In this study, the authors focus on the task of leaf segmentation under different imaging conditions (e.g. backgrounds and shadows). A new method - robust random walk (RW) is proposed to propagate the prior of user's specified pixels. Specifically, they first employ RWs to take the relationship of pairwise pixels into consideration. A superpixel-consistent constraint is added to make the edges of segmentation smooth. Owing to the effect of illumination, some parts of a leaf surface are brighter than others and it may further harm the subsequent label propagation. To address this problem, they learn a common subspace by taking into account the illumination of local and non-local pixels. By doing so, it has good adaptability to process noise interfering and non-uniform illumination. In addition, since RW only considers the pairwise relationship of pixels, it will be sensitive to the specified and connected pixels. Thus, they further employ a log-likelihood ratio to predict the probability of a pixel belonging to the background and use it to guide the label propagation. Based on the proposed method, they can obtain a smoothed and robust leaf segmentation. Experimental results on unconstrained leaf images demonstrate the efficiency of their algorithm.

中文翻译：

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鲁棒的随机游动进行叶分割

在这项研究中，作者专注于不同成像条件（例如背景和阴影）下的叶片分割任务。提出了一种新的方法-鲁棒随机游走（RW）来传播用户指定像素的先验。具体来说，他们首先采用RW来考虑成对像素的关系。添加了超像素一致的约束以使分割的边缘平滑。由于照明的作用，叶片表面的某些部分比其他部分更亮，并且可能进一步损害随后的标签传播。为了解决这个问题，他们通过考虑局部和非局部像素的照明来学习公共子空间。这样，它对处理噪声干扰和照明不均匀具有良好的适应性。此外，由于RW仅考虑像素的成对关系，因此它将对指定的像素和连接的像素敏感。因此，他们进一步采用对数似然比来预测像素属于背景的概率，并使用它来指导标签传播。基于所提出的方法，他们可以获得平滑且鲁棒的叶子分割。无约束叶图像的实验结果证明了其算法的有效性。