Glyph‐Based Comparative Stress Tensor Visualization in Cerebral Aneurysms

We present the first visualization tool that enables a comparative depiction of structural stress tensor data for vessel walls of cerebral aneurysms. Such aneurysms bear the risk of rupture, whereas their treatment also carries considerable risks for the patient. Medical researchers emphasize the importance of analyzing the interaction of morphological and hemodynamic information for the patient‐specific rupture risk evaluation and treatment analysis. Tensor data such as the stress inside the aneurysm walls characterizes the interplay between the morphology and blood flow and seems to be an important rupture‐prone criterion. We use different glyph‐based techniques to depict local stress tensors simultaneously and compare their applicability to cerebral aneurysms in a user study. We thus offer medical researchers an effective visual exploration tool to assess the aneurysm rupture risk. We developed a GPU‐based implementation of our techniques with a flexible interactive data exploration mechanism. Our depictions are designed in collaboration with domain experts, and we provide details about the evaluation.     

Semi‐automatic Vortex Flow Classification in 4D PC‐MRI Data of the Aorta

We present an Aortic Vortex Classification (AVOCLA) that allows to classify vortices in the human aorta semi‐automatically. Current medical studies assume a strong relation between cardiovascular diseases and blood flow patterns such as vortices. Such vortices are extracted and manually classified according to specific, unstandardized properties. We employ an agglomerative hierarchical clustering to group vortex‐representing path lines as basis for the subsequent classification. Classes are based on the vortex' size, orientation and shape, its temporal occurrence relative to the cardiac cycle as well as its spatial position relative to the vessel course. The classification results are presented by a 2D and 3D visualization technique. To confirm the usefulness of both approaches, we report on the results of a user study. Moreover, AVOCLA was applied to 15 datasets of healthy volunteers and patients with different cardiovascular diseases. The results of the semi‐automatic classification were qualitatively compared to a manually generated ground truth of two domain experts considering the vortex number and five specific properties.     

Making in‐Front‐of Cars Transparent: Sharing First‐Person‐Views via Dashcam

Visual obstruction caused by a preceding vehicle is one of the key factors threatening driving safety. One possible solution is to share the first‐person‐view of the preceding vehicle to unveil the blocked field‐of‐view of the following vehicle. However, the geometric inconsistency caused by the camera‐eye discrepancy renders view sharing between different cars a very challenging task. In this paper, we present a first‐person‐perspective image rendering algorithm to solve this problem. Firstly, we contour unobstructed view as the transferred region, then by iteratively estimating local homography transformations and performing perspective‐adaptive warping using the estimated transformations, we are able to locally adjust the shape of the unobstructed view so that its perspective and boundary could be matched to that of the occluded region. Thus, the composited view is seamless in both the perceived perspective and photometric appearance, creating an impression as if the preceding vehicle is transparent. Our system improves the driver's visibility and thus relieves the burden on the driver, which in turn increases comfort. We demonstrate the usability and stability of our system by performing its evaluation with several challenging data sets collected from real‐world driving scenarios.     

Visual and quantitative analysis of great arteries’ blood flow jets in cardiac 4D PC‐MRI data

Flow in the great arteries (aorta, pulmonary artery) is normally laminar with a parabolic velocity profile. Eccentric flow jets are linked to various diseases like aneurysms. Cardiac 4D PC‐MRI data provide spatio‐temporally resolved blood flow information for the whole cardiac cycle. In this work, we establish a time‐dependent visualization and quantification of flow jets. For this purpose, equidistant measuring planes are automatically placed along the vessel's centerline. The flow jet position and region with highest velocities are extracted for every plane in each time step. This is done during pre‐processing and without user‐defined parameters. We visualize the main flow jet as geometric tube. High‐velocity areas are depicted as a net around this tube. Both geometries are time‐dependent and can be animated. Quantitative values are provided during cross‐sectional measuring plane‐based evaluation. Moreover, we offer a plot visualization as summary of flow jet characteristics for the selected plane. Our physiologically plausible results are in accordance with medical findings. Our clinical collaborators appreciate the possibility to view the flow jet in the whole vessel at once, which normally requires repeated pathline filtering due to varying velocities along the vessel course. The overview plots are considered as valuable for documentation purposes.     

