Per‐Vertex Defocus Blur for Stochastic Rasterization

We present user‐controllable and plausible defocus blur for a stochastic rasterizer. We modify circle of confusion coefficients per vertex to express more general defocus blur, and show how the method can be applied to limit the foreground blur, extend the in‐focus range, simulate tilt‐shift photography, and specify per‐object defocus blur. Furthermore, with two simplifying assumptions, we show that existing triangle coverage tests and tile culling tests can be used with very modest modifications. Our solution is temporally stable and handles simultaneous motion blur and depth of field.     

Optimizing Photo Composition

Aesthetic images evoke an emotional response that transcends mere visual appreciation. In this work we develop a novel computational means for evaluating the composition aesthetics of a given image based on measuring several well‐grounded composition guidelines. A compound operator of crop‐and‐retarget is employed to change the relative position of salient regions in the image and thus to modify the composition aesthetics of the image. We propose an optimization method for automatically producing a maximally‐aesthetic version of the input image. We validate the performance of the method and show its effectiveness in a variety of experiments.     

Real‐time Depth of Field Rendering via Dynamic Light Field Generation and Filtering

We present a new algorithm for efficient rendering of high‐quality depth‐of‐field (DoF) effects. We start with a single rasterized view (reference view) of the scene, and sample the light field by warping the reference view to nearby views. We implement the algorithm using NVIDIA's CUDA to achieve parallel processing, and exploit the atomic operations to resolve visibility when multiple pixels warp to the same image location. We then directly synthesize DoF effects from the sampled light field. To reduce aliasing artifacts, we propose an image‐space filtering technique that compensates for spatial undersampling using MIP mapping. The main advantages of our algorithm are its simplicity and generality. We demonstrate interactive rendering of DoF effects in several complex scenes. Compared to existing methods, ours does not require ray tracing and hence scales well with scene complexity.     

Isosurface Similarity Maps

In this paper, we introduce the concept of isosurface similarity maps for the visualization of volume data. Iso‐surface similarity maps present structural information of a volume data set by depicting similarities between individual isosurfaces quantified by a robust information‐theoretic measure. Unlike conventional histograms, they are not based on the frequency of isovalues and/or derivatives and therefore provide complementary information. We demonstrate that this new representation can be used to guide transfer function design and visualization parameter specification. Furthermore, we use isosurface similarity to develop an automatic parameter‐free method for identifying representative isovalues. Using real‐world data sets, we show that isosurface similarity maps can be a useful addition to conventional classification techniques.     

Image and Video Abstraction by Anisotropic Kuwahara Filtering

We present a non‐photorealistic rendering technique to transform color images and videos into painterly abstractions. It is based on a generalization of the Kuwahara filter that is adapted to the local shape of features, derived from the smoothed structure tensor. Contrary to conventional edge‐preserving filters, our filter generates a painting‐like flattening effect along the local feature directions while preserving shape boundaries. As opposed to conventional painting algorithms, it produces temporally coherent video abstraction without extra processing. The GPU implementation of our method processes video in real‐time. The results have the clearness of cartoon illustrations but also exhibit directional information as found in oil paintings.     

Practical Real‐Time Lens‐Flare Rendering

We present a practical real‐time approach for rendering lens‐flare effects. While previous work employed costly ray tracing or complex polynomial expressions, we present a coarser, but also significantly faster solution. Our method is based on a first‐order approximation of the ray transfer in an optical system, which allows us to derive a matrix that maps lens flare‐producing light rays directly to the sensor. The resulting approach is easy to implement and produces physically‐plausible images at high framerates on standard off‐the‐shelf graphics hardware.     

Two‐Colored Pixels

In this paper we show how to use two‐colored pixels as a generic tool for image processing. We apply two‐colored pixels as a basic operator as well as a supporting data structure for several image processing applications. Traditionally, images are represented by a regular grid of square pixels with one constant color each. In the two‐colored pixel representation, we reduce the image resolution and replace blocks of N × N pixels by one square that is split by a (feature) line into two regions with constant colors. We show how the conversion of standard mono‐colored pixel images into two‐colored pixel images can be computed efficiently by applying a hierarchical algorithm along with a CUDA‐based implementation. Two‐colored pixels overcome some of the limitations that classical pixel representations have, and their feature lines provide minimal geometric information about the underlying image region that can be effectively exploited for a number of applications. We show how to use two‐colored pixels as an interactive brush tool, achieving realtime performance for image abstraction and non‐photorealistic filtering. Additionally, we propose a realtime solution for image retargeting, defined as a linear minimization problem on a regular or even adaptive two‐colored pixel image. The concept of two‐colored pixels can be easily extended to a video volume, and we demonstrate this for the example of video retargeting.     

