Biomedical subjects

Truong Q Nguyen

Publications and source records attributed to Truong Q Nguyen.

3 recordsLinked to original sources

PubMed2006

Optimal temporal interpolation filter for motion-compensated frame rate up conversion.

Frame rate up conversion (FRUC) methods that employ motion have been proven to provide better image quality compared to nonmotion-based methods. While motion-based methods improve the quality of interpolation, artifacts are introduced in the presence of incorrect motion vectors. In this paper, we study the design problem of optimal temporal interpolation filter for motion-compensated FRUC (MC-FRUC). The optimal filter is obtained by minimizing the prediction error variance between the original frame and the interpolated frame. In FRUC applications, the original frame that is skipped is not available at the decoder, so models for the power spectral density of the original signal and prediction error are used to formulate the problem. The closed-form solution for the filter is obtained by Lagrange multipliers and statistical motion vector error modeling. The effect of motion vector errors on resulting optimal filters and prediction error is analyzed. The performance of the optimal filter is compared to nonadaptive temporal averaging filters by using two different motion vector reliability measures. The results confirm that to improve the quality of temporal interpolation in MC, the interpolation filter should be designed based on the reliability of motion vectors and the statistics of the MC prediction error.

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PubMed2006

Performance analysis of motion-compensated de-interlacing systems.

A lot of research has been conducted on motion-compensated (MC) de-interlacing, but there are very few publications that discuss the performances of de-interlacing quantatively. The various methods are compared through their performance on known video sequences. Linear system analysis of interlaced video and de-interlacer are proposed in. It is well established that the performance of the MC methods outperform the fixed or motion-adaptive methods when the motion vectors used are reliable and true to the scene content. Being an open-loop process the performance of the MC de-interlacers degrade drastically when there are motion vector errors. In this paper, a linear system analysis of MC video upconversion systems is presented and the effects of motion vector accuracy on system performance are analyzed. We investigate the various factors that contribute to the motion vector inaccuracy, such as incorrect motion modelling, acceleration between the frames, and insufficient interpolation kernel.

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PubMed2004

Gaussian mixture models of ECoG signal features for improved detection of epileptic seizures.

PURPOSE: To investigate the potential for improving the performance of the Osorio-Frei seizure detection algorithm (OFA) by incorporating multiple FIR filters operating in parallel and Gaussian mixture models (GMM) for ECoG features distributions, thus creating "hybrid" system. METHODS: The "hybrid" algorithm decomposes the signal into four subbands, using wavelets, after which relevant features are extracted for each subband. Following these steps, multivariate GMM are developed for seizure and non-seizure states, using training segments. State classification is based on thresholding of the likelihood ratio of seizure vs. non-seizure data. Multiple comparisons are performed between this "hybrid" and a modified version of the OFA suitable for this purpose, using as indices false positives (FP), false negatives (FN) and speed of detection. RESULTS: GMM improved speed of detection over the modified OFA at negligible FP levels. The average detection delay from expert visually placed electrographic onset over all seizures was reduced from 4.8 s for modified OFA to 1.8 s for GMM (p < 0.002) Individualized training by subject proved superior to group-based training. CONCLUSIONS: This work introduces multi-feature extraction from ECoG signals together with use of Gaussian mixtures to model them, as tools to improve automated seizure detection. At the clinical level, this approach appears to increase warning time and with it the window during which safety measures and seizure blockage may be implemented, at an affordable computational cost and with negligible FP rate.

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