| Full text | |
| Author(s): |
Watanabe, Thomio
;
Wolf, Denis F.
;
IEEE
Total Authors: 3
|
| Document type: | Journal article |
| Source: | 2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19); v. N/A, p. 6-pg., 2019-01-01. |
| Abstract | |
The instance segmentation problem intends to precisely detect and delineate objects in images. Most of the current solutions rely on deep convolutional neural networks but despite this fact proposed solutions are very diverse. Some solutions approach the problem as a network problem, where they use several networks or specialize a single network to solve several tasks. A different approach tries to solve the problem as an annotation problem, where the instance information is encoded in a mathematical representation. This work proposes a solution based in the DCME technique to solve the instance segmentation with a single segmentation network. Different from others, the segmentation network decoder is not specialized in a multi-task network. Instead, the network encoder is repurposed to classify image objects, reducing the computational cost of the solution. (AU) | |
| FAPESP's process: | 15/26293-0 - Autonomous vehicles multi obstacle tracking with sensor fusion. |
| Grantee: | Thomio Watanabe |
| Support Opportunities: | Scholarships in Brazil - Doctorate |