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

The present work investigates the segmentation of textures by formulating itas a strongly convex optimization problem, aiming to favor piecewise constancyof fractal features (local variance and local regularity) widely used to modelreal-world textures in numerous applications very different in nature. Twoobjective functions combining these two features are compared, referred to asjoint and coupled, promoting either independent or co-localized changes inlocal variance and regularity. To solve the resulting convex nonsmoothoptimization problems, because the processing of large size images anddatabases are targeted, two categories of proximal algorithms (dualforward-backward and primal-dual), are devised and compared. An in-depth studyof the objective functions, notably of their strong convexity, memory andcomputational costs, permits to propose significantly accelerated algorithms. Aclass of synthetic models of piecewise fractal texture is constructed andstudied. They enable, by means of large-scale Monte-Carlo simulations, toquantify the benefits in texture segmentation of combining local regularity andlocal variance (as opposed to regularity only) while using strong-convexityaccelerated primal-dual algorithms. Achieved results also permit to discuss thegains/costs in imposing co-localizations of changes in local regularity andlocal variance in the problem formulation. Finally, the potential of theproposed approaches is illustrated on real-world textures taken from a publiclyavailable and documented database.

Strongly Convex Optimization for Joint Fractal Feature Estimation and Texture Segmentation