Exploiting visual context and consistency for semantic segmentation - Kang, Dang (2018)
This thesis studies the problem of semantic segmentation in both images and videos. It targets at addressing two challenges of semantic segmentation: robustness and consistency. First, assigning pixels to the corresponding semantic categories may be unreliable especially under adverse imaging conditions. Second, existing semantic segmentation algorithms often produce inconsistent labeling at region boundaries or across neighboring frames. Visual context and consistency cues are highly useful to address the two challenges; hence the objective of this thesis is to develop techniques that exploit these cues to enhance the performance of semantic segmentation. The thesis consists of the following three technical works.