Book chapter · Conference paper
Material-Based Segmentation of Objects
We present a data-driven proof of concept method for image-based semantic segmentation of objects based on their materials. We target materials with complex radiometric appearances, such as reflective and refractive materials, as their detection is particularly challenging in many modern vision systems.
Specifically, we select glass, chrome, plastic, and ceramics as these often appear in real-world settings. A large dataset of synthetic images is generated with the Blender 3D creation suite and the Cycles renderer. We use this data to fine-tune the pre-trained DeepLabv3+ semantic segmentation convolutional neural network.
The network performs well on rendered test data and, although trained with rendered images only, the network generalizes so that the four selected materials can be segmented from real photos.
| Language: | English |
|---|---|
| Publisher: | Springer |
| Year: | 2019 |
| Pages: | 152-163 |
| Proceedings: | 2019 Scandinavian Conference on Image Analysis |
| Series: | Lecture Notes in Computer Science |
| Journal subtitle: | 21st Scandinavian Conference, Scia 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings |
| ISBN: | 3030202046 , 3030202054 , 9783030202040 and 9783030202057 |
| ISSN: | 16113349 and 03029743 |
| Types: | Book chapter and Conference paper |
| DOI: | 10.1007/978-3-030-20205-7_13 |
| ORCIDs: | 0000-0002-8307-7411 , 0000-0002-7765-1747 , 0000-0002-6096-3648 , 0000-0002-5698-5983 , Stets, Jonathan Dyssel , Frisvad, Jeppe Revall and Dahl, Anders Bjorholm |
