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
Un système d'attention aux objets multi-agents est proposé, basé sur un modèle de sélection d'attracteurs d'inspiration biologique. L'attention aux objets est facilitée par l'utilisation d'une séquence vidéo et d'une carte de profondeur obtenue grâce à un capteur d'image à œil composé TOMBO. La robustesse du système multi-agent face aux changements environnementaux est améliorée en utilisant le modèle biologique de réponse adaptative par sélection d'attracteur. Pour mettre en œuvre le système proposé, une architecture VLSI efficace est utilisée, réduisant ainsi les énormes coûts de calcul et les accès à la mémoire requis pour le traitement des cartes de profondeur et le processus de sélection d'attracteurs multi-agents. Selon le résultat de la mise en œuvre du FPGA du système d'attention aux objets proposé, qui est réalisé en utilisant 7,063 640 tranches, XNUMX
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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
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@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},}
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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 -