In this paper we attempt to address the problem of geometric multi-model fitting with resorting to a few weakly annotated (WA) data points, which has been sparsely studied so far. In weak annotating, most of the manual annotations are supposed to be correct yet inevitably mixed with incorrect ones. The WA data can be naturally obtained in an interactive way for specific tasks, for example, in the case of homography estimation, one can easily annotate points on the same plane/object with a single label by observing the image. Motivated by this, we propose a novel method to make full use of the WA data to boost the multimodel fitting performance. Specifically, a graph for model proposal sampling is first constructed using the WA data, given the prior that the WA data annotated with the same weak label has a high probability of being assigned to the same model. By incorporating this prior knowledge into the calculation of edge probabilities, vertices (i.e., data points) lie on/near the latent model are likely to connect together and further form a subset/cluster for effective proposals generation. With the proposals generated, the α-expansion is adopted for labeling, and our method in return updates the proposals. This works in an iterative way. Extensive experiments validate our method and show that the proposed method produces noticeably better results than state-of-theart techniques in most cases.
This paper considers the problem of controlling both the position and the attitude of a quad-rotor helicopter. The quad-rotor helicopter is described by a set of nonlinear equations and some parameters of the dynamics are subjected to uncertainties. This paper proposes a new input-output linearization method based on an adaptive law, and presents a new adaptive tracking controller with on-line parameter estimation to make the tracking error zero asymptotically for the position and the yaw angle. The design problem of the controller is reduced to that of a linear system, and the design method is simple and straightforward. Moreover, a disturbance sensitivity reduction problem is also solved by applying a standard H-infinity control method. A numerical simulation is performed to evaluate the effectiveness of the proposed controller.
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