• International Journal of Contents
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• v.3 no.2
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• pp.25-29
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• 2007

In this paper, we discuss the mesh segmentation problem which divides a given 3D mesh into several disjoint sets. To solve the problem, we propose a greedy method based on the merging priority metric defined for representing the geometric properties of meaningful parts. The proposed priority metric is a weighted function using five geometric parameters, those are, a distribution of Gaussian map, boundary path concavity, boundary path length, cardinality, and segmentation resolution. In special, we can control by setting up the weight values of the proposed geometric parameters to obtain visually better mesh segmentation. Finally, we carry out an experiment on several 3D mesh models using the proposed methods and visualize the results.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.52-59
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• 2007

This paper presents a greedy method for user-steered mesh segmentation, which is based on the merging priority metric defined for representing the geometric properties of meaningful parts. The priority metric is a weighted function composed of five geometric parameters: distribution of Gaussian map, boundary path concavity, boundary path length, cardinality, and segmentation resolution. This scheme can be extended without any modification only by defining more geometric parameters and adding them. Our experimental results show that the shapes of segmented parts can be controlled by setting up the weight values of geometric parameters.

• Journal of the Korea Society of Computer and Information
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• v.3 no.4
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• pp.63-69
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• 1998

The area optimization of the power distribution network is an important problem in the layout design of VLSI systems. In this paper we propose noval methods to solve the problem of designing minimal area power distribution nets, while satisfying voltage drop and electromigration constraints. We propose two novel greedy heuristics for power net design-one based on bottom-up tree construction using greedy merging and the other based on top-down linearly separable partitioning.