English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  DetailsSummary

Released
Report

GPU point list generation through histogram pyramids

MPS-Authors
/persons/resource/persons45805

Ziegler,  Gernot
Computer Graphics, MPI for Informatics, Max Planck Society;

/persons/resource/persons45604

Tevs,  Art
Computer Graphics, MPI for Informatics, Max Planck Society;

/persons/resource/persons45610

Theobalt,  Christian
Computer Graphics, MPI for Informatics, Max Planck Society;

/persons/resource/persons45449

Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

MPI-I-2006-4-002.pdf
(Any fulltext), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Ziegler, G., Tevs, A., Theobalt, C., & Seidel, H.-P.(2006). GPU point list generation through histogram pyramids (MPI-I-2006-4-002). Saarbrücken: Max-Planck-Institut für Informatik.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0014-680E-9
Abstract
Image Pyramids are frequently used in porting non-local algorithms to graphics hardware. A Histogram pyramid (short: HistoPyramid), a special version of image pyramid, sums up the number of active entries in a 2D image hierarchically. We show how a HistoPyramid can be utilized as an implicit indexing data structure, allowing us to convert a sparse matrix into a coordinate list of active cell entries (a point list) on graphics hardware . The algorithm reduces a highly sparse matrix with N elements to a list of its M active entries in O(N) + M (log N) steps, despite the restricted graphics hardware architecture. Applications are numerous, including feature detection, pixel classification and binning, conversion of 3D volumes to particle clouds and sparse matrix compression.