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Conference Paper

Real-time Quadtree Analysis using HistoPyramids


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


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


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

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Ziegler, G., Dimitrov, R., Theobalt, C., & Seidel, H.-P. (2007). Real-time Quadtree Analysis using HistoPyramids. In N. Kehtarnavaz, & M. F. Carlsohn (Eds.), Real-Time Image Processing 2007 (pp. 1-11). Bellingham, WA, USA: SPIE.

Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-2076-6
Region quadtrees are convenient tools for hierarchical image analysis. Like the
related Haar wavelets, they are simple to generate within a fixed calculation
time. The clustering at each resolution level requires only local data, yet
they deliver intuitive classification results. Although the region quadtree
partitioning is very rigid, it can be rapidly computed from arbitrary imagery.
This research article demonstrates how graphics hardware can be utilized to
build region quadtrees at unprecedented speeds. To achieve this, a
data-structure called HistoPyramid registers the number of desired image
features in a pyramidal 2D array. Then, this HistoPyramid is used as an
implicit indexing data structure through quadtree traversal, creating lists of
the registered image features directly in GPU memory, and virtually eliminating
bus transfers between CPU and GPU. With this novel concept, quadtrees can be
applied in real-time video processing on standard PC hardware. A multitude of
applications in image and video processing arises, since region quadtree
analysis becomes a light-weight preprocessing step for feature clustering in
vision tasks, motion vector analysis, PDE calculations, or data compression. In
a sidenote, we outline how this algorithm can be applied to 3D volume data,
effectively generating region octrees purely on graphics hardware.