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CDC
2010
IEEE
101views Control Systems» more  CDC 2010»
10 years 8 months ago
Identification of mixed linear/nonlinear state-space models
The primary contribution of this paper is an algorithm capable of identifying parameters in certain mixed linear/nonlinear state-space models, containing conditionally linear Gauss...
Fredrik Lindsten, Thomas B. Schön
BMVC
2001
11 years 3 months ago
An EM-like Algorithm for Motion Segmentation via Eigendecomposition
This paper presents an iterative maximum likelihood framework for motion segmentation via the pairwise checking of pixel blocks. We commence from a characterisation of the motion ...
Antonio Robles-Kelly, Edwin R. Hancock
DAGM
1998
Springer
11 years 5 months ago
Discrete Mixture Models for Unsupervised Image Segmentation
This paper introduces a novel statistical mixture model for probabilistic clustering of histogram data and, more generally, for the analysis of discrete co occurrence data. Adoptin...
Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann
CAIP
1999
Springer
138views Image Analysis» more  CAIP 1999»
11 years 5 months ago
Procrustes Alignment with the EM Algorithm
This paper casts the problem of point-set alignment via Procrustes analysis into a maximum likelihood framework using the EM algorithm. The aim is to improve the robustness of the...
Bin Luo, Edwin R. Hancock
ECCV
2004
Springer
12 years 3 months ago
Learning Outdoor Color Classification from Just One Training Image
We present an algorithm for color classification with explicit illuminant estimation and compensation. A Gaussian classifier is trained with color samples from just one training im...
Roberto Manduchi
ICCV
2007
IEEE
12 years 3 months ago
pLSA for Sparse Arrays With Tsallis Pseudo-Additive Divergence: Noise Robustness and Algorithm
We introduce the Tsallis divergence error measure in the context of pLSA matrix and tensor decompositions showing much improved performance in the presence of noise. The focus of ...
Tamir Hazan, Roee Hardoon, Amnon Shashua
CVPR
2007
IEEE
12 years 3 months ago
Kernel-based Tracking from a Probabilistic Viewpoint
In this paper, we present a probabilistic formulation of kernel-based tracking methods based upon maximum likelihood estimation. To this end, we view the coordinates for the pixel...
Quang Anh Nguyen, Antonio Robles-Kelly, Chunhua Sh...
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