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

This paper describes a line-based classification method, which labels TLS point clouds into vertical object, ground, tree and low objects.A local classifier implements labeling task on individual site independently of its neighborhood, the inference of which often suffers fromsimilar local appearance across different object classes. In this paper, we describe an approach using contextual information as postclassificationimprovement to a local generative classifier. The contextual information is expected to compensate for ambiguity in objects'visual appearance. A generative classifier is produced using Gaussian Mixture Model (GMM), model parameters of which are iterativelyoptimized with Expectation-Maximization (EM). The model we use to incorporate contextual information is the Conditional RandomField (CRF), which improves the classification results obtained from GMM-EM classifier by incorporating neighborhood interactionsamong labeled objects as well as local appearance. The proposed method was validated with three TLS datasets acquired from RIEGLLMS-Z390i scanner using cross validation

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Line-based Classification of Terrestrial Laser Scanning Data using Conditional Random Field