The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Sistem perhatian objek berbilang ejen dicadangkan, yang berdasarkan model pemilihan penarik yang diilhamkan secara biologi. Perhatian objek dipermudahkan dengan menggunakan urutan video dan peta kedalaman yang diperoleh melalui sensor imej mata kompaun TOMBO. Kekukuhan sistem berbilang ejen terhadap perubahan persekitaran dipertingkatkan dengan menggunakan model biologi tindak balas penyesuaian melalui pemilihan penarik. Untuk melaksanakan sistem yang dicadangkan, seni bina VLSI yang cekap digunakan dengan mengurangkan kos pengiraan yang besar dan akses memori yang diperlukan untuk pemprosesan peta mendalam dan proses pemilihan penarik berbilang ejen. Menurut hasil pelaksanaan FPGA sistem perhatian objek yang dicadangkan, yang dicapai dengan menggunakan 7,063 keping, 640
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Salinan
Ryoji HASHIMOTO, Tomoya MATSUMURA, Yoshihiro NOZATO, Kenji WATANABE, Takao ONOYE, "Implementation of Multi-Agent Object Attention System Based on Biologically Inspired Attractor Selection" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 10, pp. 2909-2917, October 2008, doi: 10.1093/ietfec/e91-a.10.2909.
Abstract: A multi-agent object attention system is proposed, which is based on biologically inspired attractor selection model. Object attention is facilitated by using a video sequence and a depth map obtained through a compound-eye image sensor TOMBO. Robustness of the multi-agent system over environmental changes is enhanced by utilizing the biological model of adaptive response by attractor selection. To implement the proposed system, an efficient VLSI architecture is employed with reducing enormous computational costs and memory accesses required for depth map processing and multi-agent attractor selection process. According to the FPGA implementation result of the proposed object attention system, which is accomplished by using 7,063 slices, 640
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.10.2909/_p
Salinan
@ARTICLE{e91-a_10_2909,
author={Ryoji HASHIMOTO, Tomoya MATSUMURA, Yoshihiro NOZATO, Kenji WATANABE, Takao ONOYE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Implementation of Multi-Agent Object Attention System Based on Biologically Inspired Attractor Selection},
year={2008},
volume={E91-A},
number={10},
pages={2909-2917},
abstract={A multi-agent object attention system is proposed, which is based on biologically inspired attractor selection model. Object attention is facilitated by using a video sequence and a depth map obtained through a compound-eye image sensor TOMBO. Robustness of the multi-agent system over environmental changes is enhanced by utilizing the biological model of adaptive response by attractor selection. To implement the proposed system, an efficient VLSI architecture is employed with reducing enormous computational costs and memory accesses required for depth map processing and multi-agent attractor selection process. According to the FPGA implementation result of the proposed object attention system, which is accomplished by using 7,063 slices, 640
keywords={},
doi={10.1093/ietfec/e91-a.10.2909},
ISSN={1745-1337},
month={October},}
Salinan
TY - JOUR
TI - Implementation of Multi-Agent Object Attention System Based on Biologically Inspired Attractor Selection
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2909
EP - 2917
AU - Ryoji HASHIMOTO
AU - Tomoya MATSUMURA
AU - Yoshihiro NOZATO
AU - Kenji WATANABE
AU - Takao ONOYE
PY - 2008
DO - 10.1093/ietfec/e91-a.10.2909
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2008
AB - A multi-agent object attention system is proposed, which is based on biologically inspired attractor selection model. Object attention is facilitated by using a video sequence and a depth map obtained through a compound-eye image sensor TOMBO. Robustness of the multi-agent system over environmental changes is enhanced by utilizing the biological model of adaptive response by attractor selection. To implement the proposed system, an efficient VLSI architecture is employed with reducing enormous computational costs and memory accesses required for depth map processing and multi-agent attractor selection process. According to the FPGA implementation result of the proposed object attention system, which is accomplished by using 7,063 slices, 640
ER -