Topology Based Flow Analysis and Superposition Effects View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2007

AUTHORS

Julia Ebling , Alexander Wiebel , Christoph Garth , Gerik Scheuermann

ABSTRACT

Using topology for feature analysis in flow fields faces several problems. First of all, not all features can be detected using topology based methods. Second, while in flow feature analysis the user is interested in a quantification of feature parameters like position, size, shape, radial velocity and other parameters of feature models, many of these parameters can not be determined using topology based methods alone. Additionally, in some applications it is advantageous to regard the vector field as a superposition of several, possibly simple, features. As topology based methods are quite sensitive to superposition effects, their precision and usability is limited in these cases. In this paper, topology based analysis and visualization of flow fields is estimated and compared to other feature based approaches demonstrating these problems. More... »

PAGES

91-103

References to SciGraph publications

Book

TITLE

Topology-based Methods in Visualization

ISBN

978-3-540-70822-3
978-3-540-70823-0

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-70823-0_7

DOI

http://dx.doi.org/10.1007/978-3-540-70823-0_7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1013315485


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "familyName": "Ebling", 
        "givenName": "Julia", 
        "id": "sg:person.01270055053.30", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01270055053.30"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Wiebel", 
        "givenName": "Alexander", 
        "id": "sg:person.011416446133.23", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011416446133.23"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Garth", 
        "givenName": "Christoph", 
        "id": "sg:person.01245455273.74", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01245455273.74"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Scheuermann", 
        "givenName": "Gerik", 
        "id": "sg:person.0777577160.41", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0777577160.41"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-3-7091-6783-0_15", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003005518", 
          "https://doi.org/10.1007/978-3-7091-6783-0_15"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-7091-6783-0_15", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003005518", 
          "https://doi.org/10.1007/978-3-7091-6783-0_15"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ast.2004.01.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015495930"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2514/2.5323", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036294751"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1467-8659.2003.00723.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037915580"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-662-05105-4_6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053420179", 
          "https://doi.org/10.1007/978-3-662-05105-4_6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/s0022112095000462", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053768453"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tvcg.2003.1207439", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061812404"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tvcg.2005.54", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061812532"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/882262.882290", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063173540"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/visual.2004.54", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093314453"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/visual.2003.1250372", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094006991"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/vl.1996.545307", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095414430"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2007", 
    "datePublishedReg": "2007-01-01", 
    "description": "Using topology for feature analysis in flow fields faces several problems. First of all, not all features can be detected using topology based methods. Second, while in flow feature analysis the user is interested in a quantification of feature parameters like position, size, shape, radial velocity and other parameters of feature models, many of these parameters can not be determined using topology based methods alone. Additionally, in some applications it is advantageous to regard the vector field as a superposition of several, possibly simple, features. As topology based methods are quite sensitive to superposition effects, their precision and usability is limited in these cases. In this paper, topology based analysis and visualization of flow fields is estimated and compared to other feature based approaches demonstrating these problems.", 
    "editor": [
      {
        "familyName": "Hauser", 
        "givenName": "Helwig", 
        "type": "Person"
      }, 
      {
        "familyName": "Hagen", 
        "givenName": "Hans", 
        "type": "Person"
      }, 
      {
        "familyName": "Theisel", 
        "givenName": "Holger", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-540-70823-0_7", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-540-70822-3", 
        "978-3-540-70823-0"
      ], 
      "name": "Topology-based Methods in Visualization", 
      "type": "Book"
    }, 
    "name": "Topology Based Flow Analysis and Superposition Effects", 
    "pagination": "91-103", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1013315485"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-70823-0_7"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "3f6ea66409ad19b189944a013edc3c3e7a967ba35603e5e7ee175215e02c92b7"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-70823-0_7", 
      "https://app.dimensions.ai/details/publication/pub.1013315485"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-16T07:26", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000355_0000000355/records_52988_00000000.jsonl", 
    "type": "Chapter", 
    "url": "https://link.springer.com/10.1007%2F978-3-540-70823-0_7"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-70823-0_7'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-70823-0_7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-70823-0_7'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-70823-0_7'


 

This table displays all metadata directly associated to this object as RDF triples.

