360도 실감형 미디어에서의 고해상도에 대한 특징을 활용한 특징점 추출 방법
An Efficient Feature Point Extraction Method for 360˚ Realistic Media Utilizing High Resolution Characteristics
0
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
In this paper, we propose a efficient feature point extraction method that can solve the problem of performance degradation by introducing a preprocessing process when extracting feature points by utilizing the characteristics of 360-degree realistic media. 360-degree realistic media is composed of images produced by two or more cameras and this image combining process is accomplished by extracting feature points at the edges of each image and combining them into one image if they cover the same area. In this production process, however, the stitching process where images are combined into one piece can lead to the distortion of non-seamlessness. Since the realistic media of 4K-class image has higher resolution than that of a general image, the feature point extraction and matching process takes much more time than general media cases.
Abstract
In this paper, we propose a efficient feature point extraction method that can solve the problem of performance degradation by introducing a preprocessing process when extracting feature points by utilizing the characteristics of 360-degree realistic media. 360-degree realistic media is composed of images produced by two or more cameras and this image combining process is accomplished by extracting feature points at the edges of each image and combining them into one image if they cover the same area. In this production process, however, the stitching process where images are combined into one piece can lead to the distortion of non-seamlessness. Since the realistic media of 4K-class image has higher resolution than that of a general image, the feature point extraction and matching process takes much more time than general media cases.
참고문헌 (13)
- Lowe, D.G, "Distinctive image features from scale-invari ant keypoints", IJCV 2004 (2004)
- P. Viola, "Rapid object detection using a boosted cascade of simple features", Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2001)
- H. Bay, "SURF:Speeded-up robust features", Computer vision and image Understanding, 110(3), pp.346~359 (2008)
- M. Calonder, "Brief: Binary robust independent elementary features", Computer Vision - ECCV, 6314, pp.778~792 (2010)
- Rainer Lienhart, "An Extended Set of Haar-like Features for Rapid Object Detection", IEEE ICIP 2002, 1, pp.900~903 (2002)
- E. Rosten, "Machine Learning for High-Speed Corner Detection", European Conference on Computer Vision ECCV, pp.430~443 (2006)
- D. Lowe, "Distinctive Image Features from Scale-Invari ant Keypoints", International Jounal of Computer Vision, 60, pp.91~110 (2004)
- Jin-Sung Kim, "A Study on Image Feature Point Extraction for Realistic Contents", Journal of The Korea Society of Computer and Information, 26(2), pp.385~386 (2018)
- Byung Woo Chung, "A Fast and Efficient Haar-Like Feature Selection Algorithm for Object Detection", Masters, pp.2013
- Jong Seong Kim, "A Study on the Auto-Threshold Value Decision Method of Region Merging for Minimization of Over-segmentation in Image", Masters, pp.2004
- Soo Woong Jung, "A study on the efficient face retrieval system using SURF-based feature extraction", Masters, pp.2012
- 허훈, "객체인식을 위한 FAST와 BRIEF 알고리즘 기반 FPGA 설계", 전기전자학회논문지, 17(2), pp.202~207 (2013), 한국전기전자학회[DOI]
- "Dark-programer"[URL]
