Name
Abstract | ||
|---|---|---|
In this paper we tackle the problem of instance-level segmentation and depth ordering from a single monocular image. Towards this goal, we take advantage of convolutional neural nets and train them to directly predict instance-level segmentations where the instance ID encodes the depth ordering within image patches. To provide a coherent single explanation of an image we develop a Markov random field which takes as input the predictions of convolutional neural nets applied at overlapping patches of different resolutions, as well as the output of a connected component algorithm. It aims to predict accurate instance-level segmentation and depth ordering. We demonstrate the effectiveness of our approach on the challenging KITTI benchmark and show good performance on both tasks. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICCV.2015.300 | ICCV |
Field | DocType | Volume |
Computer vision,Scale-space segmentation,Pattern recognition,Range segmentation,Markov random field,Computer science,Segmentation,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Connected component,Artificial neural network | Journal | abs/1505.03159 |
Issue | ISSN | Citations |
1 | 1550-5499 | 40 |
PageRank | References | Authors |
1.52 | 32 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ziyu Zhang | 1 | 112 | 10.19 |
| Alexander G. Schwing | 2 | 696 | 51.78 |
| Sanja Fidler | 3 | 2087 | 116.71 |
| Raquel Urtasun | 4 | 6810 | 304.97 |