Data classification by kernel density shape interpolation of...

Data processing: artificial intelligence – Knowledge processing system

Reexamination Certificate

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C706S062000

Reexamination Certificate

active

07542954

ABSTRACT:
A method for representing a dataset comprises clustering the dataset using an unsupervised, non-parametric clustering method to generate a set of clusters each comprising a set of data points in an image; clustering the data points of each cluster using a supervised, partitional clustering method to partition each cluster into a specified number of sub-clusters; generating a density estimate value of each grid point of a set of grid points sampled from the image at a specified resolution for each sub-cluster using a kernel density function; identifying a maximum density estimate value and a sub-cluster associated with the maximum density estimate value for the grid point; adding each grid point for which the maximum density estimate value exceeds a specified threshold to the sub-cluster associated with the maximum density estimate value; and, for each cluster, merging the sub-clusters of the cluster into a corresponding cluster region in the image.

REFERENCES:
patent: 5671294 (1997-09-01), Rogers et al.
patent: 2003/0147558 (2003-08-01), Loui et al.
patent: 2006/0217925 (2006-09-01), Taron et al.
patent: 2007/0003137 (2007-01-01), Cremers et al.

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