Improving Photo Composition Elegantly: Considering Image Similarity During Composition Optimization

Optimization of images with bad compositions has attracted increasing attention in recent years. Previous methods however seldomly consider image similarity when improving composition aesthetics. This may lead to significant content changes or bring large distortions, resulting in an unpleasant user experience. In this paper, we present a new algorithm for improving image composition aesthetics, while retaining faithful, as much as possible, to the original image content. Our method computes an improved image using a unified model of composition aesthetics and image similarity. The term of composition aesthetics obeys the rule of thirds and aims to enhance image composition. The similarity term in contrast penalizes image difference and distortion caused by composition adjustment. We use an edge‐based measure of structure similarity which nearly coincides with human visual perception to compare the optimized image with the original one. We describe an effective scheme to generate the optimized image with the objective model. Our algorithm is able to produce the recomposed images with minimal visual distortions in an elegant and user controllable manner. We show the superiority of our algorithm by comparing our results with those by previous methods.     

ProbExplorer: Uncertainty‐guided Exploration and Editing of Probabilistic Medical Image Segmentation

In this paper, we develop an interactive analysis and visualization tool for probabilistic segmentation results in medical imaging. We provide a systematic approach to analyze, interact and highlight regions of segmentation uncertainty. We introduce a set of visual analysis widgets integrating different approaches to analyze multivariate probabilistic field data with direct volume rendering. We demonstrate the user's ability to identify suspicious regions (e.g. tumors) and correct the misclassification results using a novel uncertainty‐based segmentation editing technique. We evaluate our system and demonstrate its usefulness in the context of static and time‐varying medical imaging datasets.     

Adaptive Cross‐sections of Anatomical Models

Medical illustrations have been used for a long time for teaching and communicating information for diagnosis or surgery planning. Illustrative visualization systems create methods and tools that adapt traditional illustration techniques to enhance the result of renderings. Clipping the volume is a popular operation in volume rendering for inspecting the inner parts, though it may remove some information of the context that is worth preserving. In this paper we present a new editing technique based on the use of clipping planes, direct structure extrusion, and illustrative methods, which preserves the context by adapting the extruded region to the structures of interest of the volumetric model. We will show that users may interactively modify the clipping plane and edit the structures to highlight, in order to easily create the desired result. Our approach works with segmented volume models and non‐segmented ones. In the last case, a local segmentation is performed on‐the‐fly. We will demonstrate the efficiency and utility of our method.     

Multi‐layer Depth Peeling by Single‐Pass Rasterisation for Faster Isosurface Raytracing on GPUs

Empty‐space skipping is an essential acceleration technique for volume rendering. Image‐order empty‐space skipping is not well suited to GPU implementation, since it must perform checks on, essentially, a per‐sample basis, as in kd‐tree traversal, which can lead to a great deal of divergent branching at runtime, which is very expensive in a modern GPU pipeline. In contrast, object‐order empty‐space skipping is extremely fast on a GPU and has negligible overheads compared with approaches without empty‐space skipping, since it employs the hardware unit for rasterisation. However, previous object‐order algorithms have been able to skip only exterior empty space and not the interior empty space that lies inside or between volume objects. In this paper, we address these issues by proposing a multi‐layer depth‐peeling approach that can obtain all of the depth layers of the tight‐fitting bounding geometry of the isosurface by a single rasterising pass. The maximum count of layers peeled by our approach can be up to thousands, while maintaining 32‐bit float‐point accuracy, which was not possible previously. By raytracing only the valid ray segments between each consecutive pair of depth layers, we can skip both the interior and exterior empty space efficiently. In comparisons with 3 state‐of‐the‐art GPU isosurface rendering algorithms, this technique achieved much faster rendering across a variety of data sets.     

Scalable Programmable Motion Effects on GPUs

We present an efficient and scalable system that enables programmable motion effects on GPUs. Our system is based on the framework proposed by Schmid et al. [ [SSBG10] ] that extends the concept of a surface shader to that of a programmable motion effect. While capable of expressing a variety of motion depiction styles, the execution of motion effect programs requires global knowledge about all portions of an object's surface that passes in front of a pixel during an arbitrarily long period of time, resulting in extremely high memory usage and significantly restricting the degree of parallelism of typical GPU rendering algorithms that parallelize computations over pixels in each frame of animations. To address this problem, we design our system to process multiple frames of a pixel in parallel. This new parallelization approach enables better utilization of GPU memory and also makes it possible to design an efficient out‐of‐core algorithm required in rendering real‐world animations. We also develop an analytical visibility algorithm to resolve depth conflicts of objects, reducing the required temporal resampling rate and further exposing parallelism. Experiments show that we are able to handle very large scenes and improve runtime performance up to an order of magnitude.     

Visualizing the Positional and Geometrical Variability of Isosurfaces in Uncertain Scalar Fields

We present a novel approach for visualizing the positional and geometrical variability of isosurfaces in uncertain 3D scalar fields. Our approach extends recent work by Pöthkow and Hege [ [PH10] ] in that it accounts for correlations in the data to determine more reliable isosurface crossing probabilities. We introduce an incremental update‐scheme that allows integrating the probability computation into front‐to‐back volume ray‐casting efficiently. Our method accounts for homogeneous and anisotropic correlations, and it determines for each sampling interval along a ray the probability of crossing an isosurface for the first time. To visualize the positional and geometrical uncertainty even under viewing directions parallel to the surface normal, we propose a new color mapping scheme based on the approximate spatial deviation of possible surface points from the mean surface. The additional use of saturation enables to distinguish between areas of high and low statistical dependence. Experimental results confirm the effectiveness of our approach for the visualization of uncertainty related to position and shape of convex and concave isosurface structures.     

