3,264 research outputs found
A mathematical morphology based approach for vehicle detection in road tunnels
A novel approach to automatically detect vehicles in road tunnels is presented in this paper. Non-uniform and poor illumination conditions prevail in road tunnels making difficult to achieve robust vehicle detection. In order to cope with the illumination issues, we propose a local higher-order statistic filter to make the vehicle detection invariant to illumination changes, whereas a morphological-based background subtraction is used to generate a convex hull segmentation of the vehicles. An evaluation test comparing our approach with a benchmark object detector shows that our approach outperforms in terms of false detection rate and overlap area detection
Bayesian multi-modal model comparison: a case study on the generators of the spike and the wave in generalized spikeâwave complexes
We present a novel approach to assess the networks involved in the generation of spontaneous pathological brain activity based on multi-modal imaging data. We propose to use probabilistic fMRI-constrained EEG source reconstruction as a complement to EEG-correlated fMRI analysis to disambiguate between networks that co-occur at the fMRI time resolution. The method is based on Bayesian model comparison, where the different models correspond to different combinations of fMRI-activated (or deactivated) cortical clusters. By computing the model evidence (or marginal likelihood) of each and every candidate source space partition, we can infer the most probable set of fMRI regions that has generated a given EEG scalp data window. We illustrate the method using EEG-correlated fMRI data acquired in a patient with ictal generalized spikeâwave (GSW) discharges, to examine whether different networks are involved in the generation of the spike and the wave components, respectively. To this effect, we compared a family of 128 EEG source models, based on the combinations of seven regions haemodynamically involved (deactivated) during a prolonged ictal GSW discharge, namely: bilateral precuneus, bilateral medial frontal gyrus, bilateral middle temporal gyrus, and right cuneus. Bayesian model comparison has revealed the most likely model associated with the spike component to consist of a prefrontal region and bilateral temporalâparietal regions and the most likely model associated with the wave component to comprise the same temporalâparietal regions only. The result supports the hypothesis of different neurophysiological mechanisms underlying the generation of the spike versus wave components of GSW discharges
Acoustic echo and noise canceller for personal hands-free video IP phone
This paper presents implementation and evaluation of a proposed acoustic echo and noise canceller (AENC) for videotelephony-enabled personal hands-free Internet protocol (IP) phones. This canceller has the following features: noise-robust performance, low processing delay, and low computational complexity. The AENC employs an adaptive digital filter (ADF) and noise reduction (NR) methods that can effectively eliminate undesired acoustic echo and background noise included in a microphone signal even in a noisy environment. The ADF method uses the step-size control approach according to the level of disturbance such as background noise; it can minimize the effect of disturbance in a noisy environment. The NR method estimates the noise level under an assumption that the noise amplitude spectrum is constant in a short period, which cannot be applied to the amplitude spectrum of speech. In addition, this paper presents the method for decreasing the computational complexity of the ADF process without increasing the processing delay to make the processing suitable for real-time implementation. The experimental results demonstrate that the proposed AENC suppresses echo and noise sufficiently in a noisy environment; thus, resulting in natural-sounding speech
Splitting method for elliptic equations with line sources
In this paper, we study the mathematical structure and numerical
approximation of elliptic problems posed in a (3D) domain when the
right-hand side is a (1D) line source . The analysis and approximation
of such problems is known to be non-standard as the line source causes the
solution to be singular. Our main result is a splitting theorem for the
solution; we show that the solution admits a split into an explicit, low
regularity term capturing the singularity, and a high-regularity correction
term being the solution of a suitable elliptic equation. The splitting
theorem states the mathematical structure of the solution; in particular, we
find that the solution has anisotropic regularity. More precisely, the solution
fails to belong to in the neighbourhood of , but exhibits
piecewise -regularity parallel to . The splitting theorem can
further be used to formulate a numerical method in which the solution is
approximated via its correction function . This approach has several
benefits. Firstly, it recasts the problem as a 3D elliptic problem with a 3D
right-hand side belonging to , a problem for which the discretizations and
solvers are readily available. Secondly, it makes the numerical approximation
independent of the discretization of ; thirdly, it improves the
approximation properties of the numerical method. We consider here the Galerkin
finite element method, and show that the singularity subtraction then recovers
optimal convergence rates on uniform meshes, i.e., without needing to refine
the mesh around each line segment. The numerical method presented in this paper
is therefore well-suited for applications involving a large number of line
segments. We illustrate this by treating a dataset (consisting of
line segments) describing the vascular system of the brain
- âŠ