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

The Potts model is frequently used to describe the behavior of image classes,since it allows to incorporate contextual information linking neighboringpixels in a simple way. Its isotropic version has only one real parameter beta,known as smoothness parameter or inverse temperature, which regulates theclasses map homogeneity. The classes are unavailable, and estimating them iscentral in important image processing procedures as, for instance, imageclassification. Methods for estimating the classes which stem from a Bayesianapproach under the Potts model require to adequately specify a value for beta.The estimation of such parameter can be efficiently made solving the PseudoMaximum likelihood (PML) equations in two different schemes, using the prior orthe posterior model. Having only radiometric data available, the first schemeneeds the computation of an initial segmentation, while the second uses boththe segmentation and the radiometric data to make the estimation. In thispaper, we compare these two PML estimators by computing the mean square error(MSE), bias, and sensitivity to deviations from the hypothesis of the model. Weconclude that the use of extra data does not improve the accuracy of the PML,moreover, under gross deviations from the model, this extra informationintroduces unpredictable distortions and bias.

When Data do not Bring Information: A Case Study in Markov Random Fields Estimation