Name
Abstract | ||
|---|---|---|
In this paper, we propose a deep convolutional network architecture, called DOC, which detects object boundaries and estimates the occlusion relationships (i.e. which side of the boundary is foreground and which is background). Specifically, we first represent occlusion relations by a binary edge indicator, to indicate the object boundary, and an occlusion orientation variable whose direction specifies the occlusion relationships by a left-hand rule, see Fig. 1. Then, our DOC networks exploit local and non-local image cues to learn and estimate this representation and hence recover occlusion relations. To train and test DOC, we construct a large-scale instance occlusion boundary dataset using PASCAL VOC images, which we call the PASCAL instance occlusion dataset (PIOD). It contains 10,000 images and hence is two orders of magnitude larger than existing occlusion datasets for outdoor images. We test two variants of DOC on PIOD and on the BSDS ownership dataset and show they outperform state-of-the-art methods typically by more than 5AP. Finally, we perform numerous experiments investigating multiple settings of DOC and transfer between BSDS and PIOD, which provides more insights for further study of occlusion estimation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46448-0_33 | COMPUTER VISION - ECCV 2016, PT I |
Keywords | Field | DocType |
Object Boundary,Markov Random Field,Boundary Detection,Deep Network,Semantic Segmentation | Computer vision,Occlusion,Computer science,Markov random field,Network architecture,Exploit,Boundary detection,Artificial intelligence,Machine learning,Binary number | Conference |
Volume | ISSN | Citations |
9905 | 0302-9743 | 2 |
PageRank | References | Authors |
0.35 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Wang | 1 | 253 | 12.25 |
| Alan L. Yuille | 2 | 10339 | 1902.01 |