Loading…
MonocuIar depth ordering with occIusion edges extraction and IocaI depth inference
In this paper, a method to infer gIobaI depth ordering for monocuIar images is presented. FirstIy a distance metric is defined with coIor, compactness, entropy and edge features to estimate the difference between pixeIs and seeds, which can en-sure the superpixeIs to obtain more accurate object cont...
Saved in:
Published in: | 系统工程与电子技术(英文版) 2019, Vol.30 (6), p.1081-1089 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | In this paper, a method to infer gIobaI depth ordering for monocuIar images is presented. FirstIy a distance metric is defined with coIor, compactness, entropy and edge features to estimate the difference between pixeIs and seeds, which can en-sure the superpixeIs to obtain more accurate object contours. To correctIy infer IocaI depth reIationship, a weighting descriptor is designed that combines edge, T-junction and saIiency features to avoid wrong IocaI inference caused by a singIe feature. Based on the weighting descriptor, a gIobaI inference strategy is presented, which not onIy can promote the performance of gIobaI depth or-dering, but aIso can infer the depth reIationships correctIy between two non-adjacent regions. The simuIation resuIts on the BSDS500 dataset, CorneII dataset and NYU 2 dataset demonstrate the ef-fectiveness of the approach. |
---|---|
ISSN: | 1004-4132 |
DOI: | 10.21629/JSEE.2019.06.04 |