There are various situations where image data is binary: character recognition, result of image segmentation etc. As a first contribution, we compare Gaussian based principal component analysis (PCA), which is often used to model images, and ”binary PCA ” which models the binary data more naturally using Bernoulli distributions. Furthermore, we address the problem of data alignment. Image data is often perturbed by some global transformations such as shifting, rotation, scaling etc. In such cases the data needs to be transformed to some canonical aligned form. As a second contribution, we extend the binary PCA to the ”transformation invariant mixture of binary PCAs ” which simultaneously corrects the data for a set of global transformations...
We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As ima...
Principal component analysis (PCA) computes a succinct data representation by converting the data to...
Classically, encoding of images by only a few, important components is done by the Principal Compone...
International audienceThere are various situations where image data is binary: character recognition...
This paper proposes an effective and robust method for image alignment and recovery on a set of line...
Submitted to the IEEE International Conference on Computer Vision, Kerkyra, Greece, Sept. 20-25, 199...
Fast training and testing procedures are crucial in biometrics recognition research. Conventional al...
We present an automatic technique for image alignment using a principal component analysis (PCA) tha...
Parameterized Appearance Models (PAMs) (e.g. eigen-tracking, active appearance models, morphable mod...
Binary image of individual n-dimensional object is an information source for ob-ject recognition. Th...
Motivation Genome-wide measurements of genetic and epigenetic alterations are generating more and mo...
The objectives of this research are to analyze and develop a modified Principal Component Analysis (...
Principal component analysis is a versatile statistical method for reducing a cases-by-variables da...
Principal components analysis (PCA) is a process of identifying image sequences in an effective way ...
Principal Component Analysis (PCA) has been successfully applied to construct linear models of shape...
We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As ima...
Principal component analysis (PCA) computes a succinct data representation by converting the data to...
Classically, encoding of images by only a few, important components is done by the Principal Compone...
International audienceThere are various situations where image data is binary: character recognition...
This paper proposes an effective and robust method for image alignment and recovery on a set of line...
Submitted to the IEEE International Conference on Computer Vision, Kerkyra, Greece, Sept. 20-25, 199...
Fast training and testing procedures are crucial in biometrics recognition research. Conventional al...
We present an automatic technique for image alignment using a principal component analysis (PCA) tha...
Parameterized Appearance Models (PAMs) (e.g. eigen-tracking, active appearance models, morphable mod...
Binary image of individual n-dimensional object is an information source for ob-ject recognition. Th...
Motivation Genome-wide measurements of genetic and epigenetic alterations are generating more and mo...
The objectives of this research are to analyze and develop a modified Principal Component Analysis (...
Principal component analysis is a versatile statistical method for reducing a cases-by-variables da...
Principal components analysis (PCA) is a process of identifying image sequences in an effective way ...
Principal Component Analysis (PCA) has been successfully applied to construct linear models of shape...
We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As ima...
Principal component analysis (PCA) computes a succinct data representation by converting the data to...
Classically, encoding of images by only a few, important components is done by the Principal Compone...