Patent · US Active

Training constrained deconvolutional networks for road scene semantic segmentation

US9916522B2 · kind B2 · utility

16Cited by

2References

20Claims

0Family size

Assignee

Kabushiki Kaisha Toshiba · JP

Inventors

German Ros Sanchez · Mountain View, US
Simon A. I. Stent · Cambridge, US
Pablo Alcantarilla · Cambridge, GB

Key dates

Filing date	Apr 5, 2016
Grant date	Mar 13, 2018
Priority date	—
Expiry date	Aug 12, 2036

Classification

Technology area (CPC G)Physics
CPC primaryG06T2207/30252
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

A source deconvolutional network is adaptively trained to perform semantic segmentation. Image data is then input to the source deconvolutional network and outputs of the S-Net are measured. The same image data and the measured outputs of the source deconvolutional network are then used to train a target deconvolutional network. The target deconvolutional network is defined by a substantially fewer numerical parameters than the source deconvolutional network.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.