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

This paper aims at developing an automatic algorithm for moth recognitionfrom trap images in real-world conditions. This method uses our previous workfor detection [1] and introduces an adapted classification step. Moreprecisely, SVM classifier is trained with a multi-scale descriptor, HistogramOf Curviness Saliency (HCS). This descriptor is robust to illumination changesand is able to detect and to describe the external and the internal contours ofthe target insect in multi-scale. The proposed classification method can betrained with a small set of images. Quantitative evaluations show that theproposed method is able to classify insects with higher accuracy (rate of95.8%) than the state-of-the art approaches.

Support Vector Machine (SVM) Recognition Approach adapted to Individual and Touching Moths Counting in Trap Images