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

In this paper, we propose a Self-aware Distance Transform (SDT) for efficient templatebased point feature tracking. The proposed SDT encapsulates the relationship between autocorrelation coefficients and the distance from the best match; therefore, it can be used to automatically determine the size of a search region in each point feature. The proposed SDT returns the expected distance between the predicted position and the best match from a statistical viewpoint, which guarantees a certain level of successful tracking depending on the cross-correlation at the predicted position. If the SDT returns a large expected distance due to the abrupt motion of a feature or inaccurate prediction, we progressively expand the search region on a hexagonal lattice while also using the SDT to reduce unnecessary computations. The performance of the proposed tracking method based on the SDT was verified experimentally in terms of its accuracy, robustness, and computational efficiency by comparing the proposed method to other tracking methods.

Efficient Point Feature Tracking based on Self-aware Distance Transform