127 TRIPLES      23 PREDICATES      39 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-540-70823-0_7 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N31548820ce2d4701a77c8c949acd8801
4 schema:citation sg:pub.10.1007/978-3-662-05105-4_6
5 sg:pub.10.1007/978-3-7091-6783-0_15
6 https://doi.org/10.1016/j.ast.2004.01.001
7 https://doi.org/10.1017/s0022112095000462
8 https://doi.org/10.1109/tvcg.2003.1207439
9 https://doi.org/10.1109/tvcg.2005.54
10 https://doi.org/10.1109/visual.2003.1250372
11 https://doi.org/10.1109/visual.2004.54
12 https://doi.org/10.1109/vl.1996.545307
13 https://doi.org/10.1111/j.1467-8659.2003.00723.x
14 https://doi.org/10.1145/882262.882290
15 https://doi.org/10.2514/2.5323
16 schema:datePublished 2007
17 schema:datePublishedReg 2007-01-01
18 schema:description Using topology for feature analysis in flow fields faces several problems. First of all, not all features can be detected using topology based methods. Second, while in flow feature analysis the user is interested in a quantification of feature parameters like position, size, shape, radial velocity and other parameters of feature models, many of these parameters can not be determined using topology based methods alone. Additionally, in some applications it is advantageous to regard the vector field as a superposition of several, possibly simple, features. As topology based methods are quite sensitive to superposition effects, their precision and usability is limited in these cases. In this paper, topology based analysis and visualization of flow fields is estimated and compared to other feature based approaches demonstrating these problems.
19 schema:editor N0488342632b4413cbad27b80c351e838
20 schema:genre chapter
21 schema:inLanguage en
22 schema:isAccessibleForFree true
23 schema:isPartOf N836c53cdfd27452daac10fc07e1067b5
24 schema:name Topology Based Flow Analysis and Superposition Effects
25 schema:pagination 91-103
26 schema:productId N24060ae7650d4eec893ba95edfcf6e58
27 N3f7936f319344bdd970f52e37406527f
28 N8b0f3282ac234fd7be72db4a446dc418
29 schema:publisher N849f9b411c204da990a9e551aa7f3cf0
30 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013315485
31 https://doi.org/10.1007/978-3-540-70823-0_7
32 schema:sdDatePublished 2019-04-16T07:26
33 schema:sdLicense https://scigraph.springernature.com/explorer/license/
34 schema:sdPublisher Ne0bf57fc31944f1bb15d4b4abc826685
35 schema:url https://link.springer.com/10.1007%2F978-3-540-70823-0_7
36 sgo:license sg:explorer/license/
37 sgo:sdDataset chapters
38 rdf:type schema:Chapter
39 N0488342632b4413cbad27b80c351e838 rdf:first N90376fda3ca441499a381952f0af8c8c
40 rdf:rest N75827da4bfc14870a0c8c9abdfb6d1e4
41 N24060ae7650d4eec893ba95edfcf6e58 schema:name readcube_id
42 schema:value 3f6ea66409ad19b189944a013edc3c3e7a967ba35603e5e7ee175215e02c92b7
43 rdf:type schema:PropertyValue
44 N31548820ce2d4701a77c8c949acd8801 rdf:first sg:person.01270055053.30
45 rdf:rest Nf883cfdd29df497aa7f9b9a32427eaa3
46 N3f7936f319344bdd970f52e37406527f schema:name dimensions_id
47 schema:value pub.1013315485
48 rdf:type schema:PropertyValue
49 N4433cbf3291f47239766f17ea00b8d98 rdf:first sg:person.01245455273.74
50 rdf:rest N68d42810567740828e693ea8fb233a3d
51 N5bd3506a4b6c4841a6244c7f59386830 schema:familyName Hagen
52 schema:givenName Hans
53 rdf:type schema:Person
54 N68d42810567740828e693ea8fb233a3d rdf:first sg:person.0777577160.41
55 rdf:rest rdf:nil
56 N75827da4bfc14870a0c8c9abdfb6d1e4 rdf:first N5bd3506a4b6c4841a6244c7f59386830
57 rdf:rest Nf82a772ea91e420d8528b5d0a4b00bd1
58 N836c53cdfd27452daac10fc07e1067b5 schema:isbn 978-3-540-70822-3
59 978-3-540-70823-0
60 schema:name Topology-based Methods in Visualization
61 rdf:type schema:Book
62 N849f9b411c204da990a9e551aa7f3cf0 schema:location Berlin, Heidelberg
63 schema:name Springer Berlin Heidelberg
64 rdf:type schema:Organisation
65 N8b0f3282ac234fd7be72db4a446dc418 schema:name doi
66 schema:value 10.1007/978-3-540-70823-0_7
67 rdf:type schema:PropertyValue
68 N90376fda3ca441499a381952f0af8c8c schema:familyName Hauser
69 schema:givenName Helwig
70 rdf:type schema:Person
71 Nd785034e772d45f7bdcdb8b517563ec5 schema:familyName Theisel
72 schema:givenName Holger
73 rdf:type schema:Person
74 Ne0bf57fc31944f1bb15d4b4abc826685 schema:name Springer Nature - SN SciGraph project
75 rdf:type schema:Organization
76 Nf82a772ea91e420d8528b5d0a4b00bd1 rdf:first Nd785034e772d45f7bdcdb8b517563ec5
77 rdf:rest rdf:nil
78 Nf883cfdd29df497aa7f9b9a32427eaa3 rdf:first sg:person.011416446133.23
79 rdf:rest N4433cbf3291f47239766f17ea00b8d98
80 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
81 schema:name Information and Computing Sciences
82 rdf:type schema:DefinedTerm
83 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
84 schema:name Artificial Intelligence and Image Processing
85 rdf:type schema:DefinedTerm
86 sg:person.011416446133.23 schema:familyName Wiebel
87 schema:givenName Alexander
88 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011416446133.23
89 rdf:type schema:Person
90 sg:person.01245455273.74 schema:familyName Garth
91 schema:givenName Christoph
92 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01245455273.74
93 rdf:type schema:Person
94 sg:person.01270055053.30 schema:familyName Ebling
95 schema:givenName Julia
96 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01270055053.30
97 rdf:type schema:Person
98 sg:person.0777577160.41 schema:familyName Scheuermann
99 schema:givenName Gerik
100 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0777577160.41
101 rdf:type schema:Person
102 sg:pub.10.1007/978-3-662-05105-4_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053420179
103 https://doi.org/10.1007/978-3-662-05105-4_6
104 rdf:type schema:CreativeWork
105 sg:pub.10.1007/978-3-7091-6783-0_15 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003005518
106 https://doi.org/10.1007/978-3-7091-6783-0_15
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1016/j.ast.2004.01.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015495930
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1017/s0022112095000462 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053768453
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1109/tvcg.2003.1207439 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061812404
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1109/tvcg.2005.54 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061812532
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1109/visual.2003.1250372 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094006991
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1109/visual.2004.54 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093314453
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1109/vl.1996.545307 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095414430
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1111/j.1467-8659.2003.00723.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1037915580
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1145/882262.882290 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063173540
125 rdf:type schema:CreativeWork
126 https://doi.org/10.2514/2.5323 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036294751
127 rdf:type schema:CreativeWork
 




Preview window. Press ESC to close (or click here)


...