Image Appearance Exploration by Model-Based Navigation

Changing the appearance of an image can be a complex and non-intuitive task. Many times the target image colors and look are only known vaguely and many trials are needed to reach the desired results. Moreover, the effect of a specific change on an image is difficult to envision, since one must take into account spatial image considerations along with the color constraints. Tools provided today by image processing applications can become highly technical and non-intuitive including various gauges and knobs.
In this paper we introduce a method for changing image appearance by navigation, focusing on recoloring images. The user visually navigates a high dimensional space of possible color manipulations of an image. He can either explore in it for inspiration or refine his choices by navigating into sub regions of this space to a specific goal. This navigation is enabled by modeling the chroma channels of an image's colors using a Gaussian Mixture Model (GMM). The Gaussians model both color and spatial image coordinates, and provide a high dimensional parameterization space of a rich variety of color manipulations. The user's actions are translated into transformations of the parameters of the model, which recolor the image. This approach provides both inspiration and intuitive navigation in the complex space of image color manipulations.     

SDViz: A Context-Preserving Interactive Visualization System for Technical Diagrams

When performing daily maintenance and repair tasks, technicians require access to a variety of technical diagrams. As technicians trace components and diagrams from page-to-page, within and across manuals, the contextual information of the components they are analyzing can easily be lost. To overcome these issues, we have developed a Schematic Diagram Visualization System (SDViz) designed for maintaining and highlighting contextual information in technical documents, such as schematic and wiring diagrams. Our system incorporates various features to aid in the navigation and diagnosis of faults, as well as maintaining contextual information when tracing components/connections through multiple diagrams. System features include highlighting relationships between components and connectors, diagram annotation tools, the animation of flow through the system, a novel contextual blending method, and a variety of traditional focus+context visualization techniques. We have evaluated the usefulness of our system through a qualitative user study in which subjects utilized our system in diagnosing faults during a standard aircraft maintenance exercise.     

Animating Pictures of Fluid using Video Examples

We propose a system that allows the user to design a continuous flow animation starting from a still fluid image. The basic idea is to apply the fluid motion extracted from a video example to the target image. The system first decomposes the video example into three components, an average image, a flow field and residuals. The user then specifies equivalent information over the target image. The user manually paints the rough flow field, and the system automatically refines it using the estimated gradients of the target image. The user semi-automatically transfers the residuals onto the target image. The system then approximates the average image and synthesizes an animation on the target image by adding the transferred residuals and warping them according to the user-specified flow field. Finally, the system adjusts the appearance of the resulting animation by applying histogram matching. We designed animations of various pictures, such as rivers, waterfalls, fires, and smoke.     

A Screen Space Quality Method for Data Abstraction

The rendering of large data sets can result in cluttered displays and non‐interactive update rates, leading to time consuming analyses. A straightforward solution is to reduce the number of items, thereby producing an abstraction of the data set. For the visual analysis to remain accurate, the graphical representation of the abstraction must preserve the significant features present in the original data. This paper presents a screen space quality method, based on distance transforms, that measures the visual quality of a data abstraction. This screen space measure is shown to better capture significant visual structures in data, compared with data space measures. The presented method is implemented on the GPU, allowing interactive creation of high quality graphical representations of multivariate data sets containing tens of thousands of items.     

Predicting Display Visibility Under Dynamically Changing Lighting Conditions

Display devices, more than ever, are finding their ways into electronic consumer goods as a result of recent trends in providing more functionality and user interaction. Combined with the new developments in display technology towards higher reproducible luminance range, the mobility and variation in capability of display devices are constantly increasing. Consequently, in real life usage it is now very likely that the display emission to be distorted by spatially and temporally varying reflections, and the observer's visual system to be not adapted to the particular display that she is viewing at that moment. The actual perception of the display content cannot be fully understood by only considering steady-state illumination and adaptation conditions. We propose an objective method for display visibility analysis formulating the problem as a full-reference image quality assessment problem, where the display emission under "ideal" conditions is used as the reference for real-life conditions. Our work includes a human visual system model that accounts for maladaptation and temporal recovery of sensitivity. As an example application we integrate our method to a global illumination simulator and analyze the visibility of a car interior display under realistic lighting conditions.     

Prostate Cancer Visualization from MR Imagery and MR Spectroscopy

Prostate cancer is one of the most prevalent cancers among males, and the use of magnetic resonance imaging (MRI) has been suggested for its detection. A framework is presented for scoring and visualizing various MR data in an efficient and intuitive manner. A classification method is introduced where a cumulative score volume is created which takes into account each of three acquisition types. This score volume is integrated into a volume rendering framework which allows the user to view the prostate gland, the multi‐modal score values, and the surrounding anatomy. A visibility persistence mode is introduced to automatically avoid full occlusion of a selected score and indicate overlaps. The use of GPU‐accelerated multi‐modal single‐pass ray casting provides an interactive experience. User driven importance rendering allows the user to gain insight into the data and can assist in localization of the disease and treatment planning. We evaluate our results against pathology and radiologists'determinations.     

