Results 1 - 10 of 1,170

Low-complexity linear demosaicing using joint spatial-chromatic image statistics

by Javier Portilla - IEEE Int’l Conf on Image Processing , 2005
"... We present an efficient Linear Minimum Mean Square Error (LMMSE) method for reconstructing full color images from single sensor Color Filter Array (CFA) data. We use a representative set of full color images to estimate the joint spatial-chromatic covariance among pixel color components. Then, we de ..."
Abstract - Cited by 12 (0 self) - Add to MetaCart
We present an efficient Linear Minimum Mean Square Error (LMMSE) method for reconstructing full color images from single sensor Color Filter Array (CFA) data. We use a representative set of full color images to estimate the joint spatial-chromatic covariance among pixel color components. Then, we

Modeling the Spatial-Chromatic Distribution of Images

by Dong-woei Lin, Shih-hsuan Yang - IEEE International Conference on Multimedia and Expo (ICME 2004 , 2004
"... The conventional global color histogram bears only the composition information of colors. Further incorporating spatial information of colors helps identify a color image. However, joint spatial-chromatic relationship of real-world images has not been rigorously explored in the literature. In this p ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
The conventional global color histogram bears only the composition information of colors. Further incorporating spatial information of colors helps identify a color image. However, joint spatial-chromatic relationship of real-world images has not been rigorously explored in the literature

Regularized discriminant analysis

by Jerome H. Friedman - J. Amer. Statist. Assoc , 1989
"... Linear and quadratic discriminant analysis are considered in the small sample high-dimensional setting. Alternatives to the usual maximum likelihood (plug-in) estimates for the covariance matrices are proposed. These alternatives are characterized by two parameters, the values of which are customize ..."
Abstract - Cited by 468 (2 self) - Add to MetaCart
Linear and quadratic discriminant analysis are considered in the small sample high-dimensional setting. Alternatives to the usual maximum likelihood (plug-in) estimates for the covariance matrices are proposed. These alternatives are characterized by two parameters, the values of which

Neuronal Synchrony: A Versatile Code for the Definition of Relations?

by Wolf Singer , 1999
"... temporal relations requires the joint evaluation of responses from more than one neuron, only experiments that permit simultaneous measurements of responses 60528 Frankfurt from multiple units are considered. These include multi-Federal Republic of Germany electrode recordings from multiple individu ..."
Abstract - Cited by 470 (20 self) - Add to MetaCart
temporal relations requires the joint evaluation of responses from more than one neuron, only experiments that permit simultaneous measurements of responses 60528 Frankfurt from multiple units are considered. These include multi-Federal Republic of Germany electrode recordings from multiple

MODELING THE SPATIAL-CHROMATIC CHARACTERISTICS OF IMAGES BY NAKAGAMI-M DISTRIBUTION

by unknown authors
"... With the advent of the Internet and World Wide Web, ubiquitous multimedia information has reached every aspect of our daily lives. Multimedia data are, however, more difficult to access due to their rich and diversified interpretations. Content-based retrieval has been proposed to provide a user-fri ..."
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-friendly interface for many multimedia applications. For image retrieval, higher accuracy can be achieved by incorporating the spatial information into the classic color histogram. Unfortunately, complete description of the joint spatialchromatic feature space incurs formidable complexity of high dimensionality

A Blind Source Separation Technique Using Second Order Statistics

by Adel Belouchrani, Karim Abed-meraim, Jean-François Cardoso, Eric Moulines , 1997
"... Separation of sources consists in recovering a set of signals of which only instantaneous linear mixtures are observed. In many situations, no a priori information on the mixing matrix is available: the linear mixture should be `blindly' processed. This typically occurs in narrow-band array pro ..."
Abstract - Cited by 336 (9 self) - Add to MetaCart
, being based on a joint diagonalization of a set of covariance matrices. Asymptotic performance analysis of this method is carried out; some numerical simulations are provided to illustrate the effectiveness of the proposed method.

Front End Factor Analysis for Speaker Verification

by Najim Dehak, Patrick J. Kenny, Réda Dehak, Pierre Dumouchel, Pierre Ouellet - IEEE Transactions on Audio, Speech and Language Processing , 2010
"... Abstract—This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis. This space is named the total variability space ..."
Abstract - Cited by 315 (22 self) - Add to MetaCart
directly uses the cosine similarity as the final decision score. We tested three channel compensation techniques in the total variability space, which are within-class covariance normalization (WCCN), linear discriminate analysis (LDA), and nuisance attribute projection (NAP). We found that the best

Linear Regression Limit Theory for Nonstationary Panel Data

by Peter C. B. Phillips, Hyungsik R. Moon - ECONOMETRICA , 1999
"... This paper develops a regression limit theory for nonstationary panel data with large numbers of cross section Ž n. and time series Ž T. observations. The limit theory allows for both sequential limits, wherein T� � followed by n��, and joint limits where T, n�� simultaneously; and the relationship ..."
Abstract - Cited by 312 (22 self) - Add to MetaCart
This paper develops a regression limit theory for nonstationary panel data with large numbers of cross section Ž n. and time series Ž T. observations. The limit theory allows for both sequential limits, wherein T� � followed by n��, and joint limits where T, n�� simultaneously; and the relationship

Sparse Gaussian processes using pseudo-inputs

by Edward Snelson, Zoubin Ghahramani - Advances in Neural Information Processing Systems 18 , 2006
"... We present a new Gaussian process (GP) regression model whose covariance is parameterized by the the locations of M pseudo-input points, which we learn by a gradient based optimization. We take M ≪ N, where N is the number of real data points, and hence obtain a sparse regression method which has O( ..."
Abstract - Cited by 229 (13 self) - Add to MetaCart
(M 2 N) training cost and O(M 2) prediction cost per test case. We also find hyperparameters of the covariance function in the same joint optimization. The method can be viewed as a Bayesian regression model with particular input dependent noise. The method turns out to be closely related to several

Covariance scaled sampling for monocular 3D body tracking

by Cristian Sminchisescu, Bill Triggs - CVPR , 2001
"... We present a method for recovering 3D human body motion from monocular video sequences using robust image matching, joint limits and non-self-intersection constraints, and a new sample-andrefine search strategy guided by rescaled cost-function covariances. Monocular 3D body tracking is challenging: ..."
Abstract - Cited by 156 (3 self) - Add to MetaCart
We present a method for recovering 3D human body motion from monocular video sequences using robust image matching, joint limits and non-self-intersection constraints, and a new sample-andrefine search strategy guided by rescaled cost-function covariances. Monocular 3D body tracking is challenging
Results 1 - 10 of 1,170