Generalized As‐Similar‐As‐Possible Warping with Applications in Digital Photography

Discrete conformal mappings of planar triangle meshes, also known as the As‐Similar‐As‐Possible (ASAP) mapping, involve the minimization of a quadratic energy function, thus are very easy to generate and are popular in image warping scenarios. We generalize this classical mapping to the case of quad meshes, taking into account the mapping of the interior of the quad, and analyze in detail the most common case ‐ the unit grid mesh. We show that the generalization, when combined with barycentric coordinate mappings between the source and target polygons, spawns an entire family of new mappings governed by quadratic energy functions, which allow to control quite precisely various effects of the mapping. This approach is quite general and applies also to arbitrary planar polygon meshes. As an application of generalized ASAP mappings of the unit grid mesh, we demonstrate how they can be used to warp digital photographs to achieve a variety of effects. One such effect is modifying the perspective of the camera that took a given photograph (without moving the camera). A related, but more challenging, effect is re‐photography ‐ warping a contemporary photograph in order to reproduce the camera view present in a vintage photograph of the same scene ‐ taken many years before with a different camera from a different viewpoint. We apply the generalized ASAP mapping to these images, discretized to a unit grid. Using a quad mesh (as opposed to a triangle mesh) permits biasing towards affine maps of the unit squares. This allows the introduction of an As‐Affine‐As‐Possible (AAAP) mapping for a good approximation of the homographies present in these warps, achieving quite accurate results. We demonstrate the advantages of the AAAP mapping on a variety of synthetic and real‐world examples.     

Visual Analysis of Governing Topological Structures in Excitable Network Dynamics

To understand how topology shapes the dynamics in excitable networks is one of the fundamental problems in network science when applied to computational systems biology and neuroscience. Recent advances in the field discovered the influential role of two macroscopic topological structures, namely hubs and modules. We propose a visual analytics approach that allows for a systematic exploration of the role of those macroscopic topological structures on the dynamics in excitable networks. Dynamical patterns are discovered using the dynamical features of excitation ratio and co‐activation. Our approach is based on the interactive analysis of the correlation of topological and dynamical features using coordinated views. We designed suitable visual encodings for both the topological and the dynamical features. A degree map and an adjacency matrix visualization allow for the interaction with hubs and modules, respectively. A barycentric‐coordinates layout and a multi‐dimensional scaling approach allow for the analysis of excitation ratio and co‐activation, respectively. We demonstrate how the interplay of the visual encodings allows us to quickly reconstruct recent findings in the field within an interactive analysis and even discovered new patterns. We apply our approach to network models of commonly investigated topologies as well as to the structural networks representing the connectomes of different species. We evaluate our approach with domain experts in terms of its intuitiveness, expressiveness, and usefulness.     

AutoStyle: Automatic Style Transfer from Image Collections to Users' Images

Stylizing photos, to give them an antique or artistic look, has become popular in recent years. The available stylization filters, however, are usually created manually by artists, resulting in a narrow set of choices. Moreover, it can be difficult for the user to select a desired filter, since the filters’ names often do not convey their functions. We investigate an approach to photo filtering in which the user provides one or more keywords, and the desired style is defined by the set of images returned by searching the web for those keywords. Our method clusters the returned images, allows the user to select a cluster, then stylizes the user's photos by transferring vignetting, color, and local contrast from that cluster. This approach vastly expands the range of available styles, and gives each filter a meaningful name by default. We demonstrate that our method is able to robustly transfer a wide range of styles from image collections to users’ photos.     

