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

The structural analysis of shape boundaries leads to the characterization ofobjects as well as to the understanding of shape properties. The literature ongraphs and networks have contributed to the structural characterization ofshapes with different theoretical approaches. We performed a study on therelationship between the shape architecture and the network topologyconstructed over the shape boundary. For that, we used a method for networkmodeling proposed in 2009. Firstly, together with curvature analysis, weevaluated the proposed approach for regular polygons. This way, it was possibleto investigate how the network measurements vary according to some specificshape properties. Secondly, we evaluated the performance of the proposed shapedescriptor in classification tasks for three datasets, accounting for bothreal-world and synthetic shapes. We demonstrated that not only degree relatedmeasurements are capable of distinguishing classes of objects. Yet, when usingmeasurements that account for distinct properties of the network structure, theconstruction of the shape descriptor becomes more computationally efficient.Given the fact the network is dynamically constructed, the number of iterationscan be reduced. The proposed approach accounts for a more robust set ofstructural measurements, that improved the discriminant power of the shapedescriptors.

An optimized shape descriptor based on structural properties of networks