PUBLICATION DATE

2006-10-31

AUTHORS

Jonathan Dinerstein, Parris K. Egbert, David Cline

TITLE

Enhancing computer graphics through machine learning: a survey

ISSUE

1

VOLUME

23

ISSN (print)

0178-2789

ISSN (electronic)

1432-2315

ABSTRACT

Machine learning has experienced explosive growth in the last few decades, achieving sufficient maturity to provide effective tools for sundry scientific and engineering fields. Machine learning provides a firm theoretical foundation upon which to build techniques that leverage existing data to extract interesting information or to synthesize more data. In this paper we survey the uses of machine learning methods and concepts in recent computer graphics techniques. Many graphics techniques are data-driven; however, few graphics papers explicitly leverage the machine learning literature to underpin, validate, and develop their proposed methods. This survey provides novel insights by casting many existing computer graphics techniques into a common learning framework. This not only illuminates how these techniques are related, but also reveals possible ways in which they may be improved. We also use our analysis to propose several directions for future work.

How to use: Click on a object to move its position. Double click to open its homepage. Right click to preview its contents.

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


35 TRIPLES      30 PREDICATES      36 URIs      22 LITERALS

Subject Predicate Object
1 articles:f6dfb10633003b5a191ceafdccd4fc50 sg:abstract Abstract Machine learning has experienced explosive growth in the last few decades, achieving sufficient maturity to provide effective tools for sundry scientific and engineering fields. Machine learning provides a firm theoretical foundation upon which to build techniques that leverage existing data to extract interesting information or to synthesize more data. In this paper we survey the uses of machine learning methods and concepts in recent computer graphics techniques. Many graphics techniques are data-driven; however, few graphics papers explicitly leverage the machine learning literature to underpin, validate, and develop their proposed methods. This survey provides novel insights by casting many existing computer graphics techniques into a common learning framework. This not only illuminates how these techniques are related, but also reveals possible ways in which they may be improved. We also use our analysis to propose several directions for future work.
2 sg:articleType OriginalPaper
3 sg:coverYear 2007
4 sg:coverYearMonth 2007-01
5 sg:ddsId s00371-006-0085-4
6 sg:ddsIdJournalBrand 371
7 sg:doi 10.1007/s00371-006-0085-4
8 sg:doiLink http://dx.doi.org/10.1007/s00371-006-0085-4
9 sg:hasContributingOrganization grid-institutes:grid.253294.b
10 sg:hasContribution contributions:56477a71b6a36d12fc120c3579ebecb9
11 contributions:a44ac5c9c3f4ac9ff4baedc10a8c54f4
12 contributions:d74047bee3e997d9d521595604422754
13 sg:hasFieldOfResearchCode anzsrc-for:08
14 anzsrc-for:0801
15 sg:hasJournal journals:0bfe5f319cbbf5dd22652bc700eeebec
16 journals:7c1ef5f89f1526b194ec8820109a7cc6
17 sg:hasJournalBrand journal-brands:9d9ce62ec2519a0ed7fa32ea331affbe
18 sg:indexingDatabase Scopus
19 Web of Science
20 sg:issnElectronic 1432-2315
21 sg:issnPrint 0178-2789
22 sg:issue 1
23 sg:language English
24 sg:license http://scigraph.springernature.com/explorer/license/
25 sg:pageEnd 43
26 sg:pageStart 25
27 sg:publicationDate 2006-10-31
28 sg:publicationYear 2006
29 sg:publicationYearMonth 2006-10
30 sg:scigraphId f6dfb10633003b5a191ceafdccd4fc50
31 sg:title Enhancing computer graphics through machine learning: a survey
32 sg:volume 23
33 sg:webpage https://link.springer.com/10.1007/s00371-006-0085-4
34 rdf:type sg:Article
35 rdfs:label Article: Enhancing computer graphics through machine learning: a survey
HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular JSON format for linked data.

curl -H 'Accept: application/ld+json' 'http://scigraph.springernature.com/things/articles/f6dfb10633003b5a191ceafdccd4fc50'

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

curl -H 'Accept: application/n-triples' 'http://scigraph.springernature.com/things/articles/f6dfb10633003b5a191ceafdccd4fc50'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'http://scigraph.springernature.com/things/articles/f6dfb10633003b5a191ceafdccd4fc50'

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

curl -H 'Accept: application/rdf+xml' 'http://scigraph.springernature.com/things/articles/f6dfb10633003b5a191ceafdccd4fc50'






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


...