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

Capable of automated near real time superpixel detection and qualityassessment in an uncalibrated monitor typical red green blue (RGB) image,depicted in either true or false colors, an original low level computer vision(CV) lightweight computer program, called RGB Image Automatic Mapper (RGBIAM),is designed and implemented. Constrained by the Calibration Validation (CalVal)requirements of the Quality Assurance Framework for Earth Observation (QA4EO)guidelines, RGBIAM requires as mandatory an uncalibrated RGB image preprocessing first stage, consisting of an automated statistical model basedcolor constancy algorithm. The RGBIAM hybrid inference pipeline comprises: (I)a direct quantitative to nominal (QN) RGB variable transform, where RGB pixelvalues are mapped onto a prior dictionary of color names, equivalent to astatic polyhedralization of the RGB cube. Prior color naming is the deductivecounterpart of inductive vector quantization (VQ), whose typical VQ errorfunction to minimize is a root mean square error (RMSE). In the output multilevel color map domain, superpixels are automatically detected in linear timeas connected sets of pixels featuring the same color label. (II) An inversenominal to quantitative (NQ) RGB variable transform, where a superpixelwiseconstant RGB image approximation is generated in linear time to assess a VQerror image. The hybrid direct and inverse RGBIAM QNQ transform is: (i) generalpurpose, data and application independent. (ii) Automated, i.e., it requires nouser machine interaction. (iii) Near real time, with a computational complexityincreasing linearly with the image size. (iv) Implemented in tile streamingmode, to cope with massive images. Collected outcome and process qualityindicators, including degree of automation, computational efficiency, VQ rateand VQ error, are consistent with theoretical expectations.

Automated Linear-Time Detection and Quality Assessment of Superpixels in Uncalibrated True- or False-Color RGB Images