Abstract
In this thesis, a visual analysis approach based on computer vision algorithm is proposed to estimate the interior layout from single input image. In the proposed system, the interior space can be viewed as a three-dimensional box which includes ceiling, floor, and walls. The regions corresponding to different surfaces can be calculated by projecting the 3D box into two-dimensional image. This thesis utilizes the consistency of coplanar lines and the boundary edges between different surfaces (ceiling, floor, and walls) to design a cost function. The rotation, translation, and deformation parameters of the interior layout can be estimated by a cost function minimization process. In the experimental results, an adaptive weight parameter of the cost function is examined. The prior knowledge of real-world testing image can improve the accuracy dramatically with an intelligent weight adjustment method. Furthermore, a simulation experiment based on large-scale testing data is exploited to prove the robustness and stability of the proposed system. Finally, the proposed system also examined various real testing images, which include new collected data and the indoor scene dataset in public domain, to demonstrate the accuracy and robustness of the proposed method.