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

Statistical shape models are a useful tool in image processing and computervision. A Procrustres registration of the contours of the same shape istypically perform to align the training samples to learn the statistical shapemodel. A Procrustes registration between two contours with knowncorrespondences is straightforward. However, these correspondences are notgenerally available. Manually placed landmarks are often used forcorrespondence in the design of statistical shape models. However, determiningmanual landmarks on the contours is time-consuming and often error-prone. Onesolution to simultaneously find correspondence and registration is theIterative Closest Point (ICP) algorithm. However, ICP requires an initialposition of the contours that is close to registration, and it is not robustagainst outliers. We propose a new strategy, based on Dynamic Time Warping,that efficiently solves the Procrustes registration problem withoutcorrespondences. We study the registration performance in a collection ofdifferent shape data sets and show that our technique outperforms competingtechniques based on the ICP approach. Our strategy is applied to an ensemble ofcontours of the same shape as an extension of the generalized Procrustesanalysis accounting for a lack of correspondence.

Procrustes registration of two-dimensional statistical shape models without correspondences