Sentence examples for features estimation from inspiring English sources

The system has three distinctive features: estimation of faults caused by design, extraction of faulty parts or faulty operations, and analysis of fault phenomena.

The novel techniques include statistical analysis, persistent histogram features estimation that allows for a consistent registration, resampling with additional robust fitting techniques, and segmentation of the environment into meaningful regions.

This is caused by the fact that it sets all CA coefficients but the detected support to zero while the classical method results in a CA that features estimation noise between the spikes.

In Section 6, experimental results regarding the accuracy of the visual features estimation from speech, the quality of the animations in terms of intelligibility of visual speech, and the computational complexity of the proposed algorithms, are presented.

In particular, results regarding the accuracy of visual feature estimation from speech are included.

Novel feature estimation methods are in the focus of this study.

Our feature estimation is expressed as a variational minimization process leading to a variational framework which is robust to noise.

Robust methods become necessary whenever the distribution of the extracted features is heavy tailed or contains outliers [26, 27] due to errors in the feature estimation step.

The ReliefF algorithm is one of the most popular algorithms to feature estimation and it has proved its usefulness in several domains.

Three-means or two-means classification is applied for classifying pixels with similar characteristics after feature estimation at the outputs of high frequency bands of spline wavelet transforms.

When rotated fast, such non-uniform point densities influence neighborhood searches which in turn may negatively affect local feature estimation and scan registration.