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» Unsupervised scene analysis: A hidden Markov model approach
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ICCV
2005
IEEE
14 years 9 months ago
Modeling Scenes with Local Descriptors and Latent Aspects
We present a new approach to model visual scenes in image collections, based on local invariant features and probabilistic latent space models. Our formulation provides answers to...
Pedro Quelhas, Florent Monay, Jean-Marc Odobez, Da...
ICASSP
2011
IEEE
12 years 11 months ago
A study of an irrelevant variability normalization based discriminative training approach for LVCSR
This paper presents a discriminative training (DT) approach to irrelevant variability normalization (IVN) based training of feature transforms and hidden Markov models for large v...
Yu Zhang, Jian Xu, Zhi-Jie Yan, Qiang Huo
CVPR
2007
IEEE
14 years 9 months ago
Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis
This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model. Most work in this area has been focused on achieving accura...
Patrick Peursum, Svetha Venkatesh, Geoff A. W. Wes...
ECCV
2006
Springer
14 years 9 months ago
Scene Classification Via pLSA
Given a set of images of scenes containing multiple object categories (e.g. grass, roads, buildings) our objective is to discover these objects in each image in an unsupervised man...
Anna Bosch, Andrew Zisserman, Xavier Muñoz
CRV
2009
IEEE
237views Robotics» more  CRV 2009»
14 years 2 months ago
SEC: Stochastic Ensemble Consensus Approach to Unsupervised SAR Sea-Ice Segmentation
The use of synthetic aperture radar (SAR) has become an integral part of sea-ice monitoring and analysis in the polar regions. An important task in sea-ice analysis is to segment ...
Alexander Wong, David A. Clausi, Paul W. Fieguth