Showing papers on "Bounding overwatch published in 2007"

Early Split Clipping for Bounding Volume Hierarchies

Abstract: Despite their algorithmic elegance and robustness, bounding volume hierarchies (BVHs) have not reached the performance of kd- trees for ray tracing. BVHs do not adapt well to scenes containing large triangles with overlapping bounding boxes. A node cannot be smaller than the bounding box of the primitives it contains. Consequently, the leafs and internal nodes will overlap substantially. This slows down ray tracing, because the number of traversal steps and ray-primitive intersections is increased. Unfortunately, this kind of geometry is common in architectural scenes and low-poly CAD models. In this paper, we present a building algorithm for BVHs that handles such scenes more efficiently. The restriction that each primitive must be contained in exactly one leaf node is relaxed. Bounding boxes of large primitives are refined with recursive split clipping before constructing the hierarchy. The resulting volumes are used as input for a regular BVH building algorithm. Neither scene geometry nor traversal or building algorithms must be modified in any way. The resulting hierarchies are superior for a wide range of data sets, leading to speed-ups of more than a factor of three.

Bounding messages for free in security protocols

Abstract: The verification of security protocols has been proven to be undecidable in general. Different approaches use simplifying hypotheses in order to obtain decidability for interesting subclasses. Amongst the most common is type abstraction, i.e. considering only well-typed runs, therefore bounding message length. In this paper, we show how to get message boundedness "for free" under a reasonable (syntactic) assumption on protocols, which we call well-formedness. This enables us to improve existing decidability results.

Variational 3D Shape Segmentation for Bounding Volume Computation

Abstract: We propose a variational approach to computing an optimal segmentation of a 3D shape for computing a union of tight bounding volumes. Based on an affine invariant measure of e-tightness, the resemblance to ellipsoid, a novel functional is formulated that governs an optimization process to obtain a partition with multiple components. Refinement of segmentation is driven by application-specific error measures, so that the final bounding volume meets pre-specified user requirement. We present examples to demonstrate the effectiveness of our method and show that it works well for computing ellipsoidal bounding volumes as well as oriented bounding boxes.

Generalizing the Optimality of Multi-step k -Nearest Neighbor Query Processing

Abstract: Similarity search algorithms that directly rely on index structures and require a lot of distance computations are usually not applicable to databases containing complex objects and defining costly distance functions on spatial, temporal and multimedia data. Rather, the use of an adequate multi-step query processing strategy is crucial for the performance of a similarity search routine that deals with complex distance functions. Reducing the number of candidates returned from the filter step which then have to be exactly evaluated in the refinement step is fundamental for the efficiency of the query process. The state-of-the-art multi-step k-nearest neighbor (kNN) search algorithms are designed to use only a lower bounding distance estimation for candidate pruning. However, in many applications, also an upper bounding distance approximation is available that can additionally be used for reducing the number of candidates. In this paper, we generalize the traditional concept of R-optimality and introduce the notion of RI -optimality depending on the distance information I available in the filter step. We propose a new multi-step kNN search algorithm that utilizes lower- and upper bounding distance information (Ilu) in the filter step. Furthermore, we show that, in contrast to existing approaches, our proposed solution is RIlu - optimal. In an experimental evaluation, we demonstrate the significant performance gain over existing methods.

How good is an ensemble at capturing truth? Using bounding boxes for forecast evaluation

Abstract: Ensemble prediction systems aim to account for uncertainties of initial conditions and model error. Ensemble forecasting is sometimes viewed as a method of obtaining (objective) probabilistic forecasts. How is one to judge the quality of an ensemble at forecasting a system? The probability that the bounding box of an ensemble captures some target (such as "truth" in a perfect model scenario) provides new statistics for quantifying the quality of an ensemble prediction system; information that can provide insight all the way from ensemble system design and to user decision support. These simple measures clarify basic questions, like, what the minimal size of an ensemble should be. To illustrate their utility, bounding boxes are used in the imperfect model context to quantify the dierences between ensemble forecasting with a stochastic-model ensemble prediction system and a deterministic-model prediction system. Examining forecasts via their bounding boxes statistics provides illustration of how adding stochastic terms to an imperfect model may improve forecasts even when the underlying system is deterministitc. Copyright c 0000 Royal Meteorological Society

Some Notes on Bounding

Abstract: In this paper I argue for a specific way to understand successive cyclic movement by showing that (i) the conceptualization of successive cyclicity I examine requires a ban on movement that is too short, and (ii) the ban required is the one that is empirically superior to recent alternative ways of defining lower bounds on movement. Empirical arguments come from the domains of applicative and psych constructions.

