Face pose estimation is essential for interactive remote video communication as well as human-computer interaction. In the case of a vision system for video communication using multiple cameras, not only precise but also fast estimation is required for the switching control of the camera views. However, most of the methods based on facial landmarks are not fast enough due to the calculation cost for the detection and alignment of the landmarks. This paper proposes a straightforward method to directly estimate the face pose from input camera images, using multiple camera views and deep learning.
High-speed vision sensing becomes a driving factor in developing new methods for robotic manipulation. In this paper we present two such methods in order to realize high-performance manipulation. First, we present a dynamic compensation approach which aims to achieve simultaneously fast and accurate positioning under various (from system to external environment) uncertainties. Second, a high-speed motion strategy for manipulating flexible objects is introduced to address the issue of deformation uncertainties. Both methods rely on high-speed visual feedback and are model independent, which we believe is essential to ensure good flexibility in a wide range of applications. The high-speed visual feedback tracks the relative error between the working tool and the target in image coordinates, which implies that there is no need for accurate calibrations of the vision system. Tasks for validating these methods were implemented and experimental results were provided to illustrate the effectiveness of the proposed methods.
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