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» Two Dimensional Compressive Classifier for Sparse Images
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TMI
2008
136views more  TMI 2008»
15 years 2 months ago
Classification of fMRI Time Series in a Low-Dimensional Subspace With a Spatial Prior
We propose a new method for detecting activation in functional magnetic resonance imaging (fMRI) data. We project the fMRI time series on a low-dimensional subspace spanned by wave...
François G. Meyer, Xilin Shen
130
Voted
CVPR
2004
IEEE
16 years 4 months ago
Learning a Restricted Bayesian Network for Object Detection
Many classes of images have the characteristics of sparse structuring of statistical dependency and the presence of conditional independencies among various groups of variables. S...
Henry Schneiderman
JRTIP
2008
118views more  JRTIP 2008»
15 years 2 months ago
Custom parallel caching schemes for hardware-accelerated image compression
Abstract In an effort to achieve lower bandwidth requirements, video compression algorithms have become increasingly complex. Consequently, the deployment of these algorithms on Fi...
Su-Shin Ang, George A. Constantinides, Wayne Luk, ...
135
Voted
ICCV
2007
IEEE
16 years 4 months ago
High-Dimensional Feature Matching: Employing the Concept of Meaningful Nearest Neighbors
Matching of high-dimensional features using nearest neighbors search is an important part of image matching methods which are based on local invariant features. In this work we hi...
Dusan Omercevic, Ondrej Drbohlav, Ales Leonardis
130
Voted
ECCV
2010
Springer
15 years 1 months ago
Hybrid Compressive Sampling via a New Total Variation TVL1
Compressive sampling (CS) is aimed at acquiring a signal or image from data which is deemed insufficient by Nyquist/Shannon sampling theorem. Its main idea is to recover a signal ...
Xianbiao Shu, Narendra Ahuja