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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present a novel approach for inspecting variable data prints (VDP) with anultra-low false alarm rate (0.005%) and potential applicability to otherreal-world problems. The system is based on a comparison between two images: areference image and an image captured by low-cost scanners. The comparison taskis challenging as low-cost imaging systems create artifacts that mayerroneously be classified as true (genuine) defects. To address this challengewe introduce two new fusion methods, for change detection applications, whichare both fast and efficient. The first is an early fusion method that combinesthe two input images into a single pseudo-color image. The second, calledChange-Detection Single Shot Detector (CD-SSD) leverages the SSD by fusingfeatures in the middle of the network. We demonstrate the effectiveness of theproposed deep learning-based approach with a large dataset from real-worldprinting scenarios. Finally, we evaluate our models on a different domain ofaerial imagery change detection (AICD). Our best method clearly outperforms thestate-of-the-art baseline on this dataset.

A Novel Inspection System For Variable Data Printing Using Deep Learning