Data‐driven Handwriting Synthesis in a Conjoined Manner

A person's handwriting appears differently within a typical range of variations, and the shapes of handwriting characters also show complex interaction with their nearby neighbors. This makes automatic synthesis of handwriting characters and paragraphs very challenging. In this paper, we propose a method for synthesizing handwriting texts according to a writer's handwriting style. The synthesis algorithm is composed by two phases. First, we create the multidimensional morphable models for different characters based on one writer's data. Then, we compute the cursive probability to decide whether each pair of neighboring characters are conjoined together or not. By jointly modeling the handwriting style and conjoined property through a novel trajectory optimization, final handwriting words can be synthesized from a set of collected samples. Furthermore, the paragraphs’ layouts are also automatically generated and adjusted according to the writer's style obtained from the same dataset. We demonstrate that our method can successfully synthesize an entire paragraph that mimic a writer's handwriting using his/her collected handwriting samples.     

MIQP‐based Layout Design for Building Interiors

We propose a hierarchical framework for the generation of building interiors. Our solution is based on a mixed integer quadratic programming (MIQP) formulation. We parametrize a layout by polygons that are further decomposed into small rectangles. We identify important high‐level constraints, such as room size, room position, room adjacency, and the outline of the building, and formulate them in a way that is compatible with MIQP and the problem parametrization. We also propose a hierarchical framework to improve the scalability of the approach. We demonstrate that our algorithm can be used for residential building layouts and can be scaled up to large layouts such as office buildings, shopping malls, and supermarkets. We show that our method is faster by multiple orders of magnitude than previous methods.     

DeepProp: Extracting Deep Features from a Single Image for Edit Propagation

Edit propagation is a technique that can propagate various image edits (e.g., colorization and recoloring) performed via user strokes to the entire image based on similarity of image features. In most previous work, users must manually determine the importance of each image feature (e.g., color, coordinates, and textures) in accordance with their needs and target images. We focus on representation learning that automatically learns feature representations only from user strokes in a single image instead of tuning existing features manually. To this end, this paper proposes an edit propagation method using a deep neural network (DNN). Our DNN, which consists of several layers such as convolutional layers and a feature combiner, extracts stroke‐adapted visual features and spatial features, and then adjusts the importance of them. We also develop a learning algorithm for our DNN that does not suffer from the vanishing gradient problem, and hence avoids falling into undesirable locally optimal solutions. We demonstrate that edit propagation with deep features, without manual feature tuning, can achieve better results than previous work.     

Visualizing Probabilistic Multi‐Phase Fluid Simulation Data using a Sampling Approach

Eulerian Method of Moment (MoM) solvers are gaining popularity for multi‐phase CFD simulation involving bubbles or droplets in process engineering. Because the actual positions of bubbles are uncertain, the spatial distribution of bubbles is described by scalar fields of moments, which can be interpreted as probability density functions. Visualizing these simulation results and comparing them to physical experiments is challenging, because neither the shape nor the distribution of bubbles described by the moments lend themselves to visual interpretation. In this work, we describe a visualization approach that provides explicit instances of the bubble distribution and produces bubble geometry based on local flow properties. To facilitate animation, the instancing of the bubble distribution provides coherence over time by advancing bubbles between time steps and updating the distribution. Our approach provides an intuitive visualization and enables direct visual comparison of simulation results to physical experiments.     

Patch2Vec: Globally Consistent Image Patch Representation

Many image editing applications rely on the analysis of image patches. In this paper, we present a method to analyze patches by embedding them to a vector space, in which the Euclidean distance reflects patch similarity. Inspired by Word2Vec, we term our approach Patch2Vec. However, there is a significant difference between words and patches. Words have a fairly small and well defined dictionary. Image patches, on the other hand, have no such dictionary and the number of different patch types is not well defined. The problem is aggravated by the fact that each patch might contain several objects and textures. Moreover, Patch2Vec should be universal because it must be able to map never‐seen‐before texture to the vector space. The mapping is learned by analyzing the distribution of all natural patches. We use Convolutional Neural Networks (CNN) to learn Patch2Vec. In particular, we train a CNN on labeled images with a triplet‐loss objective function. The trained network encodes a given patch to a 128D vector. Patch2Vec is evaluated visually, qualitatively, and quantitatively. We then use several variants of an interactive single‐click image segmentation algorithm to demonstrate the power of our method.     

