MISC

2017年

Attribute profiles from partitioning trees

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Petra Bosilj
  • ,
  • Bharath Bhushan Damodaran
  • ,
  • Erchan Aptoula
  • ,
  • Mauro Dalla Mura
  • ,
  • Sébastien Lefèvre

10225 LNCS
開始ページ
381
終了ページ
392
DOI
10.1007/978-3-319-57240-6_31

© Springer International Publishing AG 2017. Morphological attribute profiles are among the most prominent spatial-spectral pixel description tools. They can be calculated efficiently from tree based representations of an image. Although widely and successfully used with various inclusion trees (i.e., component trees and tree of shape), in this paper, we investigate their implementation through partitioning trees, and specifically α- and(ω)-trees. Our preliminary findings show that they are capable of comparable results to the state-of-the-art, while possessing additional properties rendering them suitable for the analysis of multivariate images.

リンク情報
DOI
https://doi.org/10.1007/978-3-319-57240-6_31
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85019266508&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85019266508&origin=inward
ID情報
  • DOI : 10.1007/978-3-319-57240-6_31
  • ISSN : 0302-9743
  • eISSN : 1611-3349
  • SCOPUS ID : 85019266508

エクスポート
BibTeX RIS