Relation Oriented Modeling for Heterogeneous Object Design

2005 ◽  
Author(s):  
X. Y. Kou ◽  
S. T. Tan ◽  
W. S. Sze
Keyword(s):  
Complex Objects ◽  
Tree Structures ◽  
Modeling Approaches ◽  
Modeling Process ◽  
Heterogeneous Objects ◽  
Model Complex ◽  
Heterogeneous Object ◽  
Linear List ◽  
Heterogeneous Object Modeling ◽  
Heterogeneous Feature

Relation oriented modeling approaches are proposed to design heterogeneous objects. The heterogeneous object modeling process is viewed as representing and manipulating complex geometrical, topological and material variation relations with proper data structures. Linear list structure, hierarchical tree structures and more general graph structures are used to represent complex heterogeneous objects. The powerful non-manifold cellular representation and the hierarchical heterogeneous feature tree representation are combined to model complex objects with simultaneous geometry intricacies and compound material variations. We demonstrate that relations play critical roles in heterogeneous object design and under the relation oriented framework, heterogeneous objects can be modeled with generic, uniform representations. The proposed relation oriented modeling approaches are tested with a prototype heterogeneous CAD modeler and presented with different types of heterogeneous object examples.

Heterogeneous Object Modeling Approach Based on ACIS and HOOPS

2009 ◽  
Vol 419-420 ◽  
pp. 793-796
Author(s):  
An Ping Xu ◽  
Ting Zang ◽  
Zhen Peng Ji ◽  
Yun Xia Qu
Keyword(s):  
Inverse Distance Weighting ◽  
Material Composition ◽  
Functional Modules ◽  
Object Modeling ◽  
Modeling Approach ◽  
Heterogeneous Objects ◽  
Distance Weighting ◽  
Heterogeneous Object ◽  
Heterogeneous Object Modeling ◽  
Inverse Distance

This paper deals with the background and significance of working on heterogeneous objects modeling and briefly introduces the architecture of ACIS and HOOPS and their corresponding functional modules. Based on inverse-distance weighting algorithm to determine the material composition within the object, the general approach to modeling the heterogeneous objects by using ACIS and HOOPS is introduced and demonstrated via some simple examples.

Heterogeneous object modeling: A review

2007 ◽  
Vol 39 (4) ◽  
pp. 284-301 ◽  
Author(s):  
X.Y. Kou ◽  
S.T. Tan
Keyword(s):  
Object Modeling ◽  
Heterogeneous Object ◽  
Heterogeneous Object Modeling

scholarly journals Weakly-Supervised Multi-view Multi-instance Multi-label Learning

2020 ◽  
Author(s):  
Yuying Xing ◽  
Guoxian Yu ◽  
Jun Wang ◽  
Carlotta Domeniconi ◽  
Xiangliang Zhang
Keyword(s):  
Objective Function ◽  
Dictionary Learning ◽  
Function Optimization ◽  
Learning To Learn ◽  
Complex Objects ◽  
Representational Space ◽  
Model Complex ◽  
Feature Similarity ◽  
Benchmark Datasets ◽  
Weakly Supervised

Multi-view, Multi-instance, and Multi-label Learning (M3L) can model complex objects (bags), which are represented with different feature views, made of diverse instances, and annotated with discrete non-exclusive labels. Existing M3L approaches assume a complete correspondence between bags and views, and also assume a complete annotation for training. However, in practice, neither the correspondence between bags, nor the bags' annotations are complete. To tackle such a weakly-supervised M3L task, a solution called WSM3L is introduced. WSM3L adapts multimodal dictionary learning to learn a shared dictionary (representational space) across views and individual encoding vectors of bags for each view. The label similarity and feature similarity of encoded bags are jointly used to match bags across views. In addition, it replenishes the annotations of a bag based on the annotations of its neighborhood bags, and introduces a dispatch and aggregation term to dispatch bag-level annotations to instances and to reversely aggregate instance-level annotations to bags. WSM3L unifies these objectives and processes in a joint objective function to predict the instance-level and bag-level annotations in a coordinated fashion, and it further introduces an alternative solution for the objective function optimization. Extensive experimental results show the effectiveness of WSM3L on benchmark datasets.

Constructive Heterogeneous Object Modeling Using Signed Approximate Real Distance Functions

2005 ◽  
Vol 6 (3) ◽  
pp. 221-229 ◽  
Author(s):  
Pierre-Alain Fayolle ◽  
Alexander Pasko ◽  
Benjamin Schmitt ◽  
Nikolay Mirenkov
Keyword(s):  
Distance Function ◽  
Model Material ◽  
Shape Modeling ◽  
Distance Functions ◽  
Material Distribution ◽  
Object Modeling ◽  
Smooth Approximation ◽  
Model Framework ◽  
Heterogeneous Object ◽  
Heterogeneous Object Modeling

We introduce a smooth approximation of the min∕max operations, called signed approximate real distance function (SARDF), for maintaining an approximate signed distance function in constructive shape modeling. We apply constructive distance-based shape modeling to design objects with heterogeneous material distribution in the constructive hypervolume model framework. The introduced distance approximation helps intuitively model material distributions parametrized by distances to so-called material features. The smoothness of the material functions, provided here by the smoothness of the defining function for the shape, helps to avoid undesirable singularities in the material distribution, like stress or concentrations. We illustrate application of the SARDF operations by two- and three-dimensional heterogeneous object modeling case studies.

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