Self-organizing Deformable Model for Mapping 3D Object Model onto Arbitrary Target Surface

2007 ◽  
Author(s):  
Ken'ichi Morooka ◽  
Shun Matsui ◽  
Hiroshi Nagahashi
Keyword(s):  
Target Surface ◽  
Deformable Model ◽  
Object Model ◽  
3D Object ◽  
Self Organizing

Tissue Surface Model Mapping onto Arbitrary Target Surface Based on Self-Organizing Deformable Model

2013 ◽  
Author(s):  
Shoko Miyauchi ◽  
Kenichi Morooka ◽  
Yasushi Miyagi ◽  
Takaichi Fukuda ◽  
Tokuo Tsuji ◽  
...  
Keyword(s):  
Target Surface ◽  
Deformable Model ◽  
Surface Model ◽  
Model Mapping ◽  
Tissue Surface ◽  
Self Organizing

scholarly journals 3203 Kansei Evaluation for Perception of Objects : Focus on the Relationship between 3D object model and impression

2010 ◽  
Vol 2010.20 (0) ◽  
pp. _3203-1_-_3203-2_
Author(s):  
Miyong LEE ◽  
Shinya HONDA ◽  
Yoshihiro NARITA
Keyword(s):  
Object Model ◽  
3D Object ◽  
The Relationship

3D object recognition using deformable model for negating sensing error

2009 ◽  
Author(s):  
Nobutaka Kimura ◽  
Toshio Moriya
Keyword(s):  
Object Recognition ◽  
Deformable Model ◽  
3D Object Recognition ◽  
3D Object

End-to-end 3D object model retrieval by projecting the point cloud onto a unique discriminating 2D view

2020 ◽  
Vol 402 ◽  
pp. 336-345
Author(s):  
Xuzhan Chen ◽  
Youping Chen ◽  
Homayoun Najjaran
Keyword(s):  
Point Cloud ◽  
Object Model ◽  
3D Object ◽  
Model Retrieval ◽  
End To End

scholarly journals Surface Approximation using Growing Self-Organizing Nets and Gradient Information

2007 ◽  
Vol 4 (3) ◽  
pp. 125-136 ◽  
Author(s):  
Jorge Rivera-Rovelo ◽  
Eduardo Bayro-Corrochano
Keyword(s):  
Neural Networks ◽  
Geometric Algebra ◽  
Mr Images ◽  
3D Object ◽  
Gas Network ◽  
Neural Gas ◽  
Gradient Information ◽  
Surface Estimation ◽  
Space Coordinate ◽  
Self Organizing

In this paper we show how to improve the performance of two self-organizing neural networks used to approximate the shape of a 2D or 3D object by incorporating gradient information in the adaptation stage. The methods are based on the growing versions of the Kohonen's map and the neural gas network. Also, we show that in the adaptation stage the network utilizes efficient transformations, expressed as versors in the conformal geometric algebra framework, which build the shape of the object independent of its position in space (coordinate free). Our algorithms were tested with several images, including medical images (CT and MR images). We include also some examples for the case of 3D surface estimation.

Angle- and volume-preserving mapping of organ volume model based on modified Self-organizing Deformable Model

2016 ◽  
Author(s):  
Shoko Miyauchi ◽  
Ken'ichi Morooka ◽  
Tokuo Tsuji ◽  
Yasushi Miyagi ◽  
Takaichi Fukuda ◽  
...  
Keyword(s):  
Deformable Model ◽  
Volume Model ◽  
Model Based ◽  
Organ Volume ◽  
Volume Preserving ◽  
Self Organizing

Segmentation of 3D object in volume dataset using active deformable model

2010 ◽  
Author(s):  
Jonghyun Park ◽  
Wanhyun Cho ◽  
Soonyoung Park ◽  
Sunworl Kim ◽  
Soohyung Kim ◽  
...  
Keyword(s):  
Deformable Model ◽  
3D Object
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