000 04086nam a22005535i 4500
001 978-3-031-02604-1
003 DE-He213
005 20240730164012.0
007 cr nn 008mamaa
008 220601s2017 sz | s |||| 0|eng d
020 _a9783031026041
_9978-3-031-02604-1
024 7 _a10.1007/978-3-031-02604-1
_2doi
050 4 _aQA76.9.I52
072 7 _aUYZF
_2bicssc
072 7 _aMAT013000
_2bisacsh
072 7 _aUYZF
_2thema
082 0 4 _a001.4226
_223
100 1 _aFalk, Martin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981594
245 1 0 _aInteractive GPU-based Visualization of Large Dynamic Particle Data
_h[electronic resource] /
_cby Martin Falk, Sebastian Grottel, Michael Krone, Guido Reina.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXII, 109 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Visualization,
_x2159-5178
505 0 _aAcknowledgments -- Figure Credits -- Introduction -- History -- GPU-based Glyph Ray Casting -- Acceleration Strategies -- Data Structures -- Efficient Nearest Neighbor Search on the GPU -- Improved Visual Quality -- Application-driven Abstractions -- Summary and Outlook -- Bibliography -- Authors' Biographies.
520 _aPrevalent types of data in scientific visualization are volumetric data, vector field data, and particle-based data. Particle data typically originates from measurements and simulations in various fields, such as life sciences or physics. The particles are often visualized directly, that is, by simple representants like spheres. Interactive rendering facilitates the exploration and visual analysis of the data. With increasing data set sizes in terms of particle numbers, interactive high-quality visualization is a challenging task. This is especially true for dynamic data or abstract representations that are based on the raw particle data. This book covers direct particle visualization using simple glyphs as well as abstractions that are application-driven such as clustering and aggregation. It targets visualization researchers and developers who are interested in visualization techniques for large, dynamic particle-based data. Its explanations focus on GPU-accelerated algorithms for high-performance rendering and data processing that run in real-time on modern desktop hardware. Consequently, the implementation of said algorithms and the required data structures to make use of the capabilities of modern graphics APIs are discussed in detail. Furthermore, it covers GPU-accelerated methods for the generation of application-dependent abstract representations. This includes various representations commonly used in application areas such as structural biology, systems biology, thermodynamics, and astrophysics.
650 0 _aInformation visualization.
_914255
650 0 _aData structures (Computer science).
_98188
650 0 _aInformation theory.
_914256
650 0 _aData mining.
_93907
650 1 4 _aData and Information Visualization.
_933848
650 2 4 _aData Structures and Information Theory.
_931923
650 2 4 _aData Mining and Knowledge Discovery.
_981595
700 1 _aGrottel, Sebastian.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981596
700 1 _aKrone, Michael.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981597
700 1 _aReina, Guido.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981598
710 2 _aSpringerLink (Online service)
_981599
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031014765
776 0 8 _iPrinted edition:
_z9783031037320
830 0 _aSynthesis Lectures on Visualization,
_x2159-5178
_981600
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02604-1
912 _aZDB-2-SXSC
942 _cEBK
999 _c85206
_d85206