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An algorithm for converting rasters to quadtrees.

¹Department of Computer Science, University of Maryland, College Park, MD 20742.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

A new algorithm constructs quadtrees for binary images efficiently by processing row-by-row. This method ensures a minimal quadtree size after each pixel, outperforming traditional array-based approaches.

Area of Science:

Computer Science
Image Processing
Data Structures

Background:

Quadtrees are hierarchical data structures used for spatial partitioning.
Efficient construction of quadtrees for binary images is crucial for various applications.
Existing methods may require significant memory or processing time.

Purpose of the Study:

To present a novel algorithm for constructing quadtrees from row-by-row binary image data.
To optimize quadtree construction for space efficiency.
To enable real-time or near-real-time quadtree generation.

Main Methods:

The algorithm processes the binary image data one row at a time.
Identically colored child nodes (sons) in the quadtree are merged as early as possible.

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The construction integrates pixel processing with quadtree node merging.

Main Results:

A minimal size quadtree is maintained throughout the construction process.
The row-by-row processing approach leads to superior space efficiency compared to array-based methods.
The algorithm facilitates incremental quadtree updates.

Conclusions:

The presented algorithm offers an efficient and space-saving method for binary image quadtree construction.
This approach is advantageous for large images or memory-constrained environments.
The row-by-row processing strategy enhances the practicality of quadtree generation.