• The KIPS Transactions:PartA
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• v.14A no.7
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• pp.435-442
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• 2007

This paper is concerned with the mesh segmentation problem that can be applied to diverse applications such as texture mapping, simplification, morphing, compression, and shape matching for 3D mesh models. The mesh segmentation is the process of dividing a given mesh into the disjoint set of sub-meshes. We propose a method for segmenting meshes by simultaneously reflecting global and local geometric characteristics of the meshes. First, we extract sharp vertices over mesh vertices by interpreting the curvatures and convexity of a given mesh, which are respectively contained in the local and global geometric characteristics of the mesh. Next, we partition the sharp vertices into the $\kappa$ number of clusters by adopting the $\kappa$-means clustering method [29] based on the Euclidean distances between all pairs of the sharp vertices. Other vertices excluding the sharp vertices are merged into the nearest clusters by Euclidean distances. Also we implement the proposed method and visualize its experimental results on several 3D mesh models.

• The Journal of the Korea Contents Association
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• v.8 no.5
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• pp.94-103
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• 2008

In this paper, we adapt the $\kappa$-means clustering technique to segmenting a given 3D mesh. In order to avoid the locally minimal convergence and speed up the computing time, first we extract sharp vertices from the mesh by analysing its curvature and convexity that respectively reflect the local and global geometric characteristics from the viewpoint of cognitive science. Next the sharp vertices are partitioned into $\kappa$ clusters by iterated converging with the $\kappa$-means clustering method based on the geodesic distance instead of the Euclidean distance between each pair of the sharp vertices. For obtaining the effective result of $\kappa$-means clustering method, it is crucial to assign an initial value to $\kappa$ appropriately. Hence, we automatically compute a reasonable number of clusters as an initial value of $\kappa$. Finally the mesh segmentation is completed by merging other vertices except the sharp vertices into the nearest cluster by geodesic distance.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06b
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• pp.167-170
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• 2007

본 논문에서는 텍스춰매핑, 재메쉬화, 메쉬의 단순화와 모핑 및 압축 등 다양한 분야에 적용되는 메쉬분할 문제를 다룬다. 메쉬분할은 주어진 삼차원 메쉬를 서로 떨어진 집합(disjoint sets)으로 분할하는 것으로서 여러 연구자들에 의해 많은 연구 결과들이 제시되어 왔다. 본 논문에서는 삼차원 메쉬가 가지고 있는 기하학적 특성을 고려하여 메쉬를 분할하는 방법을 제시하고자 한다. 먼저 메쉬의 국부적 기하 특성인 곡률 정보와 전역적 기하 특성인 볼록성을 이용하여 삼차원 메쉬를 구성하는 첨예정점을 추출하였고, 이들간의 거리 정보를 이용하여 이 첨예정점들을 군집화(clustering)하였다. 최종 메쉬분할을 위해 분할된 첨예정점에 속하지 않는 나머지 정점들에 대해 거리 정보를 이용하여 군집화를 수행하였다. 본 논문에서 제안한 메쉬분할 방법을 검증하기 위해 벤치마크로 공개된 여러 메쉬 모델에 대해 실험하여 그 결과를 보여주었다.