Abstract
We propose neural models for segmenting the area of a body from visual scene based on predictability. Neuroscience has shown that a prediction model in brain, which predicts sensory-feedback from motor command, can divide the sensory-feedback into the self-motion derived feedback and other derived feedback. The prediction model is important for prediction control of the body. Previous studies in robotics of the prediction model assumed that a robot can recognize the position of its body (e.g. its hand) and that the view contains only that body part. In our models, motor commands and visual feedback (pixel image that includes not only a hand but also object and background) are input into a neural network model and then the body area is segmented and prediction model of body is acquired. Our model contains two parts: 1) An object detection model obtains a conversion system between object positions and the pixel image. 2) A movement prediction model predicts hand-object positions from motor commands and identifies the body. We confirmed that our models can segment the body/object area based on their pixel textures and discriminate between them by using prediction error.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
| DOIs | |
| Publication status | Published - 2012 |
| Externally published | Yes |
| Event | 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD Duration: 2012 Jun 10 → 2012 Jun 15 |
Other
| Other | 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 |
|---|---|
| City | Brisbane, QLD |
| Period | 12/6/10 → 12/6/15 |
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ASJC Scopus subject areas
- Software
- Artificial Intelligence
Cite this
Body area segmentation from visual scene based on predictability of neuro-dynamical system. / Nobuta, Harumitsu; Kawamoto, Kenta; Noda, Kuniaki; Sabe, Kohtaro; Nishide, Shun; Okuno, Hiroshi G.; Ogata, Tetsuya.
Proceedings of the International Joint Conference on Neural Networks. 2012. 6252530.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Body area segmentation from visual scene based on predictability of neuro-dynamical system
AU - Nobuta, Harumitsu
AU - Kawamoto, Kenta
AU - Noda, Kuniaki
AU - Sabe, Kohtaro
AU - Nishide, Shun
AU - Okuno, Hiroshi G.
AU - Ogata, Tetsuya
PY - 2012
Y1 - 2012
N2 - We propose neural models for segmenting the area of a body from visual scene based on predictability. Neuroscience has shown that a prediction model in brain, which predicts sensory-feedback from motor command, can divide the sensory-feedback into the self-motion derived feedback and other derived feedback. The prediction model is important for prediction control of the body. Previous studies in robotics of the prediction model assumed that a robot can recognize the position of its body (e.g. its hand) and that the view contains only that body part. In our models, motor commands and visual feedback (pixel image that includes not only a hand but also object and background) are input into a neural network model and then the body area is segmented and prediction model of body is acquired. Our model contains two parts: 1) An object detection model obtains a conversion system between object positions and the pixel image. 2) A movement prediction model predicts hand-object positions from motor commands and identifies the body. We confirmed that our models can segment the body/object area based on their pixel textures and discriminate between them by using prediction error.
AB - We propose neural models for segmenting the area of a body from visual scene based on predictability. Neuroscience has shown that a prediction model in brain, which predicts sensory-feedback from motor command, can divide the sensory-feedback into the self-motion derived feedback and other derived feedback. The prediction model is important for prediction control of the body. Previous studies in robotics of the prediction model assumed that a robot can recognize the position of its body (e.g. its hand) and that the view contains only that body part. In our models, motor commands and visual feedback (pixel image that includes not only a hand but also object and background) are input into a neural network model and then the body area is segmented and prediction model of body is acquired. Our model contains two parts: 1) An object detection model obtains a conversion system between object positions and the pixel image. 2) A movement prediction model predicts hand-object positions from motor commands and identifies the body. We confirmed that our models can segment the body/object area based on their pixel textures and discriminate between them by using prediction error.
UR - http://www.scopus.com/inward/record.url?scp=84865106492&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84865106492&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2012.6252530
DO - 10.1109/IJCNN.2012.6252530
M3 - Conference contribution
AN - SCOPUS:84865106492
SN - 9781467314909
BT - Proceedings of the International Joint Conference on Neural Networks
ER -