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TMM
2011
169views more  TMM 2011»
15 years 1 months ago
Empowering Visual Categorization With the GPU
—Visual categorization is important to manage large collections of digital images and video, where textual metadata is often incomplete or simply unavailable. The bag-of-words mo...
Koen E. A. van de Sande, Theo Gevers, Cees G. M. S...
ICASSP
2011
IEEE
14 years 10 months ago
Online Kernel SVM for real-time fMRI brain state prediction
The Support Vector Machine (SVM) methodology is an effective, supervised, machine learning method that gives stateof-the-art performance for brain state classification from funct...
Yongxin Taylor Xi, Hao Xu, Ray Lee, Peter J. Ramad...
ICASSP
2011
IEEE
14 years 10 months ago
Using the kernel trick in compressive sensing: Accurate signal recovery from fewer measurements
Compressive sensing accurately reconstructs a signal that is sparse in some basis from measurements, generally consisting of the signal’s inner products with Gaussian random vec...
Hanchao Qi, Shannon Hughes

Book
5396views
17 years 5 months ago
Markov Random Field Modeling in Computer Vision
Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms sy...
Stan Z. Li
CVPR
2003
IEEE
16 years 8 months ago
Resolution vs. tracking error: zoom as a gain controller
During tracking, lens zoom acts as a gain between scene dynamics and fixation errors, providing a trade-off between maximising resolution and minimising tracking error. Using a li...
Ben Tordoff, David W. Murray
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