Sentence examples for feature robustness from inspiring English sources

Behaviour-based architectures enable rapid response to environment changes and they feature robustness in real worlds.

Ahonen and Pietikäinen [44] introduced soft LBP (SLBP) to enhance the feature robustness in the sense that a small change in an input image causes only a small change in the feature extraction output by employing fuzzy theory.

In addition to speech enhancement and feature robustness techniques, MLLR [33], MAP [34] and PC-PMC model adaptation methods can be applied optionally on acoustic models to adapt the acoustic model parameters to speaker variations and environmental noises.

The feature robustness is affected by the fact that the head of the child is not perfectly circular, the area of rotation can be irregular due to occlusions from the hands of demonstrator guiding rotation, and time varying.

where p ^ i, j and p i, j) are the j th element of p ^ i and p(i) respectively, floor(x) denotes the largest integer less than or equal to x. Quantization of the feature values is not only necessary to reduce the data bits but also to increase the feature robustness against small disturbance.

Model-based approaches like PMC, MLLR and MAP, along with feature robustness methods such as CMS, PCA, RCC and VTLN, and speech enhancement methods like spectral subtraction and Wiener filtering, along with their modified versions, were diligently implemented and evaluated in the system.

This section addresses the performance of the proposed range-bin alignment technique in relation to important features: robustness against target scintillation, against clutter, and so forth.

This algorithm presents two relevant features: robustness that is achieved by readjusting measurement weights, and accuracy that is attained by considering measurement dependencies.

Together with the feature of robustness against multipath effect, the proposed MC code is helpful for urban, tunnel, even indoor and underground positioning.

Then, to achieve an automatic color segmentation suitable for robot control, we developed a technique based on a discriminant color feature with robustness capabilities with respect to intensity variations and specularities.

This special issue comprises nine articles covering a range of topics in signal processing methods for vocal source and acoustic feature extraction, robustness issues, novel applications of pattern recognition techniques, methods for detecting mental states and recognition of non-prototypical spontaneous and naturalistic emotion in speech.