키넥트로부터 획득된 깊이 영상의 확장을 위한 GPU 기반 채움 기법

• 영문명: GPU Based Filling Methods for Expansion of Depth Image Captured by Kinect
• 발행기관: 한국컴퓨터게임학회
• 저자명: 송민호(Min-Ho Song) 류관희(Kwan-Hee Yoo)
• 간행물 정보: 『한국컴퓨터게임학회논문지』제29권 1호, 47~54쪽, 전체 8쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2016.03.30

영문 초록

Three-dimensional(3D) display technique is widely used in our daily life. Especially, to product augmented game contents which can interact with users, it is necessary to get high quality resolution image data to reconstruct 3D model more exquisitely. In this paper, we tried to expand depth image captured by Kinect using various interpolation methods(nearest neighbor, bilinear, bicubic) to adapt it to the size of original Kinect color image. To measure the quality of expanded depth image compared to original depth image, we used PSNR(Peak Signal-to-noise ratio) index. Besides, we implemented GPU parallel processing algorithm with OpenCL to interpolate a large amount of image data rapidly. As a result of the experiment, a bicubic interpolation method made an accurate depth image although it had a long time.

목차

1. 서론

2. 관련연구 및 문제점

3. GPU 기반 확장된 깊이영상의 채움 기법

4. 구현 및 실험 결과

5. 결 론

키워드

Image Expansion Interpolation GPU parallel processing OpenCL