Perceptual Evaluation of Ghosted View Techniques for the Exploration of Vascular Structures and Embedded Flow

This paper presents three controlled perceptual studies investigating the visualization of the cerebral aneurysm anatomy with embedded flow visualization. We evaluate and compare the common semitransparent visualization technique with a ghosted view and a ghosted view with depth enhancement technique. We analyze the techniques’ ability to facilitate and support the shape and spatial representation of the aneurysm models as well as evaluating the smart visibility characteristics. The techniques are evaluated with respect to the participants accuracy, response time and their personal preferences. We used as stimuli 3D aneurysm models of five clinical datasets. There was overwhelming preference for the two ghosted view techniques over the semitransparent technique. Since smart visibility techniques are rarely evaluated, this paper may serve as orientation for further studies.     

Learning and Applying Color Styles From Feature Films

Directors employ a process called “color grading” to add color styles to feature films. Color grading is used for a number of reasons, such as accentuating a certain emotion or expressing the signature look of a director. We collect a database of feature film clips and label them with tags such as director, emotion, and genre. We then learn a model that maps from the low‐level color and tone properties of film clips to the associated labels. This model allows us to examine a number of common hypotheses on the use of color to achieve goals, such as specific emotions. We also describe a method to apply our learned color styles to new images and videos. Along with our analysis of color grading techniques, we demonstrate a number of images and videos that are automatically filtered to resemble certain film styles.     

Map Displays for the Analysis of Scalar Data on Cerebral Aneurysm Surfaces

Cerebral aneurysms result from a congenital or evolved weakness of stabilizing parts of the vessel wall and potentially lead to rupture and a life-threatening bleeding. Current medical research concentrates on the integration of blood flow simulation results for risk assessment of cerebral aneurysms. Scalar flow characteristics close to the aneurysm surface, such as wall shear stress, form an important part of the simulation results. Aneurysms exhibit variable surface shapes with only few landmarks. Therefore, the exploration and mental correlation of different surface regions is a difficult task. In this paper, we present an approach for the intuitive and interactive overview visualization of near wall flow data that is mapped onto the surface of a 3D model of a cerebral aneurysm. We combine a multi-perspective 2D projection map with a standard 3D visualization and present techniques to facilitate the correlation between a 3D model and a related 2D map. An informal evaluation with 4 experienced radiologists has shown that the map-based overview actually improves the surface exploration. Furthermore, different color schemes were discussed and, as a result, an appropriate color scheme for the visual analysis of the wall shear stress is presented.     

Semantic‐Preserving Word Clouds by Seam Carving

Word clouds are proliferating on the Internet and have received much attention in visual analytics. Although word clouds can help users understand the major content of a document collection quickly, their ability to visually compare documents is limited. This paper introduces a new method to create semantic‐preserving word clouds by leveraging tailored seam carving, a well‐established content‐aware image resizing operator. The method can optimize a word cloud layout by removing a left‐to‐right or top‐to‐bottom seam iteratively and gracefully from the layout. Each seam is a connected path of low energy regions determined by a Gaussian‐based energy function. With seam carving, we can pack the word cloud compactly and effectively, while preserving its overall semantic structure. Furthermore, we design a set of interactive visualization techniques for the created word clouds to facilitate visual text analysis and comparison. Case studies are conducted to demonstrate the effectiveness and usefulness of our techniques.     

Context Aware Terrain Visualization for Wayfinding and Navigation

To assist wayfinding and navigation, the display of maps and driving directions on mobile devices is nowadays commonplace. While existing system can naturally exploit GPS information to facilitate orientation, the inherently limited screen space is often perceived as a drawback compared to traditional street maps as it constrains the perception of contextual information. Moreover, occlusion issues add to this problem if the environment is shown from the popular egocentric perspective. In this paper we describe an interactive visualization system that addresses these problems by reallocating the available screen space. At the heart of our system are three novel visualization techniques: First, we propose a non‐standard perspective that allows to blend between the familiar pedestrian perspective and a standard map depiction with reduced occlusion. Second, we derive an efficient deformation technique that allows an interactive allocation of screen space to areas of interest like e.g. nearby touristic attractions. Finally, a path adaptive isometric perspective is proposed that reveals otherwise hidden facades in top‐down views. We describe efficient implementations of all techniques and exemplify our interactive system on real world urban models.     

