k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Human skeleton tracking from depth data using geodesic distances and optical flow
Image and Vision Computing
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In this study we consider the problem of estimating Human-Cloth topological relationship using a depth sensor and its application to robotic clothing assistance. In the past, reinforcement learning with low dimensional topological representations has been used to learn the necessary motor skills to perform clothing. In this framework, motion capture system was used to observe the Human-Cloth relationship. There were problems faced with the use of motion capture system: 1) Elaborate and expensive setup of the system 2) Occlusion of optical markers by other objects in the environment 3) Observation of non existent markers due to unwanted reflections. To overcome these difficulties, we propose a framework to observe the Human-Cloth topological relationship using a depth sensor. We demonstrate that the depth sensor can provide reliable estimates of topology coordinates and can replace the complex and expensive setup of motion capture system.