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
생물학적으로 영감을 받은 어트랙터 선택 모델을 기반으로 하는 다중 에이전트 객체 주의 시스템이 제안됩니다. 복안 이미지 센서 TOMBO를 통해 얻은 비디오 시퀀스와 깊이 맵을 사용하여 객체 주의를 촉진합니다. 환경 변화에 대한 다중 에이전트 시스템의 견고성은 어트랙터 선택에 의한 적응 반응의 생물학적 모델을 활용하여 향상됩니다. 제안된 시스템을 구현하기 위해 깊이 맵 처리 및 다중 에이전트 어트랙터 선택 프로세스에 필요한 막대한 계산 비용과 메모리 액세스를 줄이는 효율적인 VLSI 아키텍처가 사용됩니다. 제안된 객체 주의 시스템의 FPGA 구현 결과에 따르면 7,063개의 슬라이스, 640개의 슬라이스를 이용하여 구현된다.
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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},}
부
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 -