Boundary detection has long been a fundamental tool for image processing and computer vision, supporting the analysis of static time-varying data. In this work, we built upon theory Graph Signal Processing to propose novel boundary filter in context graphs, having as main application scenario visual spatio-temporal More specifically, equivalent graphs so-called Laplacian Gaussian edge filter, w...

This paper presents an adaptive differentiator of sequential data based on the adaptive control theory. The algorithm is applied to detect moving objects by estimating a temporal gradient of sequential data at a specified pixel. We adopt two nonlinear intensity functions to reduce the influence of noises. The derivatives of the nonlinear intensity functions are estimated by an adaptive observer...

In this paper we present and compare two different spatio-temporal decorrelation learning algorithms for updating the weights of a linear feedforward network with FIR synapses (MIMO FIR filter). Both standard gradient and the natural gradient are employed to derive the spatio-temporal decorrelation algorithms. These two algorithms are applied to multichannel blind deconvolution task and their p...

Magnetoencephalography (MEG) is a method to measure a magnetic field generated by electrical neural activity in a brain, and it plays increasingly important role in clinical diagnoses and neurophysiological studies. However, in MEG analysis, the estimation of the brain activity, of the electric current density distribution in a brain which is represented by current dipoles, is problematic. A sp...

In this paper, we consider a minimum mean-squared error (MMSE) based receiver structure which combines multiuser detection (temporal filtering) and receiver beamforming (spatial filtering). Motivated by the high complexity of the optimum receiver, we propose rank constrained temporal-spatial filters which are simpler and near optimum. An iterative algorithm based on alternating minimization is ...

e present a simplified dual-channel discrimination model with spatio-temporal filters to represent the t s visual system contrast sensitivity, and masking based on local spatio-temporal contrast energy. The contras ensitivity filter parameters of the model were based on previous work. The masking and global sensitivity h t parameters are calibrated to masking data using brief grating target sig...

This paper presents a probabilistic algorithm that extracts a mapping between two subspaces by representing each subspace as a collection of states. In many cases, the data is a time series with temporal constraints. This paper suggests a method to impose these temporal constraints on the transitions between the states of the subspace. This probabilistic model has been successfully applied to t...

INTRODUCTION A Kalman filter provides causal operation, which is desirable in several rapid imaging applications. A successful real-time implementation of a Kalman filter has recently been developed used with spiral trajectories for applications in dynamic cardiac MRI and has been shown to outperform an existing sliding window method [1,2]., However there are different ways to estimate paramete...