A fast AAM search algorithm based on canonical correlation analysis (CCA-AAM) is introduced. It efficiently models the dependency between texture residuals and model parameters dur...
Rene Donner, Michael Reiter, Georg Langs, Philipp ...
In this paper, we develop a new effective multiple kernel learning algorithm. First, we map the input data into m different feature spaces by m empirical kernels, where each genera...
An improved method for generalized constrained canonical correlation analysis (GCCANO) is proposed. In the original GCCANO, data matrices were first decomposed into the sum of sev...
Stimulus selectivity of sensory neurons is often characterized by estimating their receptive field properties such as orientation selectivity. Receptive fields are usually deriv...
Canonical correlation analysis (CCA) is a powerful tool for analyzing multi-dimensional paired data. However, CCA tends to perform poorly when the number of paired samples is limit...
In a study of crew interaction with the automatic flight control system of the Boeing 757/767 aircraft, we observed 60 flights and recorded every change in the aircraft control mo...
We address the problem of automatic interpretation of nonexaggerated human facial and body behaviours captured in video. We illustrate our approach by three examples. (1) We intro...
Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide complementary information about the brain function. We propose a novel scheme to examine asso...
Correlation is often used to measure the similarity between signals and is an important tool in signal and image processing. In some applications it is common that signals are cor...
Typically data acquired through imaging techniques such as functional magnetic resonance imaging (fMRI), structural MRI (sMRI), and electroencephalography (EEG) are analyzed separ...