Improving Graph Cuts algorithm to transform sequence of stereo image to depth map

Recently, 3D display systems are getting considerable attentions not only from theater but also from home. 3D multimedia content development plays very important role in helping to setup a visual reality entertainment program. Lenticular Autostereoscopic display is one of the 3D-TV having the following advantages such as improving 3D viewing experience, supporting wider viewing angles for multiple viewers, and no requiring any special glasses. However, most of the current 3D movie and camera do not support the Autostereoscopic function. Therefore, we proposed a system that can transform the current 3D stereoscopic image sequence to the depth map sequence. These sequences can be warped into the multiplexed image by DIBR (Depth Image Based Rendering), and show with Autostereoscopic.Some recent techniques that transform the stereoscopic correspondence problem are based on Graph Cuts. They transform the matching problem to a minimization of a global energy function. However, it has been difficult to include high level information in the formulation of the Graph Cut. In this paper, we describe a new technique for generating depth map sequence from stereoscopic image sequence. We improve the Graph Cuts from pixel-based matching to region-based by using the Mean Shift 3D regions clustering to link the features of images before segmentation. And we also use the result of 3D regions clustering to assign depth values to time domain. After the sequence of depth map has been obtained, the DIBR method was used in transformation process. The experimental result shows that our system not only establishes a mechanism of depth transformation but also improves the accuracy and effectiveness on traditional Graph Cuts.

► Improve the Graph Cuts from pixel-based matching to region-based.
► Region-based matching process to deal with the data term of Graph Cuts.
► 3D Mean Shift algorithm for the smooth term of Graph Cuts.
► To improve the time-consuming and error estimation problems in Graph Cuts.
► A mechanism of depth transformation and improves the accuracy and effectiveness.