Paper Contents
Abstract
Semantic image segmentation is a crucial aspect of image processing and computer vision, finding applications in various domains such as medicine and intelligent transportation. This paper reviews both traditional and recent Deep Neural Network (DNN) methods for semantic segmentation. Traditional methods and datasets are briefly summarized, followed by a comprehensive investigation of DNN methods across eight aspects. These aspects include fully convolutional networks, upsampling techniques, joint methods with Conditional Random Fields (CRF), dilated convolution approaches, advancements in backbone networks, pyramid methods, multi-level and multi-stage methods, and various supervised, weakly-supervised, and unsupervised techniques
Copyright
Copyright © 2024 Swathi M. This is an open access article distributed under the Creative Commons Attribution License.