Scale‐aware Structure‐Preserving Texture Filtering

This paper presents a novel method to enhance the performance of structure‐preserving image and texture filtering. With conventional edge‐aware filters, it is often challenging to handle images of high complexity where features of multiple scales coexist. In particular, it is not always easy to find the right balance between removing unimportant details and protecting important features when they come in multiple sizes, shapes, and contrasts. Unlike previous approaches, we address this issue from the perspective of adaptive kernel scales. Relying on patch‐based statistics, our method identifies texture from structure and also finds an optimal per‐pixel smoothing scale. We show that the proposed mechanism helps achieve enhanced image/texture filtering performance in terms of protecting the prominent geometric structures in the image, such as edges and corners, and keeping them sharp even after significant smoothing of the original signal.     

Learning Probabilistic Transfer Functions: A Comparative Study of Classifiers

Complex volume rendering tasks require high‐dimensional transfer functions, which are notoriously difficult to design. One solution to this is to learn transfer functions from scribbles that the user places in the volumetric domain in an intuitive and natural manner. In this paper, we explicitly model and visualize the uncertainty in the resulting classification. To this end, we extend a previous intelligent system approach to volume rendering, and we systematically compare five supervised classification techniques – Gaussian Naive Bayes, k Nearest Neighbor, Support Vector Machines, Neural Networks, and Random Forests – with respect to probabilistic classification, support for multiple materials, interactive performance, robustness to unreliable input, and easy parameter tuning, which we identify as key requirements for the successful use in this application. Based on theoretical considerations, as well as quantitative and visual results on volume datasets from different sources and modalities, we conclude that, while no single classifier can be expected to outperform all others under all circumstances, random forests are a useful off‐the‐shelf technique that provides fast, easy, robust and accurate results in many scenarios.     

Semi‐Automated Video Morphing

We explore creating smooth transitions between videos of different scenes. As in traditional image morphing, good spatial correspondence is crucial to prevent ghosting, especially at silhouettes. Video morphing presents added challenges. Because motions are often unsynchronized, temporal alignment is also necessary. Applying morphing to individual frames leads to discontinuities, so temporal coherence must be considered. Our approach is to optimize a full spatiotemporal mapping between the two videos. We reduce tedious interactions by letting the optimization derive the fine‐scale map given only sparse user‐specified constraints. For robustness, the optimization objective examines structural similarity of the video content. We demonstrate the approach on a variety of videos, obtaining results using few explicit correspondences.     

Illuminant Aware Gamut‐Based Color Transfer

This paper proposes a new approach for color transfer between two images. Our method is unique in its consideration of the scene illumination and the constraint that the mapped image must be within the color gamut of the target image. Specifically, our approach first performs a white‐balance step on both images to remove color casts caused by different illuminations in the source and target image. We then align each image to share the same ‘white axis’ and perform a gradient preserving histogram matching technique along this axis to match the tone distribution between the two images. We show that this illuminant‐aware strategy gives a better result than directly working with the original source and target image's luminance channel as done by many previous methods. Afterwards, our method performs a full gamut‐based mapping technique rather than processing each channel separately. This guarantees that the colors of our transferred image lie within the target gamut. Our experimental results show that this combined illuminant‐aware and gamut‐based strategy produces more compelling results than previous methods. We detail our approach and demonstrate its effectiveness on a number of examples.     

Interactive 3D Force‐Directed Edge Bundling

Interactive analysis of 3D relational data is challenging. A common way of representing such data are node‐link diagrams as they support analysts in achieving a mental model of the data. However, naïve 3D depictions of complex graphs tend to be visually cluttered, even more than in a 2D layout. This makes graph exploration and data analysis less efficient. This problem can be addressed by edge bundling. We introduce a 3D cluster‐based edge bundling algorithm that is inspired by the force‐directed edge bundling (FDEB) algorithm [ HvW09b ] and fulfills the requirements to be embedded in an interactive framework for spatial data analysis. It is parallelized and scales with the size of the graph regarding the runtime. Furthermore, it maintains the edge's model and thus supports rendering the graph in different structural styles. We demonstrate this with a graph originating from a simulation of the function of a macaque brain.