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» Estimating Vision Parameters given Data with Covariances
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ICPR
2000
IEEE
14 years 7 months ago
Reduction of Bias in Maximum Likelihood Ellipse Fitting
An improved maximum likelihood estimator for ellipse fitting based on the heteroscedastic errors-in-variables (HEIV) regression algorithm is proposed. The technique significantly ...
Bogdan Matei, Peter Meer
PR
2002
202views more  PR 2002»
13 years 6 months ago
Illumination color covariant locale-based visual object retrieval
Search by Object Model -- finding an object inside a target image -- is a desirable and yet difficult mechanism for querying multimedia data. An added difficulty is that objects c...
Mark S. Drew, Ze-Nian Li, Zinovi Tauber
BMVC
2002
13 years 9 months ago
A New Constrained Parameter Estimator: Experiments in Fundamental Matrix Computation
In recent work the authors proposed a wide-ranging method for estimating parameters that constrain image feature locations and satisfy a constraint not involving image data. The p...
Anton van den Hengel, Michael J. Brooks, Wojciech ...
NIPS
2008
13 years 8 months ago
Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform
Covariance estimation for high dimensional vectors is a classically difficult problem in statistical analysis and machine learning. In this paper, we propose a maximum likelihood ...
Guangzhi Cao, Charles A. Bouman
IJCV
2000
133views more  IJCV 2000»
13 years 6 months ago
Heteroscedastic Regression in Computer Vision: Problems with Bilinear Constraint
We present an algorithm to estimate the parameters of a linear model in the presence of heteroscedastic noise, i.e., each data point having a different covariance matrix. The algor...
Yoram Leedan, Peter Meer