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

Human skin detection in images is a widely studied topic of Computer Visionfor which it is commonly accepted that analysis of pixel color or local patchesmay suffice. This is because skin regions appear to be relatively uniform andmany argue that there is a small chromatic variation among different samples.However, we found that there are strong biases in the datasets commonly used totrain or tune skin detection methods. Furthermore, the lack of contextualinformation may hinder the performance of local approaches. In this paper wepresent a comprehensive evaluation of holistic and local Convolutional NeuralNetwork (CNN) approaches on in-domain and cross-domain experiments and comparewith state-of-the-art pixel-based approaches. We also propose a combination ofinductive transfer learning and unsupervised domain adaptation methods, whichare evaluated on different domains under several amounts of labelled dataavailability. We show a clear superiority of CNN over pixel-based approacheseven without labelled training samples on the target domain. Furthermore, weprovide experimental support for the counter-intuitive superiority of holisticover local approaches for human skin detection.

Domain adaptation for holistic skin detection