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

1'0 effectively address the issue of multi-scene co-saliency detection , we proposed a novel model based on superpixel matching and cellular automata. First of all , we introduced an adjacent superpixel sets matching algorithm based on Hausdorff distance to achieve exact matching between image supperpixels. Comparing to the traditional superpixel matching algorithm , the new algorithm greatly improves the matching accuracy. In addition , we further proposed the 2-1ayer cellular automata via intra image and inter images to carry out the significant propagation of multiple images , thus exploit the intrinsic relevance of similar regions through interactions with neighbors in multi-scene. Experimental results demonstrate that our model outperforms state-of-the-art methods. Furthermore , the proposed methods is effi cient and robust.

基于超像素匹配的图像协同显著性检测 (Co-saliency Detection via Superpixel Matching).