Aesthetic images evoke an emotional response that transcends mere visual appreciation. In this work we develop a novel computational means for evaluating the composition aesthetics of a given image based on measuring several well‐grounded composition guidelines. A compound operator of crop‐and‐retarget is employed to change the relative position of salient regions in the image and thus to modify the composition aesthetics of the image. We propose an optimization method for automatically producing a maximally‐aesthetic version of the input image. We validate the performance of the method and show its effectiveness in a variety of experiments.

We present a novel approach to localization of sensors in a network given a subset of noisy inter-sensor distances. The algorithm is based on “stitching” together local structures by solving an optimization problem requiring the structures to fit together in an “As-Rigid-As-Possible” manner, hence the name ARAP. The local structures consist of reference “patches” and reference triangles, both obtained from inter-sensor distances. We elaborate on the relationship between the ARAP algorithm and other state-of-the-art algorithms, and provide experimental results demonstrating that ARAP is significantly less sensitive to sparse connectivity and measurement noise. We also show how ARAP may be distributed.

The Delaunay triangulation of a planar point set is a fundamental construct in computational geometry. A simple algorithm to generate it is based on flips of diagonal edges in convex quads. We characterize the effect of a single edge flip in a triangulation on the geometric Laplacian of the triangulation, which leads to a simpler and shorter proof of a theorem of Rippa that the Dirichlet energy of any piecewise-linear scalar function on a triangulation obtains its minimum on the Delaunay triangulation. Using Rippa's theorem, we provide a spectral characterization of the Delaunay triangulation, namely that the spectrum of the geometric Laplacian is minimized on this triangulation. This spectral theorem then leads to a simpler proof of a theorem of Musin that the harmonic index also obtains its minimum on the Delaunay triangulation.

This paper presents a new approach, called cubic clipping, for computing all the roots of a given polynomial within an interval. In every iterative computation step, two cubic polynomials are generated to enclose the graph of the polynomial within the interval of interest. A sequence of intervals is then obtained by intersecting the sequence of strips with the abscissa axis. The sequence of these intervals converges to the corresponding root with the convergence rate 4 for the single roots, 2 for the double roots and super-linear for the triple roots. Numerical examples show that cubic clipping has many expected advantages over Bézier clipping and quadratic clipping. We also extend our approach by enclosing the graph of the polynomial using two lower degree polynomials by degree reduction. The sequence of intervals converges to the corresponding root of multiplicity s with convergence rate .

The essence of a 3D shape can often be well captured by its salient feature curves. In this paper, we explore the use of salient curves in synthesizing intuitive, shape-revealing textures on surfaces. Our texture synthesis is guided by two principles: matching the direction of the texture patterns to those of the salient curves, and aligning the prominent feature lines in the texture to the salient curves exactly. We have observed that textures synthesized by these principles not only fit naturally to the surface geometry, but also visually reveal, even reinforce, the shape's essential characteristics. We call these feature-aligned shape texturing. Our technique is fully automatic, and introduces two novel technical components in vector-field-guided texture synthesis: an algorithm that orients the salient curves on a surface for constrained vector field generation, and a feature-to-feature texture optimization.