Dennie Reniers

Skeleton-based Hierarchical Shape Segmentation

We present an effective framework for segmenting 3D shapes into meaningful components using the curve skeleton. Our algorithm identifies a number of critical points on the curve skeleton, either fully automatically as the junctions of the curve skeleton, or based on user input. We use these points to construct a partitioning of the object surface using geodesies. Because it is based on the curve skeleton, our segmentation intrinsically reflects the shape symmetry and topology. By using geodesies we obtain segments that have smooth, minimally twisting borders. Finally, we present a hierarchical segmentation of shapes which reflects the hierarchical structure of the curve skeleton. We describe a voxel-based implementation of our method which is robust and noise resistant, computationally efficient, able to handle shapes of complex topology, and which delivers level- of-detail segmentations. We demonstrate the framework on various real-world 3D shapes.

Computing Multiscale Curve and Surface Skeletons of Genus 0 Shapes Using a Global Importance Measure

We present a practical algorithm for computing robust multiscale curve and surface skeletons of 3D objects of genus zero. Based on a model that follows an advection principle, we assign to each point on the skeleton a part of the object surface, called the collapse. The size of the collapse is used as a uniform importance measure for the curve and surface skeleton, so that both can be simplified by imposing a single threshold on this intuitive measure. The simplified skeletons are connected by default, without special precautions, due to the monotonicity of the importance measure. The skeletons possess additional desirable properties: They are centered, robust to noise, hierarchical, and provide a natural skeleton-to-boundary mapping. We present a voxel-based algorithm that is straightforward to implement and simple to use. We illustrate our method on several realistic 3D objects.

Extraction and visualization of call dependencies for large C/C++ code bases: A comparative study

Investigating program dependencies such as function calls is challenging for very large systems. We present here an integrated pipeline for extraction and visualization of call-and-hierarchy graphs for C/C++ programs. We present several adaptions and enhancements of a recent visualization method for large call graphs and compare its effectiveness with classical node-link diagrams. Examples are given on large real-world code bases such as bison, Mozilla and oink.

The Solid* toolset for software visual analytics of program structure and metrics comprehension: From research prototype to product

Software visual analytics (SVA) tools combine static program analysis and fact extraction with information visualization to support program comprehension. However, building efficient and effective SVA tools is highly challenging, as it involves extensive software development in program analysis, graphics, information visualization, and interaction. We present a SVA toolset for software maintenance, and detail two of its components which target software structure, metrics and code duplication. We illustrate the toolsets usage for constructing software visualizations with examples in education, research, and industrial contexts. We discuss the design evolution from research prototypes to integrated, scalable, and easy-to-use products, and present several guidelines for the development of efficient and effective SVA solutions.

Part-type segmentation of articulated voxel-shapes using the junction rule

We present a part‐type segmentation method for articulated voxel‐shapes based on curve skeletons. Shapes are considered to consist of several simpler, intersecting shapes. Our method is based on the junction rule: the observation that two intersecting shapes generate an additional junction in their joined curve‐skeleton near the place of intersection. For each curve‐skeleton point, we construct a piecewise‐geodesic loop on the shape surface. Starting from the junctions, we search along the curve skeleton for points whose associated loops make for suitable part cuts. The segmentations are robust to noise and discretization artifacts, because the curve skeletonization incorporates a single user‐parameter to filter spurious curve‐skeleton branches. Furthermore, segment borders are smooth and minimally twisting by construction. We demonstrate our method on several real‐world examples and compare it to existing part‐type segmentation methods.

Tolerance-Based Feature Transforms

Tolerance-based feature transforms (TFTs) assign to each pixel in an image not only the nearest feature pixels on the boundary (origins), but all origins from the minimum distance up to a user-defined tolerance. In this paper, we compare four simple-to-implement methods for computing TFTs on binary images. Of these methods, the Fast Marching TFT and Euclidean TFT are new. The other two extend existing distance transform algorithms. We quantitatively and qualitatively compare all algorithms on speed and accuracy of both distance and origin results. Our analysis is aimed at helping practitioners in the field to choose the right method for given accuracy and performance constraints.

Patch-type Segmentation of Voxel Shapes using Simplified Surface Skeletons

We present a new method for decomposing a 3D voxel shape into disjoint segments using the shapes simplified surface‐skeleton. The surface skeleton of a shape consists of 2D manifolds inside its volume. Each skeleton point has a maximally inscribed ball that touches the boundary in at least two contact points. A key observation is that the boundaries of the simplified fore‐ and background skeletons map one‐to‐one to increasingly fuzzy, soft convex, respectively concave, edges of the shape. Using this property, we build a method for segmentation of 3D shapes which has several desirable properties. Our method segments both noisy shapes and shapes with soft edges which vanish over low‐curvature regions. Multiscale segmentations can be obtained by varying the simplification level of the skeleton. We present a voxel‐based implementation of our approach and illustrate it on several realistic examples.

Visual exploration of program structure, dependencies and metrics with SolidSX

We present SolidSX, an visual analysis tool for code structure, dependencies, and metrics. Our tool facilitates the understanding of large program code bases by simplifying the entire pipeline from data acquisition up to visualization and interactive querying. Secondly, SolidSX is an easy to use, scalable, and configurable visualization component for compound attributed graphs extracted by third-party tools, easy to integrate by developers in their own applications. We detail the architecture and functions of SolidSX, present examples for its two use-cases, and outline insights collected from tool usage in academia and industry.

Robust classification and analysis of anatomical surfaces using 3D skeletons

We present a method for computing a surface classifier that can be used to detect convex ridges on voxel sur- faces extracted from 3D scans. In contrast to classical approaches based on (discrete) curvature computations, which can be sensitive to various types of noise, we propose here a new method that detects convex ridges on such surfaces, based on the computation of the surfaces 3D skeleton. We use a suitable robust, noise-resistant skeletonization algorithm to extract the full 3D skeleton of the given surface, and subsequently compute a surface classifier that separates convex ridges from quasi-flat regions, using the feature points of the simplified skeleton. We demonstrate our method on voxel surfaces extracted from actual anatomical scans, with a focus on cortical surfaces, and compare our results with curvature-based classifiers. As a second application of the 3D skeleton, we show how a partitioning of the brain skeleton can be used in a preprocessing step for the brain surface analysis.

Hierarchical part-type segmentation using voxel-based curve skeletons

We present an effective framework for segmenting 3D shapes into meaningful components using the curve skeleton. Our algorithm identifies a number of critical points on the efficiently computed curve skeleton, either fully automatically as the junctions of the curve skeleton, or based on user input. We use these points to construct a partitioning of the object surface using geodesics. Because the segmentation is based on the curve skeleton, it intrinsically reflects the shape symmetry and articulation, and can handle shapes with tunnels. We describe a voxel-based implementation of our method which is robust and noise resistant, able to handle shapes of complex articulation and topology, produces smooth segment borders, and delivers hierarchical level-of-detail segmentations. We demonstrate the framework on various real-world 3D shapes. Additionally, we discuss the use of both curve and surface skeletons to produce part-type and patch-type, respectively, segmentations of 3D shapes.