Sentence examples for robust pose from inspiring English sources

This paper proposes a robust pose classification framework based on dictionary-learning and sparse representation.

In this paper, we present a visual simultaneous localization and mapping (SLAM) system which integrates measurements from multiple cameras to achieve robust pose tracking for autonomous navigation of micro aerial vehicles (MAVs) in unknown complex environments.

These two measures enable robust pose estimation of the robots with subpixel precision, while the significant simplification of the image processing algorithms ensures tracking of many robots with very high framerates.

For this reason, many applications would benefit from automatic and robust head pose estimation systems.

Since face orientation is estimated, the method is robust against pose (i.e., yaw).

To construct a robust head pose estimator, we follow an appearance-based head pose estimation architecture instead of a model-based one.

There are many applications with accurate and robust head pose estimation algorithms, such as human-computing interfaces (HCI), driver surveillance systems, entertainment systems, and so on.

Since multiple instances of sub-shapes are initialized, the scheme is claimed to be robust against pose, illumination and expression artifacts.

Note that the system used to combine the three detectors can be extended to get a face detector robust to pose and to occlusion.

This approach is motivated by the fact that the non-ferrous materials present similar spectral properties and their robust classification pose a challenging research issue.

Existing methods [5, 6, 20] are known to perform using an arbitrary view, but they are not robust to pose variations.