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

Most existing methods for object segmentation in computer vision areformulated as a labeling task. This, in general, could be transferred to apixel-wise label assignment task, which is quite similar to the structure ofhidden Markov random field. In terms of Markov random field, each pixel can beregarded as a state and has a transition probability to its neighbor pixel, thelabel behind each pixel is a latent variable and has an emission probabilityfrom its corresponding state. In this paper, we reviewed several modern imagelabeling methods based on Markov random field and conditional random Field. Andwe compare the result of these methods with some classical image labelingmethods. The experiment demonstrates that the introduction of Markov randomfield and conditional random field make a big difference in the segmentationresult.

Image Labeling with Markov Random Fields and Conditional Random Fields