영역 분할 기법과 경계 보존 변이 평활화를 이용한 스테레오 영상의 변이 추정

Disparity Estimation using a Region-Dividing Technique and Edge-preserving Regularization

  • 김한성 (연세대학교 전기전자공학과) ;
  • 손광훈 (연세대학교 전기전자공학과)
  • 발행 : 2004.11.01

초록

본 논문에서는 스테레오 영상으로부터 자연스러우면서도 정확한 변이 정보를 추출하기 위한 변이 추정 알고리듬을 제안한다. 제안된 알고리듬은 영역 분할 기법을 이용한 계층적 변이 추정부와 편미분 방정식(PDE: Partial Differential Equation)을 이용한 에너지 기반 경계 보존 변이 평활화부로 구성되어 있다. 제안된 계층적 변이 추정 기법은 빠르면서도 신뢰도 있는 변이를 제공하며, 이러한 변이장은 정확도와 평활화도를 함께 고려한 에너지 모델의 최소화 기법에 의해 자연스럽고 정밀한 최종 변이장으로 추출된다. 에너지 모델의 최소화 과정은 대응되는 Euler-Lagrange 방정식으로 변형되어 유한차분법(FDM: Finite difference Method)을 이용한 근사화를 통해 구현된다. 실험을 통해 제안된 변이 추정 기법은 다양한 환경의 영상에 대해서도 자연스러우면서도 정확하고, 경계가 잘 보존된 변이를 추정해 낼 수 있음을 검증하였다.

We propose a hierarchical disparity estimation algorithm with edge-preserving energy-based regularization. Initial disparity vectors are obtained from downsampled stereo images using a feature-based region-dividing disparity estimation technique. Dense disparities are estimated from these initial vectors with shape-adaptive windows in full resolution images. Finally, the vector fields are regularized with the minimization of the energy functional which considers both fidelity and smoothness of the fields. The first two steps provide highly reliable disparity vectors, so that local minimum problem can be avoided in regularization step. The proposed algorithm generates accurate disparity map which is smooth inside objects while preserving its discontinuities in boundaries. Experimental results are presented to illustrate the capabilities of the proposed disparity estimation technique.

키워드

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