In this paper, an automatic RGBD object segmentation method is described. The method integrates depth feature with the cues from RGB images and then uses maximal similarity based region merging (MSRM) method to obtain the segmentation results. Firstly, the depth information is fused to the simple linear iterative clustering (SLIC) method so as to produce superpixels whose boundaries are well adhered to the edges of the natural image. Meanwhile, the depth prior is also incorporated into the saliency estimation, which helps a more accurate localization of representative object and background seeds. By introducing the depth cue into the region merging rule, the maximal geometry weighted similarity (MGWS) is considered, and the resulting segmentation framework has the ability to handle the complex image with similar colour appearance between object and background. Extensive experiments on public RGBD image datasets show that our proposed approach can reliably and automatically provide very promising segmentation results.

中文翻译：

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基于MSRM框架集成深度值的自动RGBD对象分割

在本文中，描述了一种自动 RGBD 对象分割方法。该方法将深度特征与来自RGB图像的线索相结合，然后使用基于最大相似度的区域合并（MSRM）方法获得分割结果。首先，将深度信息融合到简单线性迭代聚类（SLIC）方法中，以产生边界很好地附着在自然图像边缘的超像素。同时，深度先验也被纳入显着性估计中，有助于更准确地定位代表对象和背景种子。通过将深度线索引入区域合并规则，考虑最大几何加权相似度（MGWS），并且由此产生的分割框架能够处理对象和背景之间具有相似颜色外观的复杂图像。在公共 RGBD 图像数据集上进行的大量实验表明，我们提出的方法可以可靠且自动地提供非常有希望的分割结果。