Incorporating fuzzy-based methods to deep learning models for semantic segmentation - Lin, Qiao (2023)
This thesis focuses on improving the workflow of semantic segmentation through a combination of reducing model complexity, improving segmentation accuracy, and making semantic segmentation results more reliable and robust. Semantic segmentation refers to pixel-level classification, the objective of which is to classify each pixel of the input image into different categories. The process typically consists of three steps: model construction, training, and application. Thus, in this thesis, fuzzy-based techniques are utilized in the aforementioned three steps to improve semantic segmentation workflow .