3,068 research outputs found
A Computational Model of the Short-Cut Rule for 2D Shape Decomposition
We propose a new 2D shape decomposition method based on the short-cut rule.
The short-cut rule originates from cognition research, and states that the
human visual system prefers to partition an object into parts using the
shortest possible cuts. We propose and implement a computational model for the
short-cut rule and apply it to the problem of shape decomposition. The model we
proposed generates a set of cut hypotheses passing through the points on the
silhouette which represent the negative minima of curvature. We then show that
most part-cut hypotheses can be eliminated by analysis of local properties of
each. Finally, the remaining hypotheses are evaluated in ascending length
order, which guarantees that of any pair of conflicting cuts only the shortest
will be accepted. We demonstrate that, compared with state-of-the-art shape
decomposition methods, the proposed approach achieves decomposition results
which better correspond to human intuition as revealed in psychological
experiments.Comment: 11 page
DisLoc: A Convex Partitioning Based Approach for Distributed 3-D Localization in Wireless Sensor Networks
Accurate localization in wireless sensor networks (WSNs) is fundamental to many applications, such as geographic routing and position-aware data processing. This, however, is challenging in large scale 3-D WSNs due to the irregular topology, such as holes in the path, of the network. The irregular topology may cause overestimated Euclidean distance between nodes as the communication path is bent and accordingly introduces severe errors in 3-D WSN localization. As an effort towards the issue, this paper develops a distributed algorithm to achieve accurate 3-D WSN localization. Our proposal is composed of two steps, segmentation and joint localization. In specific, the entire network is first divided into several subnetworks by applying the approximate convex partitioning. A spatial convex node recognition mechanism is developed to assist the network segmentation, which relies on the connectivity information only. After that, each subnetwork is accurately localized by using the multidimensional scaling-based algorithm. The proposed localization algorithm also applies a new 3-D coordinate transformation algorithm, which helps reduce the errors introduced by coordinate integration between subnetworks and improve the localization accuracy. Using extensive simulations, we show that our proposal can effectively segment a complex 3-D sensor network and significantly improve the localization rate in comparison with existing solutions
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