Interactive Volume Rendering with Dynamic Ambient Occlusion and Color Bleeding

We propose a method for rendering volumetric data sets at interactive frame rates while supporting dynamic ambient occlusion as well as an approximation to color bleeding. In contrast to ambient occlusion approaches for polygonal data, techniques for volumetric data sets have to face additional challenges, since by changing rendering parameters, such as the transfer function or the thresholding, the structure of the data set and thus the light interactions may vary drastically. Therefore, during a preprocessing step which is independent of the rendering parameters we capture light interactions for all combinations of structures extractable from a volumetric data set. In order to compute the light interactions between the different structures, we combine this preprocessed information during rendering based on the rendering parameters defined interactively by the user. Thus our method supports interactive exploration of a volumetric data set but still gives the user control over the most important rendering parameters. For instance, if the user alters the transfer function to extract different structures from a volumetric data set the light interactions between the extracted structures are captured in the rendering while still allowing interactive frame rates. Compared to known local illumination models for volume rendering our method does not introduce any substantial rendering overhead and can be integrated easily into existing volume rendering applications. In this paper we will explain our approach, discuss the implications for interactive volume rendering and present the achieved results.     

Fast Soft Self‐Shadowing on Dynamic Height Fields

We present a new, real‐time method for rendering soft shadows from large light sources or lighting environments on dynamic height fields. The method first computes a horizon map for a set of azimuthal directions. To reduce sampling, we compute a multi‐resolution pyramid on the height field. Coarser pyramid levels are indexed as the distance from caster to receiver increases. For every receiver point and every azimuthal direction, a smooth function of blocking angle in terms of log distance is reconstructed from a height difference sample at each pyramid level. This function's maximum approximates the horizon angle. We then sum visibility at each receiver point over wedges determined by successive pairs of horizon angles. Each wedge represents a linear transition in blocking angle over its azimuthal extent. It is precomputed in the order‐4 spherical harmonic (SH) basis, for a canonical azimuthal origin and fixed extent, resulting in a 2D table. The SH triple product of 16D vectors representing lighting, total visibility, and diffuse reflectance then yields the soft‐shadowed result. Two types of light sources are considered; both are distant and low‐frequency. Environmental lights require visibility sampling around the complete 360 ° azimuth, while key lights sample visibility within a partial swath. Restricting the swath concentrates samples where the light comes from (e.g. 3 azimuthal directions vs. 16‐32 for a full swath) and obtains sharper shadows. Our GPU implementation handles height fields up to 1024 × 1024 in real‐time. The computation is simple, local, and parallel, with performance independent of geometric content.     

An Example-based Procedural System for Element Arrangement

We present a method for synthesizing two dimensional (2D) element arrangements from an example. The main idea is to combine texture synthesis techniques based‐on a local neighborhood comparison and procedural modeling systems based‐on local growth. Given a user‐specified reference pattern, our system analyzes neigh‐borhood information of each element by constructing connectivity. Our synthesis process starts with a single seed and progressively places elements one by one by searching a reference element which has local features that are the most similar to the target place of the synthesized pattern. To support creative design activities, we introduce three types of interaction for controlling global features of the resulting pattern, namely a spray tool, a flow field tool, and a boundary tool. We also introduce a global optimization process that helps to avoid local error concentrations. We illustrate the feasibility of our method by creating several types of 2D patterns.     

Deep Opacity Maps

We present a new method for rapidly computing shadows from semi‐transparent objects like hair. Our deep opacity maps method extends the concept of opacity shadow maps by using a depth map to obtain a per pixel distribution of opacity layers. This approach eliminates the layering artifacts of opacity shadow maps and requires far fewer layers to achieve high quality shadow computation. Furthermore, it is faster than the density clustering technique, and produces less noise with comparable shadow quality. We provide qualitative comparisons to these previous methods and give performance results. Our algorithm is easy to implement, faster, and more memory efficient, enabling us to generate high quality hair shadows in real‐time using graphics hardware on a standard PC.     

A privacy preserving technique for distance-based classification with worst case privacy guarantees

There has been relatively little work on privacy preserving techniques for distance based mining. The most widely used ones are additive perturbation methods and orthogonal transform based methods. These methods concentrate on privacy protection in the average case and provide no worst case privacy guarantee. However, the lack of privacy guarantee makes it difficult to use these techniques in practice, and causes possible privacy breach under certain attacking methods. This paper proposes a novel privacy protection method for distance based mining algorithms that gives worst case privacy guarantees and protects the data against correlation-based and transform-based attacks. This method has the following three novel aspects. First, this method uses a framework to provide theoretical bound of privacy breach in the worst case. This framework provides easy to check conditions that one can determine whether a method provides worst case guarantee. A quick examination shows that special types of noise such as Laplace noise provide worst case guarantee, while most existing methods such as adding normal or uniform noise, as well as random projection method do not provide worst case guarantee. Second, the proposed method combines the favorable features of additive perturbation and orthogonal transform methods. It uses principal component analysis to decorrelate the data and thus guards against attacks based on data correlations. It then adds Laplace noise to guard against attacks that can recover the PCA transform. Third, the proposed method improves accuracy of one of the popular distance-based classification algorithms: K-nearest neighbor classification, by taking into account the degree of distance distortion introduced by sanitization. Extensive experiments demonstrate the effectiveness of the proposed method.     

