We propose a motion detection model, which is suitable for higher speed operation than the video rate, inspired by the neuronal propagation in the hippocampus in the brain. The model detects motion of edges, which are extracted from monocular image sequences, on specified 2D maps without image matching. We introduce gating units into a CA3-CA1 model, where CA3 and CA1 are the names of hippocampal regions. We use the function of gating units to reduce mismatching for applying our model in complicated situations. We also propose a map-division method to achieve accurate detection. We have evaluated the performance of the proposed model by using artificial and real image sequences. The results show that the proposed model can run up to 1.0 ms/frame if using a resolution of 64
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copy
Haichao LIANG, Takashi MORIE, "A Motion Detection Model Inspired by the Neuronal Propagation in the Hippocampus" in IEICE TRANSACTIONS on Fundamentals,
vol. E95-A, no. 2, pp. 576-585, February 2012, doi: 10.1587/transfun.E95.A.576.
Abstract: We propose a motion detection model, which is suitable for higher speed operation than the video rate, inspired by the neuronal propagation in the hippocampus in the brain. The model detects motion of edges, which are extracted from monocular image sequences, on specified 2D maps without image matching. We introduce gating units into a CA3-CA1 model, where CA3 and CA1 are the names of hippocampal regions. We use the function of gating units to reduce mismatching for applying our model in complicated situations. We also propose a map-division method to achieve accurate detection. We have evaluated the performance of the proposed model by using artificial and real image sequences. The results show that the proposed model can run up to 1.0 ms/frame if using a resolution of 64
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E95.A.576/_p
Copy
@ARTICLE{e95-a_2_576,
author={Haichao LIANG, Takashi MORIE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Motion Detection Model Inspired by the Neuronal Propagation in the Hippocampus},
year={2012},
volume={E95-A},
number={2},
pages={576-585},
abstract={We propose a motion detection model, which is suitable for higher speed operation than the video rate, inspired by the neuronal propagation in the hippocampus in the brain. The model detects motion of edges, which are extracted from monocular image sequences, on specified 2D maps without image matching. We introduce gating units into a CA3-CA1 model, where CA3 and CA1 are the names of hippocampal regions. We use the function of gating units to reduce mismatching for applying our model in complicated situations. We also propose a map-division method to achieve accurate detection. We have evaluated the performance of the proposed model by using artificial and real image sequences. The results show that the proposed model can run up to 1.0 ms/frame if using a resolution of 64
keywords={},
doi={10.1587/transfun.E95.A.576},
ISSN={1745-1337},
month={February},}
Copy
TY - JOUR
TI - A Motion Detection Model Inspired by the Neuronal Propagation in the Hippocampus
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 576
EP - 585
AU - Haichao LIANG
AU - Takashi MORIE
PY - 2012
DO - 10.1587/transfun.E95.A.576
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E95-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2012
AB - We propose a motion detection model, which is suitable for higher speed operation than the video rate, inspired by the neuronal propagation in the hippocampus in the brain. The model detects motion of edges, which are extracted from monocular image sequences, on specified 2D maps without image matching. We introduce gating units into a CA3-CA1 model, where CA3 and CA1 are the names of hippocampal regions. We use the function of gating units to reduce mismatching for applying our model in complicated situations. We also propose a map-division method to achieve accurate detection. We have evaluated the performance of the proposed model by using artificial and real image sequences. The results show that the proposed model can run up to 1.0 ms/frame if using a resolution of 64
ER -