Name
Abstract | ||
|---|---|---|
Semantic segmentation of traffic scenes has potential applications in intelligent transportation systems. Deep learning techniques can improve segmentation accuracy, especially when the information from depth maps is introduced. However, little research has been done on the application of depth maps to the segmentation of traffic scene. In this paper, we propose a method for semantic segmentation ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TITS.2017.2724138 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
Image segmentation,Semantics,Machine learning,Training,Decoding,Feature extraction,Network architecture | Computer vision,Scale-space segmentation,Pattern recognition,Convolutional neural network,Computer science,Segmentation,Network architecture,Segmentation-based object categorization,Feature extraction,Image segmentation,Artificial intelligence,Deep learning | Journal |
Volume | Issue | ISSN |
19 | 5 | 1524-9050 |
Citations | PageRank | References |
7 | 0.50 | 0 |
Authors | ||
5 | ||