Real-time quadtree analysis using HistoPyramids

Abstract

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.