基于Graph Cuts多特征选择的双目图像分割方法

Binocular Image Segmentation Based on Graph Cuts Multi-feature Selection

双目图像分割对后续立体目标合成与三维重建等应用至关重要。由于双目图像中包含场景深度信息,因此直接将单目图像分割方法应用于双目图像尚不能得到理想的分割结果。目前,大多数双目图像分割方法将双目图像的深度特征作为颜色特征的额外通道来使用,仅对颜色特征与深度特征做简单整合,未能充分利用图像的深度特征。文中基于多分类Graph Cuts框架,提出了一种交互式双目图像分割方法。该方法将颜色、深度和纹理等特征融合到一个图模型中,以更充分地利用不同特征信息。同时,在Graph Cuts框架中引入了特征空间邻域系统,增强了图像前景区域与背景区域内部像素点之间的关系,提高了分割目标的完整性。实验结果表明,所提方法有效提升了双目图像分割结果的精确度。

Binocular image segmentation is crucial for subsequent applications such as stereoscopic object synthesis and 3D reconstruction.Since binocular images contain scene depth information,it is difficult to obtain ideal segmentation results by applying monocular image segmentation methods to binocular images directly.At present,most binocular image segmentation methods use the depth feature of the binocular image as an additional channel for the color feature.Only the color feature and the depth feature are simply integrated,and the depth feature of the image cannot be fully utilized.Based on the multi-class Graph Cuts framework,this paper proposes an interactive binocular image segmentation method.Combining features such as color,depth and texture into a graph model can make full use of different feature information.At the same time,the feature space neighborhood system is introduced in the Graph Cuts framework,which enhances the relationship between the pixels in the foreground and background areas of the image,and improves the integrity of the segmentation target.Experimental results show that the proposed method improves the accuracy of binocular image segmentation results effectively.