Yoon, Sang Min ; Kuijper, Arjan (2011)
View-based 3D Model Retrieval using Compressive Sensing Based Classification.
ISPA 2011.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The number of available 3D models in various areas increases steadily. Efficient methods to search for 3D models by content, rather than textual annotations, are crucial. For this purpose, we propose content based 3D model retrieval using a compressive sensing technique which is very efficient in classification by using only few input information. Our approach to search and automatically return a set of 3D mesh models from a large database consists of three major steps: (1) suggestive contours extraction from different viewpoints to extract features of the query 3D model; (2) descriptor computation by analyzing the Histogram of Oriented Gradients of the suggestive contours in the space of diffusion tensor fields; and (3) compressive sensing based machine learning to retrieve the models and the most probable view-point. Experimental results show that our proposed 3D model retrieval system is very effective to retrieve the 3D models, even though there are variations of shape and pose of the models.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2011 |
| Autor(en): | Yoon, Sang Min ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | View-based 3D Model Retrieval using Compressive Sensing Based Classification |
| Sprache: | Englisch |
| Publikationsjahr: | 2011 |
| Verlag: | University of Zagreb, Zagreb |
| Veranstaltungstitel: | ISPA 2011 |
| Kurzbeschreibung (Abstract): | The number of available 3D models in various areas increases steadily. Efficient methods to search for 3D models by content, rather than textual annotations, are crucial. For this purpose, we propose content based 3D model retrieval using a compressive sensing technique which is very efficient in classification by using only few input information. Our approach to search and automatically return a set of 3D mesh models from a large database consists of three major steps: (1) suggestive contours extraction from different viewpoints to extract features of the query 3D model; (2) descriptor computation by analyzing the Histogram of Oriented Gradients of the suggestive contours in the space of diffusion tensor fields; and (3) compressive sensing based machine learning to retrieve the models and the most probable view-point. Experimental results show that our proposed 3D model retrieval system is very effective to retrieve the 3D models, even though there are variations of shape and pose of the models. |
| Freie Schlagworte: | Business Field: Visual decision support, Research Area: Semantics in the modeling process, 3D Object retrieval, Histograms, Diffusion tensor fields, 3D Object localisation |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 12 Nov 2018 11:16 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum