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  • 1. Munoz-Salinas, Rafael
    et al.
    Yeguas-Bolivar, E.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Medina-Carnicer, R.
    Multi-camera head pose estimation2012In: Machine Vision and Applications, ISSN 0932-8092, E-ISSN 1432-1769, Vol. 23, no 3, p. 479-490Article in journal (Refereed)
    Abstract [en]

    Estimating people's head pose is an important problem, for which many solutions have been proposed. Most existing solutions are based on the use of a single camera and assume that the head is confined in a relatively small region of space. If we need to estimate unintrusively the head pose of persons in a large environment, however, we need to use several cameras to cover the monitored area. In this work, we propose a novel solution to the multi-camera head pose estimation problem that exploits the additional amount of information that provides multi-camera configurations. Our approach uses the probability estimates produced by multi-class support vector machines to calculate the probability distribution of the head pose. The distributions produced by the cameras are fused, resulting in a more precise estimate than the one provided individually. We report experimental results that confirm that the fused distribution provides higher accuracy than the individual classifiers and a high robustness against errors.

  • 2.
    Siddiqui, J. Rafid
    et al.
    Department of Computing, Blekinge Institute of Technology, Karlskrona, Sweden.
    Havaei, Mohammad
    Department of Computing, Blekinge Institute of Technology, Karlskrona, Sweden.
    Siamak, Khatibi
    Department of Computing, Blekinge Institute of Technology, Karlskrona, Sweden.
    Lindley, Craig A.
    Intelligent Sensing and Systems, Common-wealth Scientific and Research Organization (CSIRO), Hobart, Australia.
    A novel plane extraction approach using supervised learning2013In: Machine Vision and Applications, ISSN 0932-8092, E-ISSN 1432-1769, Vol. 24, no 6, p. 1229-1237Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel approach for the classification of planar surfaces in an unorganized point clouds. A feature-based planner surface detection method is proposed which classifies a point cloud data into planar and non-planar points by learning a classification model from an example set of planes. The algorithm performs segmentation of the scene by applying a graph partitioning approach with improved representation of association among graph nodes. The planarity estimation of the points in a scene segment is then achieved by classifying input points as planar points which satisfy planarity constraint imposed by the learned model. The resultant planes have potential application in solving simultaneous localization and mapping problem for navigation of an unmanned-air vehicle. The proposed method is validated on real and synthetic scenes. The real data consist of five datasets recorded by capturing three-dimensional(3D) point clouds when a RGBD camera is moved in five different indoor scenes. A set of synthetic 3D scenes are constructed containing planar and non-planar structures. The synthetic data are contaminated with Gaussian and random structure noise. The results of the empirical evaluation on both the real and the simulated data suggest that the method provides a generalized solution for plane detection even in the presence of the noise and non-planar objects in the scene. Furthermore, a comparative study has been performed between multiple plane extraction methods.

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