State-of-the-art methods for semantic segmentation of images involve computationally intensive neural network architectures. Most of these methods are not adaptable to high-resolution image segmentation due to memory and other computational issues. Typical approaches in literature involve design of neural network architectures that can fuse global information from low-resolution images and local information from the high-resolution counterparts. However, architectures designed for processing high resolution images are unnecessarily complex and involve a lot of hyper parameters that can be difficult to tune. Also, most of these architectures require ground truth annotations of the high resolution images to train, which can be hard to obtain. In this article, we develop a robust pipeline based on mathematical morphological (MM) operators that can seamlessly extend any existing semantic segmentation algorithm to high resolution images. Our method does not require the ground truth annotations of the high resolution images. It is based on efficiently utilizing information from the low-resolution counterparts, and gradient information on the high-resolution images. We obtain high quality seeds from the inferred labels on low-resolution images using traditional morphological operators and propagate seed labels using a random walker to refine the semantic labels at the boundaries. We show that the semantic segmentation results obtained by our method beat the existing state-of-the-art algorithms on high-resolution images. We empirically prove the robustness of our approach to the hyper parameters used in our pipeline. Further, we characterize some necessary conditions under which our pipeline is applicable and provide an in-depth analysis of the proposed approach.

翻译：当前最先进的图像语义分割方法通常依赖计算密集型的神经网络架构。但由于内存及其他计算问题，大多数方法难以适应高分辨率图像分割。现有文献中的典型做法是设计能够融合低分辨率图像全局信息与高分辨率图像局部信息的神经网络架构。然而，针对高分辨率图像处理的架构往往过于复杂，且包含大量难以调优的超参数。此外，这些架构大多需要高分辨率图像的标注真值进行训练，而此类数据往往难以获取。本文提出一种基于数学形态学算子的鲁棒流水线，可将现有语义分割算法无缝扩展至高分辨率图像。本方法无需高分辨率图像的标注真值，其核心在于高效利用低分辨率图像信息及高分辨率图像的梯度信息。我们通过传统形态学算子从低分辨率图像的推理标签中获取高质量种子点，并采用随机游走算法传播种子标签以精细化语义标签边界。实验表明，本方法在高分辨率图像上的语义分割结果优于现有最先进算法。我们通过实证验证了本方法对所采用超参数的鲁棒性，进一步刻画了本流水线适用的若干必要条件，并对所提方法进行了深入分析。