Alexandru Telea

An image inpainting technique based on the fast marching method

Abstract Digital inpainting provides a means for reconstruction of small damaged portions of an image. Although the inpainting basics are straightforward, most inpainting techniques published in the literature are complex to understand and implement. We present here a new algorithm for digital inpainting based on the fast marching method for level set applications. Our algorithm is very simple to implement, fast, and produces nearly identical results to more complex, and usually slower, known methods. Source code is available online.

An augmented Fast Marching Method for computing skeletons and centerlines

We present a simple and robust method for computing skeletons for arbitrary planar objects and centerlines for 3D objects. We augment the Fast Marching Method (FMM) widely used in level set applications by computing the paramterized boundary location every pixel came from during the boundary evolution. The resulting parameter field is then thresholded to produce the skeleton branches created by boundary features of a given size. The presented algorithm is straightforward to implement, has low memory costs and short execution times, and is robust with respect to the used threshold and initial shape noisiness. The produced skeletons are very similar to the ones delivered by more complex algorithms. Various 2D and 3D applications are presented.

Simplified representation of vector fields

Vector field visualization remains a difficult task. Many local and global visualization methods for vector fields such as flow data exist, but they usually require extensive user experience on setting the visualization parameters in order to produce images communicating the desired insight. We present a visualization method that produces simplified but suggestive images of the vector field automatically, based on a hierarchical clustering of the input data. The resulting clusters are then visualized with straight or curved arrow icons. The presented method has a few parameters with which users can produce various simplified vector field visualizations that communicate different insights on the vector data.

CVSscan: visualization of code evolution

During the life cycle of a software system, the source code is changed many times. We study how developers can be enabled to get insight in these changes, in order to understand the status, history and structure better, as well as for instance the roles played by various contributors. We present CVSscan, an integrated multiview environment for this. Central is a line-oriented display of the changing code, where each version is represented by a column, and where the horizontal direction is used for time, Separate linked displays show various metrics, as well as the source code itself. A large variety of options is provided to visualize a number of different aspects. Informal user studies demonstrate the efficiency of this approach for real world use cases.

Skeleton-Based Edge Bundling for Graph Visualization

In this paper, we present a novel approach for constructing bundled layouts of general graphs. As layout cues for bundles, we use medial axes, or skeletons, of edges which are similar in terms of position information. We combine edge clustering, distance fields, and 2D skeletonization to construct progressively bundled layouts for general graphs by iteratively attracting edges towards the centerlines of level sets of their distance fields. Apart from clustering, our entire pipeline is image-based with an efficient implementation in graphics hardware. Besides speed and implementation simplicity, our method allows explicit control of the emphasis on structure of the bundled layout, i.e. the creation of strongly branching (organic-like) or smooth bundles. We demonstrate our method on several large real-world graphs.

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.

Image-based edge bundles: simplified visualization of large graphs

We present a new approach aimed at understanding the structure of connections in edge‐bundling layouts. We combine the advantages of edge bundles with a bundle‐centric simplified visual representation of a graphs structure. For this, we first compute a hierarchical edge clustering of a given graph layout which groups similar edges together. Next, we render clusters at a user‐selected level of detail using a new image‐based technique that combines distance‐based splatting and shape skeletonization. The overall result displays a given graph as a small set of overlapping shaded edge bundles. Luminance, saturation, hue, and shading encode edge density, edge types, and edge similarity. Finally, we add brushing and a new type of semantic lens to help navigation where local structures overlap. We illustrate the proposed method on several real‐world graph datasets.

Graph Bundling by Kernel Density Estimation

We present a fast and simple method to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply an image sharpening technique which progressively merges local height maxima by moving the convolved graph edges into the height gradient flow. Our technique can be easily and efficiently implemented using standard graphics acceleration techniques and produces graph bundlings of similar appearance and quality to state‐of‐the‐art methods at a fraction of the cost. Additionally, we show how to create bundled layouts constrained by obstacles and use shading to convey information on the bundling quality. We demonstrate our method on several large graphs.

Generalized distance transforms and skeletons in graphics hardware

We present a framework for computing generalized distance transforms and skeletons of two-dimensional objects using graphics hardware. Our method is based on the concept of footprint splatting. Combining different splats produces weighted distance transforms for different metrics, as well as the corresponding skeletons and Voronoi diagrams. We present a hierarchical acceleration scheme and a subdivision scheme that allows visualizing the computed skeletons with subpixel accuracy in real time. Our splatting approach allows one to easily change all the metric parameters, treat any 2D boundaries, and easily produce both DTs and skeletons. We illustrate the method by several examples.

Robust feature detection and local classification for surfaces based on moment analysis

The stable local classification of discrete surfaces with respect to features such as edges and corners or concave and convex regions, respectively, is as quite difficult as well as indispensable for many surface processing applications. Usually, the feature detection is done via a local curvature analysis. If concerned with large triangular and irregular grids, e.g., generated via a marching cube algorithm, the detectors are tedious to treat and a robust classification is hard to achieve. Here, a local classification method on surfaces is presented which avoids the evaluation of discretized curvature quantities. Moreover, it provides an indicator for smoothness of a given discrete surface and comes together with a built-in multiscale. The proposed classification tool is based on local zero and first moments on the discrete surface. The corresponding integral quantities are stable to compute and they give less noisy results compared to discrete curvature quantities. The stencil width for the integration of the moments turns out to be the scale parameter. Prospective surface processing applications are the segmentation on surfaces, surface comparison, and matching and surface modeling. Here, a method for feature preserving fairing of surfaces is discussed to underline the applicability of the presented approach.