Abstract
We propose a novel diminished reality method which is able to (i) automatically recognize the region to be diminished, (ii) work with a single RGB-D sensor, and (iii) work without pre-processing to generate a 3D model of the target scene by utilizing SLAM, segmentation, and recognition framework. Especially, regarding the recognition of the area to be diminished, our method is able to maintain high accuracy no matter how the camera moves by distributing the viewpoints for each object uniformly and aggregating recognition results from each distributed viewpoint as the same weight. These advantages are demonstrated on the UW RGB-D Dataset and Scenes.
| Original language | English |
|---|---|
| Title of host publication | Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 338-343 |
| Number of pages | 6 |
| ISBN (Electronic) | 9780769563275 |
| DOIs | |
| Publication status | Published - 2017 Oct 27 |
| Event | 16th Adjunct IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017 - Nantes, France Duration: 2017 Oct 9 → 2017 Oct 13 |
Other
| Other | 16th Adjunct IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017 |
|---|---|
| Country | France |
| City | Nantes |
| Period | 17/10/9 → 17/10/13 |
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Keywords
- Convolutional Neural Network
- Diminished Reality
- Object Recognition
- Segmentation
- SLAM
ASJC Scopus subject areas
- Human-Computer Interaction
- Media Technology
- Computer Science Applications
Cite this
Semantic Object Selection and Detection for Diminished Reality Based on SLAM with Viewpoint Class. / Nakajima, Yoshikatsu; Mori, Shohei; Saito, Hideo.
Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 338-343 8088517.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Semantic Object Selection and Detection for Diminished Reality Based on SLAM with Viewpoint Class
AU - Nakajima, Yoshikatsu
AU - Mori, Shohei
AU - Saito, Hideo
PY - 2017/10/27
Y1 - 2017/10/27
N2 - We propose a novel diminished reality method which is able to (i) automatically recognize the region to be diminished, (ii) work with a single RGB-D sensor, and (iii) work without pre-processing to generate a 3D model of the target scene by utilizing SLAM, segmentation, and recognition framework. Especially, regarding the recognition of the area to be diminished, our method is able to maintain high accuracy no matter how the camera moves by distributing the viewpoints for each object uniformly and aggregating recognition results from each distributed viewpoint as the same weight. These advantages are demonstrated on the UW RGB-D Dataset and Scenes.
AB - We propose a novel diminished reality method which is able to (i) automatically recognize the region to be diminished, (ii) work with a single RGB-D sensor, and (iii) work without pre-processing to generate a 3D model of the target scene by utilizing SLAM, segmentation, and recognition framework. Especially, regarding the recognition of the area to be diminished, our method is able to maintain high accuracy no matter how the camera moves by distributing the viewpoints for each object uniformly and aggregating recognition results from each distributed viewpoint as the same weight. These advantages are demonstrated on the UW RGB-D Dataset and Scenes.
KW - Convolutional Neural Network
KW - Diminished Reality
KW - Object Recognition
KW - Segmentation
KW - SLAM
UR - http://www.scopus.com/inward/record.url?scp=85040242065&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040242065&partnerID=8YFLogxK
U2 - 10.1109/ISMAR-Adjunct.2017.98
DO - 10.1109/ISMAR-Adjunct.2017.98
M3 - Conference contribution
AN - SCOPUS:85040242065
SP - 338
EP - 343
BT - Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -