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This paper is concerned with applications of the theory of approximation and interpolation based on compensated convex transforms developed in [K. Zhang, E. Crooks, and A. Orlando, SIAM J. Math. Anal., 48 (2016), pp. 4126--4154]. We apply our methods to (i) surface reconstruction starting from the knowledge of finitely many level sets (or ''contour lines"); (ii) scattered data approximation; (iii) image inpainting. For (i) and (ii) our methods give interpolations. For the case of finite sets (scattered data), in particular, our approximations provide a natural triangulation and piecewise affine interpolation. Prototype examples of explicitly calculated approximations and inpainting results are presented for both finite and compact sets. We also show numerical experiments for applications of our methods to high density salt & pepper noise reduction in image processing, for image inpainting, and for approximation and interpolations of continuous functions sampled on finitely many level sets and on scattered points. [ABSTRACT FROM AUTHOR]

We introduce Lipschitz continuous and C1,1 geometric approximation and interpolation methods for sampled bounded uniformly continuous functions over compact sets and over complements of bounded open sets in Rn by using compensated convex transforms. Error estimates are provided for the approximations of bounded uniformly continuous functions, of Lipschitz functions, and of C1,1 functions. We also prove that our approximation methods, which are differentiation and integration free and not sensitive to sample type, are stable with respect to the Hausdorff distance between samples. [ABSTRACT FROM AUTHOR]

In this paper we introduce a new stable mathematical model for locating and measuring the medial axis of geometric objects, called the quadratic multiscale medial axis map of scale λ, and provide a sharp regularity result for the squared-distance function to any closed nonempty subset K of Rn. Our results exploit properties of the function Cλl (dist2(.; K)) obtained by applying the quadratic lower compensated convex transform of parameter λ [K. Zhang, Ann. Inst. H. Poincaré Anal. Non Linéaire, 25 (2008), pp. 743-771] to dist2(.; K), the Euclidean squared-distance function to K. Using a quantitative estimate for the tight approximation of dist2(.; K) by Cλl (dist2(.; K)), we prove the C1,1-regularity of dist2(.; K) outside a neighborhood of the closure of the medial axis MK of K, which can be viewed as a weak Lusin-type theorem for dist2(.; K), and give an asymptotic expansion formula for Cλl (dist2(.; K)) in terms of the scaled squared-distance transform to the set and to the convex hull of the set of points that realize the minimum distance to K. The multiscale medial axis map, denoted by Mλ(.; K), is a family of nonnegative functions, parametrized by λ > 0, whose limit as λ→ ∞exists and is called the multiscale medial axis landscape map, M∞(.; K). We show that M∞(.; K) is strictly positive on the medial axis MK and zero elsewhere. We give conditions that ensure Mλ(.; K) keeps a constant height along the parts of MK generated by two-point subsets with the value of the height dependent on the scale of the distance between the generating points, thus providing a hierarchy of heights (hence, the word "multiscale") between different parts of MK that enables subsets of MK to be selected by simple thresholding. Asymptotically, further understanding of the multiscale effect is provided by our exact representation of M∞(.; K). Moreover, given a compact subset K of Rn, while it is well known that MK is not Hausdorff stable, we prove that in contrast, Mλ(.; K) is stable under the Hausdorff distance, and deduce implications for the localization of the stable parts of MK. Explicitly calculated prototype examples of medial axis maps are also presented and used to illustrate the theoretical findings. [ABSTRACT FROM AUTHOR]

We give estimates for the closed c-neighborhood K[sube] of the set, K = ...[subk][supi=1]&lambda[subi]SO(2) ⊂ M[sup2x2] of multiple parallel elastic wells such that dist(Du[subj], K[sub...]) → 0 in L¹(Ω) implies, up to a subsequenee, dist,(Du[subj], (Λi[sub0]SO(2))...) → 0 in L¹(Ω) for some 1 ≤ i[sub0] ≤ k, where Ω ⊂ R² is an arcwise connected domain. In other words, K[sub...] separates gradient Young measures. [ABSTRACT FROM AUTHOR]