from sparse line rendering to the illustrative visualization of dense line data. We demonstrate how the technique can be used, in particular, for illustrating DTI fiber tracts but also show examples from gas and fluid flow simulations and mathematics as well as describe how the technique extends to point data. We report on an informal evaluation of the illustrative DTI fiber tract visualizations with domain experts in neurosurgery and tractography who commented positively about the results and suggested a number of directions for future work.

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Section: Line Data Visualization Techniquesmentioning

confidence: 99%

Depth-Dependent Halos: Illustrative Rendering of Dense Line Data

Everts

Bekker

Roerdink

et al. 2009

IEEE Trans. Visual. Comput. Graphics

109

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“…Oriented imposter methods such as stream lines are very good at representing highly organized fibers, such as those found in Diffusion Tensor Imaging [30,32,26]. These techniques have little applicability, however, in complex networks that have highly uncorrelated orientations as are found in microvasculature.…”

Section: Previous Workmentioning

confidence: 99%

Visualization of Cellular and Microvascular Relationships

Mayerich

Abbott

Keyser

2008

IEEE Trans. Visual. Comput. Graphics

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Abstract-Understanding the structure of microvasculature structures and their relationship to cells in biological tissue is an important and complex problem. Brain microvasculature in particular is known to play an important role in chronic diseases. However, these networks are only visible at the microscopic level and can span large volumes of tissue. Due to recent advances in microscopy, large volumes of data can be imaged at the resolution necessary to reconstruct these structures. Due to the dense and complex nature of microscopy data sets, it is important to limit the amount of information displayed. In this paper, we describe methods for encoding the unique structure of microvascular data, allowing researchers to selectively explore microvascular anatomy. We also identify the queries most useful to researchers studying microvascular and cellular relationships. By associating cellular structures with our microvascular framework, we allow researchers to explore interesting anatomical relationships in dense and complex data sets.

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“…Interrante and Grosch (4) used a visibility-impeding 3D volumetric halo function to highlight the locations and strengths of depth discontinuities. Stoll et al (14) presented an algorithm to visualize stylized line primitives represented as generalized cylinders. Shadow effect is implemented in their algorithm to improve the depth perception.…”

Section: Journal Of Fluid Science and Technologymentioning

confidence: 99%

A Volume Rendering Framework for Visualizing 3D Flow Fields

Hsieh

Shen

et al. 2008

JFST

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In this paper, we present a volume rendering framework for visualizing 3D flow fields. We introduce the concept of coherence field which evaluates the representativeness of a given streamline set for the underlying 3D vector field. Visualization of the coherence field can provide effective visual feedback to the user for incremental insertion of more streamline seeds. Given an initial set of streamlines, a coherence volume is constructed from a distance field to measure the similarity between the existing streamlines and those in their nearby regions based on the difference between the approximate and the actual vector directions. With the visual feedback obtained from rendering the coherence volume, new streamline seeds can be selected by the user or by a heuristic seed selection algorithm to adaptively improve the coherence volume. An improved volume rendering technique that can render user-defined appearance textures is proposed to facilitate macro-visualization of 3D vector fields.

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Visualization with stylized line primitives

Cited by 33 publications

References 14 publications

Depth-Dependent Halos: Illustrative Rendering of Dense Line Data

Depth-Dependent Halos: Illustrative Rendering of Dense Line Data

Visualization of Cellular and Microvascular Relationships

A Volume Rendering Framework for Visualizing 3D Flow Fields

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