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Kalpathi R. Subramanian, Bruce F. Naylor, "Converting Discrete Images to Partitioning Trees," IEEE Transactions on Visualization and Computer Graphics, vol. 3, no. 3, pp. 273288, JulySeptember, 1997.  
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@article{ 10.1109/2945.620493, author = {Kalpathi R. Subramanian and Bruce F. Naylor}, title = {Converting Discrete Images to Partitioning Trees}, journal ={IEEE Transactions on Visualization and Computer Graphics}, volume = {3}, number = {3}, issn = {10772626}, year = {1997}, pages = {273288}, doi = {http://doi.ieeecomputersociety.org/10.1109/2945.620493}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Visualization and Computer Graphics TI  Converting Discrete Images to Partitioning Trees IS  3 SN  10772626 SP273 EP288 EPD  273288 A1  Kalpathi R. Subramanian, A1  Bruce F. Naylor, PY  1997 KW  Partitioning trees KW  BSP trees KW  space partitioning KW  miltiresolution representations KW  image reconstruction KW  image coding KW  scientific visualization KW  MRI visualization. VL  3 JA  IEEE Transactions on Visualization and Computer Graphics ER   
Abstract—The discrete space representation of most scientific datasets (pixels, voxels, etc.), generated through instruments or by sampling continuously defined fields, while being simple, is also verbose and structureless. We propose the use of a particular spatial structure, the
The paper describes methods for constructing partitioning trees from a discrete image/volume data set. Discrete space operators developed for edge detection are used to locate discontinuities in the image from which lines/planes containing the discontinuities are fitted by using either the Hough transform or a hyperplane sort. A multiresolution representation can be generated by ordering the choice of hyperplanes by the magnitude of the discontinuities. Various approximations can be obtained by pruning the tree according to an error metric. The segmentation of the image into edgeless regions can yield significant data compression. A hierarchical encoding schema for both lossless and lossy encodings is described.
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