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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We propose a novel approach for image segmentation that combines NeuralOrdinary Differential Equations (NODEs) and the Level Set method. Our approachparametrizes the evolution of an initial contour with a NODE that implicitlylearns from data a speed function describing the evolution. In addition, forcases where an initial contour is not available and to alleviate the need forcareful choice or design of contour embedding functions, we propose aNODE-based method that evolves an image embedding into a dense per-pixelsemantic label space. We evaluate our methods on kidney segmentation (KiTS19)and on salient object detection (PASCAL-S, ECSSD and HKU-IS). In addition toimproving initial contours provided by deep learning models while using afraction of their number of parameters, our approach achieves F scores that arehigher than several state-of-the-art deep learning algorithms.

Neural ODEs for Image Segmentation with Level Sets