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» Comparing distributions and shapes using the kernel distance
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CIKM
2003
Springer
15 years 2 months ago
Dimensionality reduction using magnitude and shape approximations
High dimensional data sets are encountered in many modern database applications. The usual approach is to construct a summary of the data set through a lossy compression technique...
Ümit Y. Ogras, Hakan Ferhatosmanoglu
CVPR
2003
IEEE
15 years 11 months ago
Practical Non-parametric Density Estimation on a Transformation Group for Vision
It is now common practice in machine vision to define the variability in an object's appearance in a factored manner, as a combination of shape and texture transformations. I...
Erik G. Miller, Christophe Chefd'Hotel
ICCV
2009
IEEE
1556views Computer Vision» more  ICCV 2009»
16 years 2 months ago
Kernel Methods for Weakly Supervised Mean Shift Clustering
Mean shift clustering is a powerful unsupervised data analysis technique which does not require prior knowledge of the number of clusters, and does not constrain the shape of th...
Oncel Tuzel, Fatih Porikli, Peter Meer
3DIM
2003
IEEE
15 years 2 months ago
Human Identification from Body Shape
In this paper, we investigate the utility of static anthropometric distances as a biometric for human identification. The 3D landmark data from the CAESAR database is used to form...
Afzal Godil, Patrick Grother, Sandy Ressler
CAE
2007
14 years 12 months ago
Stipple Placement using Distance in a Weighted Graph
We present a stipple placement method which provides extra emphasis to image features, especially edges. Our algorithm transforms an image into a regular graph, with edge weights ...
David Mould