E-resources
Peer reviewed
-
Wang, Hesheng; Wang, Jingchuan; Chen, Weidong; Xu, Lifei
Neurocomputing (Amsterdam), 01/2018, Volume: 275Journal Article
High-quality original image is very important in robot vision inspection system and illumination is a significant component that directly affect cameras optical imaging system and plays a decisive role on image quality. To guarantee camera imaging system for high-quality images and achieve automatic illumination control in the motion of inspection robot under dark environment, this paper proposes an optimal light intensity planning method based image quality analysis. It is mainly achieved by building a computational model to automatically predict optimal light intensity values for desired image quality when camera observation distances fluctuate. Before regression modeling, it is necessary to extract discriminative features representing image quality. We design feature extractor by deep learning instead of human engineers which required careful engineering and considerable domain expertise. Deep learning methods are representation-learning methods that allows a machine to be fed with raw data and to automatically discover the representations needed for regression or classification. Experimental results demonstrate the feasibility and efficiency of this method.
![loading ... loading ...](themes/default/img/ajax-loading.gif)
Shelf entry
Permalink
- URL:
Impact factor
Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.
Year | Impact factor | Edition | Category | Classification | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Select the library membership card:
If the library membership card is not in the list,
add a new one.
DRS, in which the journal is indexed
Database name | Field | Year |
---|
Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
---|
Source: Personal bibliographies
and: SICRIS
The material is available in full text. If you wish to order the material anyway, click the Continue button.