Video Painting via Motion Layer Manipulation

Temporal coherence is an important problem in Non‐Photorealistic Rendering for videos. In this paper, we present a novel approach to enhance temporal coherence in video painting. Instead of painting on video frame, our approach first partitions the video into multiple motion layers, and then places the brush strokes on the layers to generate the painted imagery. The extracted motion layers consist of one background layer and several object layers in each frame. Then, background layers from all the frames are aligned into a panoramic image, on which brush strokes are placed to paint the background in one‐shot. The strokes used to paint object layers are propagated frame by frame using smooth transformations defined by thin plate splines. Once the background and object layers are painted, they are projected back to each frame and blent to form the final painting results. Thanks to painting a single image, our approach can completely eliminate the flickering in background, and temporal coherence on object layers is also significantly enhanced due to the smooth transformation over frames. Additionally, by controlling the painting strokes on different layers, our approach is easy to generate painted video with multi‐style. Experimental results show that our approach is both robust and efficient to generate plausible video painting.     

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 graph's 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.     

View and Time Interpolation in Image Space

The ability to interpolate between images taken at different time and viewpoints directly in image space opens up new possiblities. The goal of our work is to create plausible in‐between images in real time without the need for an intermediate 3D reconstruction. This enables us to also interpolate between images recorded with uncalibrated and unsynchronized cameras. In our approach we use a novel discontiniuity preserving image deformation model to robustly estimate dense correspondences based on local homographies. Once correspondences have been computed we are able to render plausible in‐between images in real time while properly handling occlusions. We discuss the relation of our approach to human motion perception and other image interpolation techniques.     

Illustrative Parallel Coordinates

Illustrative parallel coordinates (IPC) is a suite of artistic rendering techniques for augmenting and improving parallel coordinate (PC) visualizations. IPC techniques can be used to convey a large amount of information about a multidimensional dataset in a small area of the screen through the following approaches: (a) edge‐bundling through splines; (b) visualization of “branched ” clusters to reveal the distribution of the data; (c) opacity‐based hints to show cluster density; (d) opacity and shading effects to illustrate local line density on the parallel axes; and (e) silhouettes, shadows and halos to help the eye distinguish between overlapping clusters. Thus, the primary goal of this work is to convey as much information as possible in a manner that is aesthetically pleasing and easy to understand for non‐experts.     

Topological Landscape Ensembles for Visualization of Scalar‐Valued Functions

Visual representation techniques enable perception and exploration of scientific data. Following the topological landscapes metaphor of Weber et al., we provide a new algorithm for visualizing scalar functions defined on simply connected domains of arbitrary dimension. For a potentially high dimensional scalar field, our algorithm produces a collection of, in some sense complete, two‐dimensional terrain models whose contour trees and corresponding topological persistences are identical to those of the input scalar field. The algorithm exactly preserves the volume of each region corresponding to an arc in the contour tree. We also introduce an efficiently computable metric on terrain models we generate. Based on this metric, we develop a tool that can help the users to explore the space of possible terrain models.     

Efficient Packing of Arbitrary Shaped Charts for Automatic Texture Atlas Generation

Texture atlases are commonly used as representations for mesh parameterizations in numerous applications including texture and normal mapping. Therefore, packing is an important post‐processing step that tries to place and orient the single parameterizations in a way that the available space is used as efficiently as possible. However, since packing is NP hard, only heuristics can be used in practice to find near‐optimal solutions. In this publication we introduce the new search space of modulo valid packings. The key idea thereby is to allow the texture charts to wrap around in the atlas. By utilizing this search space we propose a new algorithm that can be used in order to automatically pack texture atlases. In the evaluation section we show that our algorithm achieves solutions with a significantly higher packing efficiency when compared to the state of the art, especially for complex packing problems.     

DTI in Context: Illustrating Brain Fiber Tracts In Situ

We present an interactive illustrative visualization method inspired by traditional pen‐and‐ink illustration styles. Specifically, we explore how to provide context around DTI fiber tracts in the form of surfaces of the brain, the skull, or other objects such as tumors. These contextual surfaces are derived from either segmentation data or generated using interactive iso‐surface extraction and are rendered with a flexible, slice‐based hatching technique, controlled with ambient occlusion. This technique allows us to produce a consistent and frame‐coherent appearance with precise control over the lines. In addition, we provide context through cutting planes onto which we render gray matter with stippling. Together, our methods not only facilitate the interactive exploration and illustration of brain fibers within their anatomical context but also allow us to produce high‐quality images for print reproduction. We provide evidence for the success of our approach with an informal evaluation with domain experts.