본 논문에서는 키넥트 센서로부터 획득한 관절 정보를 이용하여 손 제스처를 인식하는 방법을 나타낸다. 관절 정보에 대한 관찰열을 표현하기 위한 특징으로 방향 변형에 강인한 다각도 결합 히스토그램 특징을 제안한다. 제안한 특징은 다양한 각도의 양자화 레벨을 갖는 여러 개의 각도 히스토그램들을 결합함으로써, 사람 및 환경에 따라 발생할 수 있는 제스처의 방향 변형에 강인하게 제스처를 표현한다. 또한, 다각도 결합 히스토그램으로 표현된 제스처 관찰열은 랜덤 결정 포레스트 분류기와 잘 겹합되어 높은 성능으로 제스처의 클래스를 인식한다. 키넥트 센서로부터 획득한 정적 및 동적 타입의 손 제스처 데이터셋에서 실험을 진행하였고, 다른 제스처 특징 및 분류기를 갖는 방법과의 인식 성능 비교를 통해 제안하는 방법의 우수함을 입증하였다.
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