A distance-driven method to compute the surface and curve skeletons of 3D objects in voxel images is described. The method is based on the use of the <3,4,5> weighted distance transform, on the detection of anchor points, and on the application of topology preserving removal operations. The obtained surface and curve skeletons are centered within the object, have the same topology as the object, and have unit thickness. The object can be almost completely recovered from the surface skeleton since this includes almost all of the centers of maximal balls of the object. Hence, the surface skeleton is a faithful representation. In turn, though only partial recovery is possible from the curve skeleton, this still provides an appealing representation of the object.

New 3×3×3 operators are introduced to compute the surface skeleton of a 3D object by either sequential or parallel voxel removal. We show that the operators can be employed without creating disconnections, cavities, tunnels and vanishing of object components. A final thinning process, aimed at obtaining a unit-thick surface skeleton, is also described.

Abstract. An object decomposition method is presented, which is guided by a suitable partition of the skeleton. The method is easy to implement, has a limited computational cost and produces results in agreement with human intuition.

Ambient assisted living solutions (AAL) aim to improve the quality of life, especially of elderly with cognitive and physical disabilities, by providing assistive tasks such as medication management, reminders of daily activities or medical tests. However, the loss of physical functions (such as mobility, eyesight and hearing), as well as the loss of cognitive functions (starting from amnesia to severe disabilities as dementia or Alzheimer), require appropriate solutions in order to offer personalised assistive tasks according to user needs and characteristics. In this paper, we propose a novel methodology that is based on the integration of a service-oriented approach with normative reasoning to automatically generate assistive tasks customised for different target user's profiles, and deployable in any AAL environment. The formalisation of the conceptual steps of the methodology is provided, together with a framework implementing them. The advantages of dynamic customisation obtained by decoupling the functional and not functional aspects of assistive technology are shown through a validation scenario.

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