Title :
Dynamic Quantization: Two Adaptive Data Structures for Multidimensional Spaces
Author :
Rourke, Joseph O. ; Sloan, Kenneth R., Jr.
Author_Institution :
Department of Electrical Engineering and Computer Science, The Johns Hopkins University, Baltimore, MD 21218.
fDate :
5/1/1984 12:00:00 AM
Abstract :
Two new data structures are defined for use in multidimensional histogramming. Their purpose is to cover a parameter space with a limited number of histogram bins so that fine precision is maintained where it is needed. The original motivation for these data structures was to implement Hough-like transforms in high-dimensional parameter spaces. The two data structures share the ability to adapt to distributions that change with time.
Keywords :
Computer architecture; Data structures; Extraterrestrial measurements; Histograms; Multidimensional systems; Pixel; Quantization; Space technology; Tree data structures; Voting; Accumulator arrays; Hough transform; dynamic data structures; dynamic quantization; hierarchical data structures; k-d trees; multidimensional data structures; multidimensional histograms; pyramids;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.1984.4767519
