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ACMACE
2008
ACM
14 years 3 days ago
Dimensionality reduced HRTFs: a comparative study
Dimensionality reduction is a statistical tool commonly used to map high-dimensional data into lower a dimensionality. The transformed data is typically more suitable for regressi...
Bill Kapralos, Nathan Mekuz, Agnieszka Kopinska, S...
CVPR
2008
IEEE
15 years 3 days ago
Coherent Laplacian 3-D protrusion segmentation
In this paper, an analysis of locally linear embedding (LLE) in the context of clustering is developed. As LLE conserves the local affine coordinates of points, shape protrusions ...
Fabio Cuzzolin, Diana Mateus, David Knossow, Edmon...
ICCV
2007
IEEE
15 years 1 days ago
Graph Based Discriminative Learning for Robust and Efficient Object Tracking
Object tracking is viewed as a two-class 'one-versusrest' classification problem, in which the sample distribution of the target is approximately Gaussian while the back...
Xiaoqin Zhang, Weiming Hu, Stephen J. Maybank, Xi ...
ICCV
2005
IEEE
15 years 7 hour ago
Deformation Invariant Image Matching
We propose a novel framework to build descriptors of local intensity that are invariant to general deformations. In this framework, an image is embedded as a 2D surface in 3D spac...
Haibin Ling, David W. Jacobs
ACMACE
2007
ACM
14 years 2 months ago
Application of dimensionality reduction techniques to HRTFS for interactive virtual environments
Fundamental to the generation of 3D audio is the HRTF processing of acoustical signals. Unfortunately, given the high dimensionality of HRTFs, incorporating them into dynamic/inte...
Bill Kapralos, Nathan Mekuz