Data structures for quadtree approximation and compression
Communications of the ACM
Optimal quadtree construction algorithms
Computer Vision, Graphics, and Image Processing
Applications of spatial data structures: Computer graphics, image processing, and GIS
Applications of spatial data structures: Computer graphics, image processing, and GIS
A fast quadtree normalization algorithm
Pattern Recognition Letters
Uncertainly measures of rough set prediction
Artificial Intelligence
Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
An effective way to represent quadtrees
Communications of the ACM
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Imprecision in Finite Resolution Spatial Data
Geoinformatica
Efficient computation and data structures for graphics.
Efficient computation and data structures for graphics.
Progressive Refinement of Raster Images
IEEE Transactions on Computers
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Granular computing is closely related to the depth of the detail of information with which we are presented, or choose to process. In spatial cognition and image processing such detail is given by the resolution of a picture. The quadtree representation of an image offers a quick look at the image at various stages of granularity, and successive quadtree representations can be used to represent change. For a given image, the choice of quadtree root node plays an important role in its quadtree representation and final data compression. The goal of this paper is to present a heuristic algorithm for finding a root node of a region quadtree, which is able to reduce the number of leaf nodes when compared with the standard quadtree decomposition. The empirical results indicate that the proposed algorithm improves the quadtree representation and data compression when compared to the traditional method.