Object-based RGBD image co-segmentation with mutex constraint
Object-based RGBD image co-segmentation with mutex constraint
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DOI：
10.1109/cvpr.2015.7299072
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
H. Fu;Dong Xu;Stephen Lin;Jiang Liu
中科院分区：
文献类型：
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作者：
H. Fu;Dong Xu;Stephen Lin;Jiang Liu
我们提出了一种基于对象的联合分割方法，该方法利用深度数据，并且能够正确处理缺少共同前景对象的噪声图像。对于 RGBD 图像，我们的方法利用深度通道通过提出的 RGBD 共显着图来增强对相似前景对象的识别，以及改进对类似对象区域的检测并提供基于深度的局部特征以进行区域比较。为了准确处理公共对象出现多次或少于一次的噪声图像，我们在完全连接的图结构中制定了共同分割以及防止不正确解决方案的互斥（互斥）约束。实验表明，这种具有互斥约束的基于对象的 RGBD 联合分割在 RGBD 联合分割数据集上的性能优于相关技术，同时可以有效处理噪声图像。此外，我们还表明，该方法还提供了与常规 RGB 图像上最先进的 RGB 联合分割技术相当的性能，并根据这些图像估计了深度图。
We present an object-based co-segmentation method that takes advantage of depth data and is able to correctly handle noisy images in which the common foreground object is missing. With RGBD images, our method utilizes the depth channel to enhance identification of similar foreground objects via a proposed RGBD co-saliency map, as well as to improve detection of object-like regions and provide depth-based local features for region comparison. To accurately deal with noisy images where the common object appears more than or less than once, we formulate co-segmentation in a fully-connected graph structure together with mutual exclusion (mutex) constraints that prevent improper solutions. Experiments show that this object-based RGBD co-segmentation with mutex constraints outperforms related techniques on an RGBD co-segmentation dataset, while effectively processing noisy images. Moreover, we show that this method also provides performance comparable to state-of-the-art RGB co-segmentation techniques on regular RGB images with depth maps estimated from them.