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Abstract: A new approach to high-order-conditional probability density estimation is developed, based on a partitioning of conditioning space via decision trees. The technique is applied to image compression, image restoration, and texture synthesis, and the results compared with those obtained by standard mixture density and linear regression models. By applying the technique to subband-domain processing, some evidence is provided to support the following statement: the appropriate tradeoff between... (Update)
Context of citations to this paper: More
.... to use the Gaussian pyramid for its simplicity and greater spatial localization (a detailed discussion of this issue can be found in [19]) However, other kinds of pyramids can be used instead. Neighborhood: The neighborhood can have arbitrary size and shape; the only...
...L a = 4:2 L c a = 2:4 (b) L b = 5:2 L c b = 1:8 Figure 1: Average Joint(x# y) vs. Conditional (yjx) Likelihood Visualization Popat [6] describes a simple visualization example where 4 clusters must be fit with 2 Gaussian models as in Figure 1. Here, the model in (a) has a...
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BibTeX entry: (Update)
A C (K) Popat. Conjoint probabilistic Subband Modeling. PhD thesis, Massachusetts Institute of Technology, Program in Media Arts and Sciences, Cambridge, MA, September 1997. http://citeseer.ist.psu.edu/popat97conjoint.html More
@misc{ popat97conjoint,
author = "A. Popat",
title = "Conjoint probabilistic Subband Modeling",
text = "A C (K) Popat. Conjoint probabilistic Subband Modeling. PhD thesis, Massachusetts
Institute of Technology, Program in Media Arts and Sciences, Cambridge,
MA, September 1997.",
year = "1997",
url = "citeseer.ist.psu.edu/popat97conjoint.html" }
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