(A) feature vector matching processor with neuro-fuzzy spatio-temporal database cache = 뉴로-퍼지 및 시공간 국지성 기반 캐쉬를 내장한 고성능 특징 벡터 프로세서

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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.
Advisors
Yoo, Hoi-Junresearcher유회준researcher
Description
한국과학기술원 :전기및전자공학과,
Publisher
한국과학기술원
Issue Date
2013
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학과, 2013.2 ,[iii, 34 p. :]

Keywords

object recognition; feature vector matching; approximate nearest neighbor; neuro-fuzzy; spatio temporal locality; 물체 인식; 벡터 매칭; 뉴로-퍼지; 시공간 국지성

URI
http://hdl.handle.net/10203/221769
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=657353&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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