English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

An image fusion metric is commonly used to evaluate a fusion scheme because subjective evaluation cannot work in an intelligent manufacturing information system. In this study, an objective image fusion metric based on a log-Gabor filter (LGIMF) is presented. This metric can be calculated in five steps: (1) filtering the source and fused images into sub-bands, (2) constructing an ideal synthesis image by applying the maximization principle from the sub-band of the source images, (3) capturing the variation information between the real fused image and the ideal synthesis image in each sub-band, (4) measuring the sub-band fusion visual information by using the signalto- noise ratio model, and (5) weighting the sub-band fusion visual information to determine the overall quality. In our experiment, the proposed fusion metric is compared with other well-known metrics by using a subjective test dataset. We found that the LGIMF was more consistent in subjective perception compared with the other metrics.

A Novel Image Fusion Metric for Intelligent Manufacturing Information System