Computers and Graphics: X最新文献

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 with applicability on shape analysis, recognition and classification. 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.

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.

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.

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.

Iterated Function Systems (IFS) are a standard tool to generate fractal shapes. In a more general way, they can represent most of standard surfaces like Bézier or B-Spline surfaces known as self-similar surfaces. Controlled Iterated Function Systems (CIFS) are an extension of IFS based on automata. CIFS are basically multi-states IFS, they can handle all IFS shapes but can also manage multi self-similar shapes. For example CIFS can describe subdivision surfaces around extraordinary vertices whereas IFS cannot. Having a common CIFS formalism facilitates the development of generic methods to manage interactions (junctions, differences...) between objects of different natures.

This work focuses on a CIFS approach of Non-Uniform Rational B-Splines (NURBS) which are the main used representation of surfaces in CAGD systems. By analyzing the recursive generating process of basis functions, we prove the stationarity of NURBS computation. This implies that NURBS can be represented as a finite automaton: a CIFS. Subdivision transformations implied in the generating process are directly deduced from blossoming formulation and are expressed as a function of the initial nodal vector. We provide a method to construct the CIFS automata for NURBS of any-degree. Then NURBS-surfaces automata are deduced using a “tensor-product” of NURBS automata. This new representation of NURBS allows us to build a bridge between them and other surfaces already represented in CIFS formalism: fractals and subdivision surfaces.

The human figure is important in art. I discuss examples of the abstract depiction of the human figure, from both impressionist painting and children’s book illustration, and the challenge faced in algorithmically mimicking what human artists can achieve. I demonstrate that there are excellent examples in both genres that provide insight into what a human artist sees as important in providing abstraction at different levels of detail. The challenge lies in the human brain having enormous knowledge about the world and an ability to make fine distinctions about other humans from posture, clothing and expression. This allows a human to make assumptions about human figures from a tiny amount of data, and allows a human artist to take advantage of this when creating art. The question for the computer graphics community is whether and how we could algorithmically mimic what a human artist can do. I provide evidence from both genres to suggest possible ways forward.

We present 2DToonShade: a semi-automatic method for creating shades and self-shadows in cel animation. Besides producing attractive images, shades and shadows provide important visual cues about depth, shapes, movement and lighting of the scene. In conventional cel animation, shades and shadows are drawn by hand. As opposed to previous approaches, this method does not rely on a complex 3D reconstruction of the scene: its key advantages are simplicity and ease of use. The tool was designed to stay as close as possible to the natural 2D creative environment and therefore provides an intuitive and user-friendly interface. Our system creates shading based on hand-drawn objects or characters, given very limited guidance from the user. The method employs simple yet very efficient algorithms to create shading directly out of drawn strokes. We evaluate our system through a subjective user study and provide qualitative comparison of our method versus existing professional tools and recent state of the art.

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.