Abstract
본 논문은 손 형상 인식을 위한 보다 안정적이며 조명 변화와 회전에 강인하게 손 영역을 검출하며, 계산의 효율성과 검출 성능을 동시에 만족시키는 강인한 검출 알고리즘에 대해 제안한다. 제안한 알고리즘은 단일 카메라 환경에서 손 형상을 입력정보로 사용하여 전처리 과정을 거쳐 손 영역만을 분할한 후 자기조직화 특징 지도(SOFM: Self Organized Feature Map) 알고리즘을 이용하여 손 형상을 인식하게 된다. 그러나 조명 변화에 민감하고 자유도가 큰 손 영역을 정확히 인식하기란 쉽지 않으며 오차 범위도 크기 때문에 본 논문에서는 인식률을 높이기 위해 각각의 손 형상에 대한 회전 정보를 데이터베이스화 한 후 주성분 분석을 적용하여 군집화 함으로서 인식오차를 줄였다. 또한 차원 축소로 인해 많은 계산 량이 요구되지 않기 때문에 실시간 인식 시간도 줄일 수 있었다. This study proposed a robust detection algorithm. It detects hands more stably with respect to changes in light and rotation for the identification of a hand shape. Also it satisfies both efficiency of calculation and the function of detection. The algorithm proposed segmented the hand area through pre-processing using a hand shape as input information in an environment with a single camera and then identified the shape using a Self Organized Feature Map(SOFM). However, as it is not easy to exactly recognize a hand area which is sensitive to light, it has a large degree of freedom, and there is a large error bound, to enhance the identification rate, rotation information on the hand shape was made into a database and then a principal component analysis was conducted. Also, as there were fewer calculations due to the fewer dimensions, the time for real-time identification could be decreased.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.