Reusable Visualizations and Animations for Surgery Planning

For surgical planning, the exploration of 3D visualizations and 2D slice views is essential. However, the generation of visualizations which support the specific treatment decisions is very tedious. Therefore, the reuse of once designed visualizations for similar cases can strongly accelerate the process of surgical planning. We present a new technique that enables the easy reuse of both medical visualization types: 3D scenes and 2D slice views. We introduce the keystates as a concept to describe the state of a visualization in a general manner. They can be easily applied to new datasets to create similar visualizations. Keystates can be shared between surgeons of one specialization to reproduce and document the planning process for collaborative work. Furthermore, animations can support the surgeon on individual exploration and are also useful in collaborative environments, where complex issues must be presented in a short time. Therefore, we provide a framework, where animations can be visually designed by surgeons during their exploration process without any programming or authoring skills. We discuss several transitions between different visualizations and present an application from clinical routine.     

Shrinkability Maps for Content‐Aware Video Resizing

A novel method is given for content‐aware video resizing, i.e. targeting video to a new resolution (which may involve aspect ratio change) from the original. We precompute a per‐pixel cumulative shrinkability map which takes into account both the importance of each pixel and the need for continuity in the resized result. (If both x and y resizing are required, two separate shrinkability maps are used, otherwise one suffices). A random walk model is used for efficient offline computation of the shrinkability maps. The latter are stored with the video to create a multi‐sized video, which permits arbitrary‐sized new versions of the video to be later very efficiently created in real‐time, e.g. by a video‐on‐demand server supplying video streams to multiple devices with different resolutions. These shrinkability maps are highly compressible, so the resulting multi‐sized videos are typically less than three times the size of the original compressed video. A scaling function operates on the multi‐sized video, to give the new pixel locations in the result, giving a high‐quality content‐aware resized video. Despite the great efficiency and low storage requirements for our method, we produce results of comparable quality to state‐of‐the‐art methods for content‐aware image and video resizing.     

Interactive Visualization of Function Fields by Range-Space Segmentation

We present a dimension reduction and feature extraction method for the visualization and analysis of function field data. Function fields are a class of high-dimensional, multi-variate data in which data samples are one-dimensional scalar functions. Our approach focuses upon the creation of high-dimensional range-space segmentations, from which we can generate meaningful visualizations and extract separating surfaces between features. We demonstrate our approach on high-dimensional spectral imagery, and particulate pollution data from air quality simulations.     

Anatomy‐Guided Multi‐Level Exploration of Blood Flow in Cerebral Aneurysms

For cerebral aneurysms, the ostium, the area of inflow, is an important anatomic landmark, since it separates the pathological vessel deformation from the healthy parent vessel. A better understanding of the inflow characteristics, the flow inside the aneurysm and the overall change of pre‐ and post‐aneurysm flow in the parent vessel provide insights for medical research and the development of new risk‐reduced treatment options. We present an approach for a qualitative, visual flow exploration that incorporates the ostium and derived anatomical landmarks. It is divided into three scopes: a global scope for exploration of the in‐ and outflow, an ostium scope that provides characteristics of the flow profile close to the ostium and a local scope for a detailed exploration of the flow in the parent vessel and the aneurysm. The approach was applied to five representative datasets, including measured and simulated blood flow. Informal interviews with two board‐certified radiologists confirmed the usefulness of the provided exploration tools and delivered input for the integration of the ostium‐based flow analysis into the overall exploration workflow.     

Generating Pointillism Paintings Based on Seurat's Color Composition

This paper presents a novel example‐based stippling technique that employs a simple and intuitive concept to convert a color image into a pointillism painting. Our method relies on analyzing and imitating the color distributions of Seurat's paintings to obtain a statistical color model. Then, this model can be easily combined with the modified multi‐class blue noise sampling to stylize an input image with characteristics of color composition in Seurat's paintings. The blue noise property of the output image also ensures that the color points are randomly located but remain spatially uniform. In our experiments, the multivariate goodness‐of‐fit tests were adopted to quantitatively analyze the results of the proposed and previous methods, further confirming that the color composition of our results are more similar to Seurat's painting style than that of previous approaches. Additionally, we also conducted a user study participated by artists to qualitatively evaluate the synthesized images of the proposed method.     

Discrete Multi‐Material Interface Reconstruction for Volume Fraction Data

Material interface reconstruction (MIR) is the task of constructing boundary interfaces between regions of homogeneous material, while satisfying volume constraints, over a structured or unstructured spatial domain. In this paper, we present a discrete approach to MIR based upon optimizing the labeling of fractional volume elements within a discretization of the problem's original domain. We detail how to construct and initially label a discretization, and introduce a volume conservative swap move for optimization. Furthermore, we discuss methods for extracting and visualizing material interfaces from the discretization. Our technique has significant advantages over previous methods: we produce interfaces between multiple materials that are continuous across cell boundaries for time‐varying and static data in arbitrary dimension with bounded error.     

A Gradient‐Based Comparison Measure for Visual analysis of Multifield Data

We introduce a multifield comparison measure for scalar fields that helps in studying relations between them. The comparison measure is insensitive to noise in the scalar fields and to noise in their gradients. Further, it can be computed robustly and efficiently. Results from the visual analysis of various data sets from climate science and combustion applications demonstrate the effective use of the measure.