Semantic Segmentation Based on Depth Background Blur

作者：Hao Li, Changjiang Liu, Anup Basu · 发表于：Applied Sciences · 年份：2022 · DOI：10.3390/app12031051 · 被引用次数：6 · 研究领域：Advanced Neural Network Applications、Advanced Image and Video Retrieval Techniques、Remote Sensing and LiDAR Applications

Deep convolutional neural networks (CNNs) are effective in image classification, and are widely used in image segmentation tasks. Several neural netowrks have achieved high accuracy in segementation on existing semantic datasets, for instance PASCAL VOC, CamVid, and Cityscapes. However, there are nearly no studies on semantic segmentation from the perspective of a dataset itself. In this paper, we analyzed the characteristics of datasets, and proposed a novel experimental strategy based on bokeh to weaken the impact of futile background information. This crucial bokeh module processed each image in the inference phase by selecting an opportune fuzzy factor σ, so that the attention of our network can focus on the categories of interest. Some networks based on fully convolutional networks (FCNs) were utilized to verify the effectiveness of our method. Extensive experiments demonstrate that our approach can generally improve the segmentation results on existing datasets, such as PASCAL VOC 2012 and CamVid.