메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술대회자료
저자정보
Hirokazu Madokoro (Akita Prefectural University) Shinya Ueda (Akita Prefectural University) Kazuhito Sato (Akita Prefectural University)
저널정보
제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2018
발행연도
2018.10
수록면
265 - 270 (6page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
This paper presents a semantic scene recognition method from indoor areal time-series images obtained using a micro air vehicle (MAV). Using category maps, topologies of image features are mapped into a low-dimensional space based on competitive and neighborhood learning. The proposed method comprises two phases: a codebook feature description phase and a recognition phase using category maps. For the former phase, codebooks are created automatically as visual words using self-organizing maps (SOMs) after extracting part-based local features using a part-based descriptor from time-series scene images. For the latter phase, category maps are created using counter propagation networks (CPNs) with extraction of category boundaries using a unified distance matrix (U-Matrix). With manual MAV operation, we obtained areal time-series image datasets of five sets for two flight routes: a round flight route and a zigzag flight route. The experimentally obtained results with leave-one-out cross-validation (LOOCV) for datasets divided with 10 zones revealed respective mean recognition accuracies for the round flight datasets and zigzag flight datasets of 71.7% and 65.5%. The created category maps addressed the complexity of scenes because of segmented categories in both flight datasets.

목차

Abstract
1. INTRODUCTION
2. RELATED STUDIES
3. SEMANTIC SCENE RECOGNITION METHOD
4. EVALUATION EXPERIMENT
5. CONCLUSION
REFERENCES

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2018-003-003538310