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Probability Density Estimation Using Isocontours and Isosurfaces: Applications to Information-Theoretic Image Registration
March 2009 (vol. 31 no. 3)
pp. 475-491
ASCII Text
x 
 
Ajit Rajwade, Arunava Banerjee, Anand Rangarajan, "Probability Density Estimation Using Isocontours and Isosurfaces: Applications to Information-Theoretic Image Registration," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 3, pp. 475-491, March, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2008.97,
author = {Ajit Rajwade and Arunava Banerjee and Anand Rangarajan},
title = {Probability Density Estimation Using Isocontours and Isosurfaces: Applications to Information-Theoretic Image Registration},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {31},
number = {3},
issn = {0162-8828},
year = {2009},
pages = {475-491},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.97},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Probability Density Estimation Using Isocontours and Isosurfaces: Applications to Information-Theoretic Image Registration
IS - 3
SN - 0162-8828
SP475
EP491
EPD - 475-491
A1 - Ajit Rajwade,
A1 - Arunava Banerjee,
A1 - Anand Rangarajan,
PY - 2009
KW - computer vision
KW - pattern recognition
VL - 31
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Ajit Rajwade, University of Florida, Gainesville
Arunava Banerjee, Univeristy of Florida, Gainesville
Anand Rangarajan, University of Florida, Gainesville
We present a new, geometric approach for determining the probability density of the intensity values in an image. We drop the notion of an image as a set of discrete pixels, and assume a piecewise-continuous representation. The probability density can then be regarded as being proportional to the area between two nearby isocontours of the image surface. Our paper extends this idea to joint densities of image pairs. We demonstrate the application of our method to affine registration between two or more images using information theoretic measures such as mutual information. We show cases where our method outperforms existing methods such as simple histograms, histograms with partial volume interpolation, Parzen windows, etc. under fine intensity quantization for affine image registration under significant image noise. Furthermore, we demonstrate results on simultaneous registration of multiple images, as well as for pairs of volume datasets, and show some theoretical properties of our density estimator. Our approach requires the selection of only an image interpolant. The method neither requires any kind of kernel functions (as in Parzen windows) which are unrelated to the structure of the image in itself, nor does it rely on any form of sampling for density estimation.

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Index Terms:
computer vision, pattern recognition
Citation:
Ajit Rajwade, Arunava Banerjee, Anand Rangarajan, "Probability Density Estimation Using Isocontours and Isosurfaces: Applications to Information-Theoretic Image Registration," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 3, pp. 475-491, March 2009, doi:10.1109/TPAMI.2008.97
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