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Issue No.10 - October (2011 vol.17)
pp: 1487-1498
Jason Lawrence , University of Virginia, Charlottesville
Sean Arietta , University of Virginia, Charlottesville
Michael Kazhdan , Johns Hopkins University, Baltimore
Daniel Lepage , University of Virginia, Charlottesville
Colleen O'Hagan , University of Virginia, Charlottesville
We present a new technique for fusing together an arbitrary number of aligned images into a single color or intensity image. We approach this fusion problem from the context of Multidimensional Scaling (MDS) and describe an algorithm that preserves the relative distances between pairs of pixel values in the input (vectors of measurements) as perceived differences in a color image. The two main advantages of our approach over existing techniques are that it can incorporate user constraints into the mapping process and allows adaptively compressing or exaggerating features in the input in order to make better use of the output's limited dynamic range. We demonstrate these benefits by showing applications in various scientific domains and comparing our algorithm to previously proposed techniques.
Multidimensional images, visualization techniques, dimensionality reduction, multidimensional scaling, physical sciences and engineering, life and medical sciences.
Jason Lawrence, Sean Arietta, Michael Kazhdan, Daniel Lepage, Colleen O'Hagan, "A User-Assisted Approach to Visualizing Multidimensional Images", IEEE Transactions on Visualization & Computer Graphics, vol.17, no. 10, pp. 1487-1498, October 2011, doi:10.1109/TVCG.2010.229
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