DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Yoo, Hoi-Jun | - |
dc.contributor.advisor | 유회준 | - |
dc.contributor.author | Hong, Injoon | - |
dc.contributor.author | 홍인준 | - |
dc.date.accessioned | 2017-03-29T02:38:28Z | - |
dc.date.available | 2017-03-29T02:38:28Z | - |
dc.date.issued | 2013 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=657353&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/221769 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학과, 2013.2 ,[iii, 34 p. :] | - |
dc.description.abstract | A feature vector matching in object recognition is the process of finding nearest neighbor database vec-tor for a given feature vector. Since it needs lots of required external bandwidth, it becomes the main bottleneck of real-time object recognition. To reduce the required external bandwidth, the proposed feature vector matching processor utilizes spatio-temporal locality of nearest neighbor database vector. In video environment, the majority of the nearest neighbor vectors are commonly founded in previous frames at similar location. To support the spatio-temporal locality of nearest neighbor vector, a special cache for feature vector matching, namely, Spatio-Temporal Data-base Cache (STDB Cache) is newly proposed. In addition, to reduce matching error induced from the spatio-temporal locality method, mixed-mode neuro-fuzzy cache controller is proposed. As a result, the proposed feature vector matching processor achieves 125,582 vec/s throughput and 95.1% matching accuracy, which are 2.02x and 1.32x higher than the state-of-the-art respectively. Therefore, the proposed feature vector matching processor achieves the most efficient throughput (vec/s o accuracy) enabling real-time object recognition for VGA 30fps video streams. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | object recognition | - |
dc.subject | feature vector matching | - |
dc.subject | approximate nearest neighbor | - |
dc.subject | neuro-fuzzy | - |
dc.subject | spatio temporal locality | - |
dc.subject | 물체 인식 | - |
dc.subject | 벡터 매칭 | - |
dc.subject | 뉴로-퍼지 | - |
dc.subject | 시공간 국지성 | - |
dc.title | (A) feature vector matching processor with neuro-fuzzy spatio-temporal database cache | - |
dc.title.alternative | 뉴로-퍼지 및 시공간 국지성 기반 캐쉬를 내장한 고성능 특징 벡터 프로세서 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학과, | - |
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