Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Meyer, Johannes | |
dc.contributor.author | Längle, Thomas | |
dc.contributor.author | Beyerer, Jürgen | |
dc.contributor.editor | Skala, Václav | |
dc.date.accessioned | 2018-05-21T08:39:15Z | - |
dc.date.available | 2018-05-21T08:39:15Z | - |
dc.date.issued | 2017 | |
dc.identifier.citation | WSCG '2017: short communications proceedings: The 25th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech RepublicMay 29 - June 2 2017, p. 147-152. | en |
dc.identifier.isbn | 978-80-86943-45-9 | |
dc.identifier.issn | 2464-4617 | |
dc.identifier.uri | wscg.zcu.cz/WSCG2017/!!_CSRN-2702.pdf | |
dc.identifier.uri | http://hdl.handle.net/11025/29745 | |
dc.description.abstract | Comparing two random vectors by calculating a distance measure between the underlying probability density functions is a key ingredient in many applications, especially in the domain of image processing. For this purpose, the recently introduced generalized Cramér-von Mises distance is an interesting choice, since it is well defined even for the multivariate and discrete case. Unfortunately, the naive way of computing this distance, e.g., for two discrete two-dimensional random vectors ˜x; ˜y 2 [0; : : : ;n1]2;n 2 N has a computational complexity of O(n5) that is impractical for most applications. This paper introduces fastGCVM, an algorithm that makes use of the well known concept of summed area tables and that allows to compute the generalized Cramér-von Mises distance with a computational complexity of O(n3) for the mentioned case. Two experiments demonstrate the achievable speed up and give an example for a practical application employing fastGCVM. | en |
dc.format | 6 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | Václav Skala - UNION Agency | en |
dc.relation.ispartofseries | WSCG '2017: short communications proceedings | en |
dc.rights | © Václav Skala - UNION Agency | cs |
dc.subject | vzdálenost náhodných vektorů | cs |
dc.subject | souhrnné tabulky oblastí | cs |
dc.subject | zrychlení | cs |
dc.subject | srovnání histogramu | cs |
dc.subject | lokalizovaná kumulativní distribuce | cs |
dc.subject | generalizovaná Cramér-von Misesova vzdálenost | cs |
dc.title | fastGCVM: a fast algorithm for the computation of the discrete generalized cramér-von mises distance | en |
dc.type | konferenční příspěvek | cs |
dc.type | conferenceObject | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.subject.translated | cistance of random vectors | en |
dc.subject.translated | summed area tables | en |
dc.subject.translated | speedup | en |
dc.subject.translated | histogram comparison | en |
dc.subject.translated | localized cumulative distributions | en |
dc.subject.translated | generalized Cramér-von Mises distance | en |
dc.type.status | Peer-reviewed | en |
Vyskytuje se v kolekcích: | WSCG '2017: Short Papers Proceedings |
Soubory připojené k záznamu:
Soubor | Popis | Velikost | Formát | |
---|---|---|---|---|
Meyer.pdf | Plný text | 1,11 MB | Adobe PDF | Zobrazit/otevřít |
Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam:
http://hdl.handle.net/11025/29745
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