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

This paper focuses on the statistical analysis of shapes of data objectscalled shape graphs, a set of nodes connected by articulated curves witharbitrary shapes. A critical need here is a constrained registration of points(nodes to nodes, edges to edges) across objects. This, in turn, requiresoptimization over the permutation group, made challenging by differences innodes (in terms of numbers, locations) and edges (in terms of shapes,placements, and sizes) across objects. This paper tackles this registrationproblem using a novel neural-network architecture and involves an unsupervisedloss function developed using the elastic shape metric for curves. Thisarchitecture results in (1) state-of-the-art matching performance and (2) anorder of magnitude reduction in the computational cost relative to baselineapproaches. We demonstrate the effectiveness of the proposed approach usingboth simulated data and real-world 2D and 3D shape graphs. Code and data willbe made publicly available after review to foster research.

Shape-Graph Matching Network (SGM-net): Registration for Statistical Shape Analysis