IEEE Trans Pattern Anal Mach Intell - KNN Matting.

Tópicos

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Resumo

This paper proposes to apply the nonlocal principle to general alpha matting for the simultaneous extraction of multiple image layers. The estimated alphas also satisfy the summation constraint. Our approach does not assume the local color-line model does not require sophisticated sampling or learning strategies, while generalizing well to any color or feature space in any dimensions, any number of alphas and layers at a pixel beyond two, and comes with an arguably simpler implementation which we have made publicly available. Our matting technique, aptly called KNN matting, capitalizes on the nonlocal principle by using K nearest neighbors (KNN) in matching nonlocal neighborhoods, and contributes a simple and fast algorithm that produces competitive results with sparse user markups. KNN matting has a closed-form solution that can leverage the preconditioned conjugate gradient method to produce an efficient implementation. Experimental evaluation on benchmark datasets indicates that our matting results are comparable to or of higher quality than state-of-the-art methods requiring more involved implementation. In this paper, we take the nonlocal principle beyond alpha estimation and extract overlapping image layers using the same Laplacian framework. Given the alpha value, our closed-form solution can be elegantly generalized to solve the multi-layer extraction problem.

Resumo Limpo

paper propos appli nonloc principl general alpha mat simultan extract multipl imag layer estim alpha also satisfi summat constraint approach assum local colorlin model requir sophist sampl learn strategi general well color featur space dimens number alpha layer pixel beyond two come arguabl simpler implement made public avail mat techniqu apt call knn mat capit nonloc principl use k nearest neighbor knn match nonloc neighborhood contribut simpl fast algorithm produc competit result spars user markup knn mat closedform solut can leverag precondit conjug gradient method produc effici implement experiment evalu benchmark dataset indic mat result compar higher qualiti stateoftheart method requir involv implement paper take nonloc principl beyond alpha estim extract overlap imag layer use laplacian framework given alpha valu closedform solut can eleg general solv multilay extract problem

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