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
PIRF로 지정된 새로운 Position-Invariant Robust Feature는 매우 동적인 장면 인식 문제를 해결하기 위해 제시됩니다. PIRF는 기존 지역 특징을 식별하여 얻습니다(i.e. SIFT)는 한 장소 내에서 넓은 기본 가시성을 갖고 있습니다(한 장소에는 두 개 이상의 연속 이미지가 포함되어 있음). 이러한 넓은 기준선 가시적 특징은 단일 PIRF로 표시되며, 이는 PIRF와 관련된 모든 설명자의 평균으로 계산됩니다. 특히 PIRF는 장면의 매우 동적인 변화에 대해 강력합니다. 단일 PIRF는 많은 동적 이미지의 많은 특징과 정확하게 일치할 수 있습니다. 본 문서에서는 장면 인식을 위해 이러한 기능을 사용하는 접근 방식도 설명합니다. 인식은 개별 PIRF를 테스트 이미지의 특징 세트와 일치시키는 방식으로 진행되며, 이후 다수결 투표를 통해 가장 일치하는 PIRF가 높은 장소를 식별합니다. PIRF 시스템은 2000개 이상의 실외 전방향 이미지와 COLD 데이터 세트에 대해 교육 및 테스트되었습니다. 단순함에도 불구하고 PIRF는 역동적인 야외 장면에 대해 현저히 더 나은 인식률을 제공합니다(ca. 90%) 다른 기능을 사용하는 것보다 또한 PIRF(PIRF-Nav) 기반 로봇 내비게이션 시스템은 시간(70% 감소) 및 메모리 측면에서 다른 증분 토폴로지 매핑 방법보다 성능이 뛰어납니다. PIRF의 수를 더욱 줄여서 시간을 단축하면서도 높은 정확도를 유지할 수 있어 장기적인 인식 및 위치 파악에 적합합니다.
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Aram KAWEWONG, Sirinart TANGRUAMSUB, Osamu HASEGAWA, "Position-Invariant Robust Features for Long-Term Recognition of Dynamic Outdoor Scenes" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 9, pp. 2587-2601, September 2010, doi: 10.1587/transinf.E93.D.2587.
Abstract: A novel Position-Invariant Robust Feature, designated as PIRF, is presented to address the problem of highly dynamic scene recognition. The PIRF is obtained by identifying existing local features (i.e. SIFT) that have a wide baseline visibility within a place (one place contains more than one sequential images). These wide-baseline visible features are then represented as a single PIRF, which is computed as an average of all descriptors associated with the PIRF. Particularly, PIRFs are robust against highly dynamical changes in scene: a single PIRF can be matched correctly against many features from many dynamical images. This paper also describes an approach to using these features for scene recognition. Recognition proceeds by matching an individual PIRF to a set of features from test images, with subsequent majority voting to identify a place with the highest matched PIRF. The PIRF system is trained and tested on 2000+ outdoor omnidirectional images and on COLD datasets. Despite its simplicity, PIRF offers a markedly better rate of recognition for dynamic outdoor scenes (ca. 90%) than the use of other features. Additionally, a robot navigation system based on PIRF (PIRF-Nav) can outperform other incremental topological mapping methods in terms of time (70% less) and memory. The number of PIRFs can be reduced further to reduce the time while retaining high accuracy, which makes it suitable for long-term recognition and localization.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2587/_p
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@ARTICLE{e93-d_9_2587,
author={Aram KAWEWONG, Sirinart TANGRUAMSUB, Osamu HASEGAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Position-Invariant Robust Features for Long-Term Recognition of Dynamic Outdoor Scenes},
year={2010},
volume={E93-D},
number={9},
pages={2587-2601},
abstract={A novel Position-Invariant Robust Feature, designated as PIRF, is presented to address the problem of highly dynamic scene recognition. The PIRF is obtained by identifying existing local features (i.e. SIFT) that have a wide baseline visibility within a place (one place contains more than one sequential images). These wide-baseline visible features are then represented as a single PIRF, which is computed as an average of all descriptors associated with the PIRF. Particularly, PIRFs are robust against highly dynamical changes in scene: a single PIRF can be matched correctly against many features from many dynamical images. This paper also describes an approach to using these features for scene recognition. Recognition proceeds by matching an individual PIRF to a set of features from test images, with subsequent majority voting to identify a place with the highest matched PIRF. The PIRF system is trained and tested on 2000+ outdoor omnidirectional images and on COLD datasets. Despite its simplicity, PIRF offers a markedly better rate of recognition for dynamic outdoor scenes (ca. 90%) than the use of other features. Additionally, a robot navigation system based on PIRF (PIRF-Nav) can outperform other incremental topological mapping methods in terms of time (70% less) and memory. The number of PIRFs can be reduced further to reduce the time while retaining high accuracy, which makes it suitable for long-term recognition and localization.},
keywords={},
doi={10.1587/transinf.E93.D.2587},
ISSN={1745-1361},
month={September},}
부
TY - JOUR
TI - Position-Invariant Robust Features for Long-Term Recognition of Dynamic Outdoor Scenes
T2 - IEICE TRANSACTIONS on Information
SP - 2587
EP - 2601
AU - Aram KAWEWONG
AU - Sirinart TANGRUAMSUB
AU - Osamu HASEGAWA
PY - 2010
DO - 10.1587/transinf.E93.D.2587
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2010
AB - A novel Position-Invariant Robust Feature, designated as PIRF, is presented to address the problem of highly dynamic scene recognition. The PIRF is obtained by identifying existing local features (i.e. SIFT) that have a wide baseline visibility within a place (one place contains more than one sequential images). These wide-baseline visible features are then represented as a single PIRF, which is computed as an average of all descriptors associated with the PIRF. Particularly, PIRFs are robust against highly dynamical changes in scene: a single PIRF can be matched correctly against many features from many dynamical images. This paper also describes an approach to using these features for scene recognition. Recognition proceeds by matching an individual PIRF to a set of features from test images, with subsequent majority voting to identify a place with the highest matched PIRF. The PIRF system is trained and tested on 2000+ outdoor omnidirectional images and on COLD datasets. Despite its simplicity, PIRF offers a markedly better rate of recognition for dynamic outdoor scenes (ca. 90%) than the use of other features. Additionally, a robot navigation system based on PIRF (PIRF-Nav) can outperform other incremental topological mapping methods in terms of time (70% less) and memory. The number of PIRFs can be reduced further to reduce the time while retaining high accuracy, which makes it suitable for long-term recognition and localization.
ER -