Polygon clipping is a frequent operation in many fields, including computer graphics, CAD, and GIS. Thus, efficient and general polygon clipping algorithms are of great importance. Greiner and Hormann (1998) propose a simple and time-efficient algorithm that can clip arbitrary polygons, including concave and self-intersecting polygons with holes. However, the Greiner–Hormann algorithm does not properly handle degenerate intersection cases, without the undesirable need for perturbing vertices. We present an extension of the Greiner–Hormann polygon clipping algorithm that properly deals with such degenerate cases.
Topological data analysis and its main method, persistent homology, provide a toolkit for computing topological information of high-dimensional and noisy data sets. Kernels for one-parameter persistent homology have been established to connect persistent homology with machine learning techniques. We contribute a kernel construction for multi-parameter persistence by integrating a one-parameter kernel weighted along straight lines. We prove that our kernel is stable and efficiently computable, which establishes a theoretical connection between topological data analysis and machine learning for multivariate data analysis.
•A semi-automatic method for creating shades and self-shadows in cel animation.•Tool designed to stay as close as possible to the natural 2D creative environment and therefore provides an intuitive and user-friendly interface.•Shade enhancement system allowing the deformation of shades around detail strokes, such as, but not limited to, facial elements or cloth folds.•An efficient and robust propagation technique allowing the reduction of the effort required to create plausible shades and local self-shadows in a hand-drawn animation.
•Humans are able to recognise extremely abstract human figures.•Children’s picture books give excellent examples of how artists achieve abstraction.•Textural detail can alter considerably between levels of abstraction.•Abstraction of human figures requires model-based understanding.•The principal challenge in automating such abstraction is replicating human knowledge of the world.
•Our global project is a CAGD-system based on CIFS-automata which are an extension of Iterative Functions Systems (IFS).•This model can already handle several types of surfaces with the same unique formalism: Bézier, uniform B-Splines, fractals, subdivision surfaces.•The purpose of this article is to integrate NURBS surfaces, which are the main representation in CAGD, in this model.•Once integrated, NURBS can interact with the other types of surfaces thanks to common tools intrisic to our formalism.
In this paper, we describe cellPACKexplorer, a system designed to help developers of cellPACK find errors in and improve their algorithm. cellPACKexplorer focuses on visualizing the effects of cellPACK recipe parameters on the final packing output. We found that the developers have two different methods for understanding the output, numerical and visual, depending on their background. We designed cellPACKexplorer with a flexible interface to support both types of users. We evaluated our tool through case studies and questionnaires. Novice users were able to create cell models with cellPACK and explore the behavior of different parameters. Further, expert users discovered an error in the code and were able to locate the problem quickly with our new analysis tool. We conclude with a discussion of the implications of our findings in the wider visualization community.
Hands deserve particular attention in virtual reality (VR) applications because they represent our primary means for interacting with the environment. Although marker-based motion capture works adequately for full body tracking, it is less reliable for small body parts such as hands and fingers which are often occluded when captured optically, thus leading VR professionals to rely on additional systems (e.g. inertial trackers). We present a machine learning pipeline to track hands and fingers using solely a motion capture system based on cameras and active markers. Our finger animation is performed by a predictive model based on neural networks trained on a movements dataset acquired from several subjects with a complementary capture system. We employ a two-stage pipeline that first resolves occlusions and then recovers all joint transformations. We show that our method compares favorably to inverse kinematics by inferring automatically the constraints from the data, provides a natural reconstruction of postures, and handles occlusions better than three proposed baselines.
Shape analysis of cell nuclei is becoming increasingly important in biology and medicine. Recent results have identified that large variability in shape and size of nuclei has an important impact on many biological processes. Current analysis techniques involve automatic methods for detection and segmentation of histology and microscopy images, but are mostly performed in 2D. Methods for 3D shape analysis, made possible by emerging acquisition methods capable to provide nanometric-scale 3D reconstructions, are still at an early stage, and often assume a simple spherical shape. We introduce here a framework for analyzing 3D nanoscale reconstructions of nuclei of brain cells (mostly neurons), obtained by semiautomatic segmentation of electron micrographs. Our method considers two parametric representations: the first one customizes the implicit hyperquadricsformulation and it is particularly suited for convex shapes, while the latter considers a spherical harmonics decomposition of the explicit radial representation. Point clouds of nuclear envelopes, extracted from image data, are fitted to the parameterized models which are then used for performing statistical analysis and shape comparisons. We report on the analysis of a collection of 121 nuclei of brain cells obtained from the somatosensory cortex of a juvenile rat.
