| Project/Area Number |
17300033
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Media informatics/Database
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
NAGAHASHI Hiroshi Tokyo Institute of Technology, Graduate School of Science and Engineering, Professor (20143084)
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| Co-Investigator(Kenkyū-buntansha) |
KUMAZAWA Itsuo Tokyo Institute of Technology, Graduate School of Science and Engineering, Professor (70186469)
AOKI Kota Tokyo Institute of Technology, Graduate School of Science and Engineering, Assistant Professor (90447532)
諸岡 健一 九州大学, デジタルメディシンイニシアティブ, 助教授 (80323806)
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| Project Period (FY) |
2005 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥15,960,000 (Direct Cost: ¥15,000,000、Indirect Cost: ¥960,000)
Fiscal Year 2007: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2006: ¥4,600,000 (Direct Cost: ¥4,600,000)
Fiscal Year 2005: ¥7,200,000 (Direct Cost: ¥7,200,000)
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| Keywords | multi-resolution measurement / 3D object representation / objective sensitivity / mesh adaptations / analysis of scalable textures / least-square mesh / self-organizing 3D deformable model / 3 dimensional video contents / 情報感度特性 / 3次元物体モーフィング / 変形度 / 主観的情報量 / 因子分解法 / 3次元マップ / 時空間画像コンテンツ / モデル統合 / 確信度分布 / 統計的形状特性 / メッシュ細分割 / スケール空間 / 物体のクロスパラメータ化 / 形状変形 / 物体の情報感度特牲 / 3次元物体計測 / 選択的多重解像度計測 / 解像度空間 / データ統合 / LOD / ステレオビジョン / 形状空間感度特性 / 3次元近似モデル |
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| Research Abstract |
In this project, we propose a new method for 3D object model representation and its applications based on a concept of objective sensitivity to a 3D shape, which can deal with multi-view and multi-resolution range data, stereo images, texture images and videos. The objective sensitivity means how much information about the object can be obtained, when its 3D shape is presented in a certain level of detail. The work contains the following sub-goals : The first one is to integrate range data measured in different resolutions and from different view points by considering two certainties for each measured point, and then to generate a 3D shape model in a certain resolution level by using mesh adaptations like subdivision and decimation techniques. Some experimental results have proved that the goal has been achieved.
The second goal is to propose a statistical texture analysis method that can extract some 3D scale factor from a natural image taken in an outdoor scene, where it is not so eas
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y to perform 3D measurement without using an expensive tool such as a laser range finder. The method is based on a hierarchical linear discriminant analysis that can classify some features calculated from higher-order local auto-correlation functions. It has been proved that the method is available for extracting 3D scale factor from texture images. We have also constructed an active stereo vision system that can control panning, tilting and zooming of the camera as an intelligent vision system of a robot. This system can gather some available information of a scene in the local system without any request from outside.
The third goal is to develop a new cross-parameterization technique between 3D mesh models that can be used in various 3 dimensional Digital Geometry Processings (DGP). The cross-parameterization method proposed is based on a least-square mesh technique and a self-organizing deformable model(SDM) developed by the authors. This technique has enabled us to transfer texture and motion attributes of a 3D model to another one directly, or to generate intermediate models between two 3D mesh models. As a special application of the 3D morphing that presents these intermediate models temporally, we did several psychological experiments where subjects answer their results when they recognize what an intermediate shape presented is. These experiments have shown that their cognitive processes depend on the combination of the source and target object models.
Finally, as other applications of the proposed method, we have developed a facial enhancement system based on the SDM, a 3D motion synthesis system based on a machine learning and a clustering algorithm, and a 3D video reconstruction system based on a factorization method. Less
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