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

A new method bused on MLE-OED is proposed for unsupervised image segmentation of multiple objects which have fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function proposed to implement image segmentation bused on the image’s local spatial information and global intensity distribution properties . The loss function consists of two terms: a local content Jitting term, which optimizes the entropy distribution, and a global statistical Jitting term, which maximizes the likelihood of the parameters for the given data. The proposed segmentation method was vulidated by simulated and real examples. Its performance in the experiments is better than those of two popular methods.

Multiple Objects Segmentation Based on Maximum-Likelihood Estimation and Optimum Entropy-Distribution (MLE-OED)