A Numerical Study of Diagonally Split Runge–Kutta Methods for PDEs with Discontinuities

Diagonally split Runge–Kutta (DSRK) time discretization methods are a class of implicit time-stepping schemes which offer both high-order convergence and a form of nonlinear stability known as unconditional contractivity. This combination is not possible within the classes of Runge–Kutta or linear multistep methods and therefore appears promising for the strong stability preserving (SSP) time-stepping community which is generally concerned with computing oscillation-free numerical solutions of PDEs. Using a variety of numerical test problems, we show that although second- and third-order unconditionally contractive DSRK methods do preserve the strong stability property for all time step-sizes, they suffer from order reduction at large step-sizes. Indeed, for time-steps larger than those typically chosen for explicit methods, these DSRK methods behave like first-order implicit methods. This is unfortunate, because it is precisely to allow a large time-step that we choose to use implicit methods. These results suggest that unconditionally contractive DSRK methods are limited in usefulness as they are unable to compete with either the first-order backward Euler method for large step-sizes or with Crank–Nicolson or high-order explicit SSP Runge–Kutta methods for smaller step-sizes. We also present stage order conditions for DSRK methods and show that the observed order reduction is associated with the necessarily low stage order of the unconditionally contractive DSRK methods. The work of C.B. Macdonald was partially supported by an NSERC Canada PGS-D scholarship, a grant from NSERC Canada, and a scholarship from the Pacific Institute for the Mathematical Sciences (PIMS). The work of S. Gottlieb was supported by AFOSR grant number FA9550-06-1-0255. The work of S.J. Ruuth was partially supported by a grant from NSERC Canada.     

Color Subspaces as Photometric Invariants

Complex reflectance phenomena such as specular reflections confound many vision problems since they produce image ‘features’ that do not correspond directly to intrinsic surface properties such as shape and spectral reflectance. A common approach to mitigate these effects is to explore functions of an image that are invariant to these photometric events. In this paper we describe a class of such invariants that result from exploiting color information in images of dichromatic surfaces. These invariants are derived from illuminant-dependent ‘subspaces’ of RGB color space, and they enable the application of Lambertian-based vision techniques to a broad class of specular, non-Lambertian scenes. Using implementations of recent algorithms taken from the literature, we demonstrate the practical utility of these invariants for a wide variety of applications, including stereo, shape from shading, photometric stereo, material-based segmentation, and motion estimation.     

Learning traversability models for autonomous mobile vehicles

Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate the terrain around them and build up an internal representation that enables them to navigate. This paper addresses the question of how to use sensor data to learn properties of the environment and use this knowledge to predict which regions of the environment are traversable. The approach makes use of sensed information from range sensors (stereo or ladar), color cameras, and the vehicle’s navigation sensors. Models of terrain regions are learned from subsets of pixels that are selected by projection into a local occupancy grid. The models include color and texture as well as traversability information obtained from an analysis of the range data associated with the pixels. The models are learned without supervision, deriving their properties from the geometry and the appearance of the scene. The models are used to classify color images and assign traversability costs to regions. The classification does not use the range or position information, but only color images. Traversability determined during the model-building phase is stored in the models. This enables classification of regions beyond the range of stereo or ladar using the information in the color images. The paper describes how the models are constructed and maintained, how they are used to classify image regions, and how the system adapts to changing environments. Examples are shown from the implementation of this algorithm in the DARPA Learning Applied to Ground Robots (LAGR) program, and an evaluation of the algorithm against human-provided ground truth is presented.

Multivariate statistical process control based on multiway locality preserving projections

An approach for multivariate statistical process control based on multiway locality preserving projections (LPP) is presented. The recently developed LPP is a linear dimensionality reduction technique for preserving the neighborhood structure of the data set. It is characterized by capturing the intrinsic structure of the observed data and finding more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. In this study, LPP is used to extract the intrinsic geometrical structure of the process data. Hotelling’s T² (D) and the squared prediction error (SPE or Q) statistic charts for on-line monitoring are then presented, and the contribution plots of these two statistical indices are used for fault diagnosis. Moreover, a moving window technique is used for the implementation of on-line monitoring. Case study was carried out with the data of industrial penicillin fed-batch cultivations. As a comparison, the results obtained with the MPCA are also presented. It is concluded that the Multiway LPP (MLPP) outperforms the conventional MPCA. Finally, the robustness of the MLPP monitoring is analyzed by adding noises to the original data.     

一种基于遗传算法的彩色图像分割改进算法

图像分割是进行图像理解的基础,也是图像工程技术中一个重要的问题.近几年来关于图像的分割方法层出不穷,但随着多媒体技术和Internet技术的发展,彩色图像分割处理的准确性和实时性要求也越来越高.为此提出了一种基于遗传算法的彩色图像分割改进算法.经过大量的对比实验表明,用这种方法分割目标和背景区域差别较大的彩色图像具有分割效果好、实时性、鲁棒性强的特点,是一种较为理想的分割方法.