• Title/Summary/Keyword: topological optimization

Search Result 84, Processing Time 0.02 seconds

Progressive Reconstruction of 3D Objects from a Single Freehand Line Drawing (Free-Hand 선화로부터 점진적 3차원 물체 복원)

  • 오범수;김창헌
    • Journal of KIISE:Computer Systems and Theory
    • /
    • v.30 no.3_4
    • /
    • pp.168-185
    • /
    • 2003
  • This paper presents a progressive algorithm that not only can narrow down the search domain in the course of face identification but also can fast reconstruct various 3D objects from a sketch drawing. The sketch drawing, edge-vertex graph without hidden line removal, which serves as input for reconstruction process, is obtained from an inaccurate freehand sketch of a 3D wireframe object. The algorithm is executed in two stages. In the face identification stage, we generate and classify potential faces into implausible, basis, and minimal faces by using geometrical and topological constraints to reduce search space. The proposed algorithm searches the space of minimal faces only to identify actual faces of an object fast. In the object reconstruction stage, we progressively calculate a 3D structure by optimizing the coordinates of vertices of an object according to the sketch order of faces. The progressive method reconstructs the most plausible 3D object quickly by applying 3D constraints that are derived from the relationship between the object and the sketch drawing in the optimization process. Furthermore, it allows the designer to change viewpoint during sketching. The progressive reconstruction algorithm is discussed, and examples from a working implementation are given.

Improved Simulated-Annealing Technique for Sequence-Pair based Floorplan (Sequence-Pair 기반의 플로어플랜을 위한 개선된 Simulated-Annealing 기법)

  • Sung, Young-Tae;Hur, Sung-Woo
    • Journal of the Institute of Electronics Engineers of Korea SD
    • /
    • v.46 no.4
    • /
    • pp.28-36
    • /
    • 2009
  • Sequence-Pair(SP) model represents the topological relation between modules. In general, SP model based floorplanners search solutions using Simulated-Annealing(SA) algorithm. Several SA based floorplanning techniques using SP model have been published. To improve the performance of those techniques they tried to improve the speed for evaluation function for SP model, to find better scheduling methods and perturb functions for SA. In this paper we propose a two phase SA based algorithm. In the first phase, white space between modules is reduced by applying compaction technique to the floorplan obtained by an SP. From the compacted floorplan, the corresponding SP is determined. Solution space has been searched by changing the SP in the SA framework. When solutions converge to some threshold value, the first phase of the SA based search stops. Then using the typical SA based algorithm, ie, without using the compaction technique, the second phase of our algorithm continues to find optimal solutions. Experimental results with MCNC benchmark circuits show that how the proposed technique affects to the procedure for SA based floorplainning algorithm and that the results obtained by our technique is better than those obtained by existing SA-based algorithms.

Optimization of Structure-Adaptive Self-Organizing Map Using Genetic Algorithm (유전자 알고리즘을 사용한 구조적응 자기구성 지도의 최적화)

  • 김현돈;조성배
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.11 no.3
    • /
    • pp.223-230
    • /
    • 2001
  • Since self-organizing map (SOM) preserves the topology of ordering in input spaces and trains itself by unsupervised algorithm, it is Llsed in many areas. However, SOM has a shortcoming: structure cannot be easily detcrmined without many trials-and-errors. Structure-adaptive self-orgnizing map (SASOM) which can adapt its structure as well as its weights overcome the shortcoming of self-organizing map: SASOM makes use of structure adaptation capability to place the nodes of prototype vectors into the pattern space accurately so as to make the decision boundmies as close to the class boundaries as possible. In this scheme, the initialization of weights of newly adapted nodes is important. This paper proposes a method which optimizes SASOM with genetic algorithm (GA) to determines the weight vector of newly split node. The leanling algorithm is a hybrid of unsupervised learning method and supervised learning method using LVQ algorithm. This proposed method not only shows higher performance than SASOM in terms of recognition rate and variation, but also preserves the topological order of input patterns well. Experiments with 2D pattern space data and handwritten digit database show that the proposed method is promising.

  • PDF

Direct Reconstruction of Displaced Subdivision Mesh from Unorganized 3D Points (연결정보가 없는 3차원 점으로부터 차이분할메쉬 직접 복원)

  • Jung, Won-Ki;Kim, Chang-Heon
    • Journal of KIISE:Computer Systems and Theory
    • /
    • v.29 no.6
    • /
    • pp.307-317
    • /
    • 2002
  • In this paper we propose a new mesh reconstruction scheme that produces a displaced subdivision surface directly from unorganized points. The displaced subdivision surface is a new mesh representation that defines a detailed mesh with a displacement map over a smooth domain surface, but original displaced subdivision surface algorithm needs an explicit polygonal mesh since it is not a mesh reconstruction algorithm but a mesh conversion (remeshing) algorithm. The main idea of our approach is that we sample surface detail from unorganized points without any topological information. For this, we predict a virtual triangular face from unorganized points for each sampling ray from a parameteric domain surface. Direct displaced subdivision surface reconstruction from unorganized points has much importance since the output of this algorithm has several important properties: It has compact mesh representation since most vertices can be represented by only a scalar value. Underlying structure of it is piecewise regular so it ran be easily transformed into a multiresolution mesh. Smoothness after mesh deformation is automatically preserved. We avoid time-consuming global energy optimization by employing the input data dependant mesh smoothing, so we can get a good quality displaced subdivision surface quickly.