Publications
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Classifying electrocardiogram peaks using new wavelet domain features Ghent University
We study distinctive properties of normal and malfunction electrocardiogram (ECG) peaks in the wavelet domain and based on this study we propose novel classification features for ECG signals. We analyze different combinations of the proposed wavelet domain and time domain features using multidimensional clustering and dimensionality reduction techniques. The results indicate encouraging accuracy rates.
Passive error concealment for wavelet coded images with efficient reconstruction of high-frequency content Ghent University
Real time image and video communication in packet switched networks suffers severely from packet loss. When using a dispersive packetization, i.e., spreading neighboring pixel data or coefficients over different packets, the lost data can be estimated from its correctly received neighbors. This paper presents a novel locally adaptive error concealment method for subband coded images. In the proposed method, we reconstruct a, lost coefficient ...
EM-based estimation of spatially variant correlated image noise Ghent University
In image denoising applications, noise is often correlated and the noise energy and correlation structure may even vary with the position in the image. Existing noise reduction and estimation methods are usually designed for stationary white Gaussian noise and generally work less efficient in this case because of the noise model mismatch. In this paper, we propose an EM algorithm for the estimation of spatially variant (nonstationary) correlated ...
Active Appearance Model (AAM) - From theory to implementation Ghent University
Active Appearance Model (AAM) is a kind of deformable shape descriptors which is widely used in computer vision and computer graphics. This approach utilizes statistical model obtained from some images in training set and gray-value information of the texture to fit on the boundaries of a new image. In this paper, we describe a brief implementation, apply the method on hand object and finally discuss its performance in compare to Active Shape ...
Object tracking using naive Bayesian classifiers Ghent University
This work presents a tracking algorithm based on a set of naive Bayesian classifiers. We consider tracking as a, classification problem and train a set of classifiers which distinguish a target object from the background around it. Classifiers' voting make a soft decision about class adherence for each pixel in video frame, forming a confidence map. We use the mean shift. algorithm to find the nearest peak in the confidence map, with respect to ...