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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In recent years, various shadow detection methods from a single image havebeen proposed and used in vision systems; however, most of them are notappropriate for the robotic applications due to the expensive time complexity.This paper introduces a fast shadow detection method using a deep learningframework, with a time cost that is appropriate for robotic applications. Inour solution, we first obtain a shadow prior map with the help of multi-classsupport vector machine using statistical features. Then, we use a semantic-aware patch-level Convolutional Neural Network that efficiently trains onshadow examples by combining the original image and the shadow prior map.Experiments on benchmark datasets demonstrate the proposed method significantlydecreases the time complexity of shadow detection, by one or two orders ofmagnitude compared with state-of-the-art methods, without losing accuracy.

Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network