영상에서의 분할정보를 사용한 스테레오 조밀 시차맵 생성
Computation of Stereo Dense Disparity Maps Using Region Segmentation
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
스테레오 비전은 시차가 있는 양안 영상으로부터 깊이맵에 해당하는 시차맵을 생성하고 시차맵과 카메라 정보로부터 3차원 구조를 복원하는 기법이다. 시차맵 생성은 정합비용을 계산하고, 전체 정합 비용을 최소화하여 시차를 계산하는 단계로 이루어진다. 본 논문에서는 스테레오 영상으로부터 빠르고 안정된 시차맵을 생성하기 위해서 후처리 과정으로 각 스캔라인에 대해서 분산을 이용하여 분할한 후에 분할 영역 정보를 사용하여 객체간 영역을 구분할 수 있도록 한다. 시차맵의 계산 시에 영역의 균일성 정보를 사용하면 잘못된 매치가 발생되지 않도록 억제할 수 있다. 조밀 시차맵을 생성하기 위해서는 시차 계산에 실패한 픽셀들에 대해서도 인접 픽셀의 값을 사용한 보간 기법을 통한 홀 메우기로 시차값을 계산하여 조밀한 시차맵이 형성되도록 한다. 실제 환경에서의 다양한 스테레오 영상에 대한 실험결과는 제안된 시차맵 생성과 홀을 메우는 방법이 기존의 방법보다 안정적이고 다양한 응용에 적용될 수 있음을 보여준다.
Abstract
Stereo vision is a fundamental method for measuring 3D structures by observing them from two cameras placed on different positions. In order to reconstruct 3D structures, it is necessary to create a disparity map from a pair of stereo images. To create a disparity map we compute the matching cost for each point correspondence and compute the disparity that minimizes the sum of the whole matching costs. In this paper, we propose a method to estimate a dense disparity map using region segmentation. We segment each scanline using region homogeneity properties. Using the segmented regions, we prohibit false matches in the stereo matching process. Disparities for pixels that failed in matching are filled by interpolating neighborhood disparities. We applied the proposed method to various stereo images of real environments. Experimental results showed that the proposed method is stable and potentially viable in practical applications.
참고문헌 (24)
- Han-Suh Koo, "An Area-Based Stereo Matching Using Adaptive Search Range and Window Size", Proceedings of the International Conference on Computational Science-Part II, pp.44~56 (2001)
- L. Di Stefano, "A Fast Area-Based Stereo Matching Algorithm", and Vision Computing, 22(12), pp.983~1005 (2004)
- Camillo J.Taylor, "Surface Reconstruction from Feature Based Stereo", IEEE International Conference on Computer Vision, pp.184~190 (2003)
- M. S. Lew, "Learning and Feature Selection in Stereo Matching", IEEE Transaction on Pattern Analysis and Machine Intelligence, 16(9), pp.869~881 (1994)
- S. B. Marapane, "Multi-Primitive Hierarchical (MPH) Stereo Analysis", IEEE Transaction on Pattern Analysis and Machine Intelligence, 16(3), pp.227~240 (1994)
- D. M. McKeown, "Hierarchical Waveform Matching: A New Feature-Based Stereo Technique", In Computer Vision and Pattern Recognition, pp.513~519 (1992)
- Daniel Scharstein, "A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms", International Journal of Computer Vision, 47(3), pp.7~42 (2002)
- P.Anandan, "A Computational Framework and An Algorithm for The Measurement of Visual Motion", International Journal of Computer Vision, 2(3), pp.283~310 (1989)
- Takeo Kanade, "Development of a Video-Rate Stereo Machine", Proceedings of the 1994 ARPA Image Understanding Workshop, pp.549~558 (1994)
- R. C. Bolles, "The JISCT Stereo Evaluation", DARPA, 93, pp.263~274 (1993)
- Aaron F. Bobick, "Large Occlusion Stereo", International Journal of Computer Vision, 33(3), pp.181~200 (1999)
- Sing Bing Kang, "Handling Occlusion in Dense Multi-View Stereo", In Computer Vision and Pattern Recognition, 1, pp.103~110 (2001)
- W.E.L.Grimson, "Computational Experiments with a Feature Based Stereo Algorithm", IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(1), pp.17~34 (1985)
- K.Prazdny, "Detection of Binocular Disparities", Readings in Computer Vision: issues problems principles and paradigms, pp.73~79 (1987)
- D.Scharstein, "Stereo Matching with Nonlinear Diffusion", International Journal of Computer Vision, 28(2), pp.155~174 (1998)
- D.Terzopoulos, "Regularization of Inverse Visual Problems Involving Discontinuities", IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(4), pp.413~424 (1986)
- C. L. Zitnick, "A Cooperative Algorithm for Stereo Matching and Occlusion Detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(7), pp.675~684 (2000)
- Y. Boykov, "Fast Approximate Energy Minimization via Graph Cuts", IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11), pp.1222~1239 (2001)
- S. Birchfield, "A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling", IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(4), pp.401~406 (1998)
- Kuk-Jin Yoon, "Adaptive Support-Weight Approach for Correspondence Search", IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4), pp.650~656 (2006)
- Z. Zivkovic, "On Matching Interest Regions Using Local Descriptors: Can an Information Theoretic Approach Help?", British Machine Vision Conference (2005)
- O.Veksler, "Stereo Correspondence with Compact Windows via Minimum Ratio Cycle", IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12), pp.1654~1660 (2002)
- O.Veksler, "Fast Variable Window for Stereo Correspondence using Integral Images", In IEEE Computer Vision and Pattern Recognition, 1, pp.556~561 (2003)
- S. Hernandez, "Region Merging using Homogeneity and Edge Integrity for Watershed-based Image Segmentation", Optical Engineering, 44, pp.1~14 (2005)