Studies in lower bounding probability of evidence using the Markov inequality

Abstract: Computing the probability of evidence even with known error bounds is NP-hard. In this paper we address this hard problem by settling on an easier problem. We propose an approximation which provides high confidence lower bounds on probability of evidence but does not have any guarantees in terms of relative or absolute error. Our proposed approximation is a randomized importance sampling scheme that uses the Markov inequality. However, a straight-forward application of the Markov inequality may lead to poor lower bounds. We therefore propose several heuristic measures to improve its performance in practice. Empirical evaluation of our scheme with state-of-the-art lower bounding schemes reveals the promise of our approach.

Generalizing the optimality of multi-step k-nearest neighbor query processing

Abstract: Similarity search algorithms that directly rely on index structures and require a lot of distance computations are usually not applicable to databases containing complex objects and defining costly distance functions on spatial, temporal and multimedia data. Rather, the use of an adequate multi-step query processing strategy is crucial for the performance of a similarity search routine that deals with complex distance functions. Reducing the number of candidates returned from the filter step which then have to be exactly evaluated in the refinement step is fundamental for the efficiency of the query process. The state-of-the-art multi-step k-nearest neighbor (kNN) search algorithms are designed to use only a lower bounding distance estimation for candidate pruning. However, in many applications, also an upper bounding distance approximation is available that can additionally be used for reducing the number of candidates. In this paper, we generalize the traditional concept of R-optimality and introduce the notion of RI -optimality depending on the distance information I available in the filter step. We propose a new multi-step kNN search algorithm that utilizes lower- and upper bounding distance information (Ilu) in the filter step. Furthermore, we show that, in contrast to existing approaches, our proposed solution is RIlu - optimal. In an experimental evaluation, we demonstrate the significant performance gain over existing methods.

A Dual Observer Method for Global Stabilization of Nonlinear Systems with Limited and Uncertain Information

Abstract: This paper considers a general setting for the stabilization of a planar nonlinear system given only the measurement of the output state. Additionally, we assume that a certain amount of uncertainties is inherent in the system under consideration, where we only need to know the bounding function of the nonlinear terms. Under this setting we consider a class of systems whose nonlinear bounding functions are polynomially bounded in the unmeasurable state, with orders both greater than and less than one. The primary novelty of this method is the utilization of a dual observer approach, estimating lower-order states and higher-order states in parallel.

Practical Methods for Bounding Type I Error Rate with an Internal Pilot Design

Abstract: New analytic forms for distributions at the heart of internal pilot theory solve many problems inherent to current techniques for linear models with Gaussian errors. Internal pilot designs use a fraction of the data to re-estimate the error variance and modify the final sample size. Too small or too large a sample size caused by an incorrect planning variance can be avoided. However, the usual hypothesis test may need adjustment to control the Type I error rate. A bounding test achieves control of Type I error rate while providing most of the advantages of the unadjusted test. Unfortunately, the presence of both a doubly truncated and an untruncated chi-square random variable complicates the theory and computations. An expression for the density of the sum of the two chi-squares gives a simple form for the test statistic density. Examples illustrate that the new results make the bounding test practical by providing very stable, convergent, and much more accurate computations. Furthermore, the new computational methods are effectively never slower and usually much faster. All results apply to any univariate linear model with fixed predictors and Gaussian errors, with the t-test a special case.

Generalized Class C Markov Chains and Computation of Closed-form Bounding Distributions

Abstract: In this article we first give a characterization of a class of probability transition matrices having closed-form solutions for transient distributions and the steady-state distribution. We propose to apply the stochastic comparison approach to construct bounding chains belonging to this class. Therefore, bounding chains can be analyzed efficiently through closed-form solutions in order to provide bounds on the distributions of the considered Markov chain. We present algorithms to construct upper-bounding matrices in the sense of the ≤st and ≤icx order.

CSR+-tree: Cache-conscious Indexing for High-dimensional Similarity Search

Abstract: In this paper, we propose a novel index structure, the CSR+-tree, to support efficient high-dimensional similarity search in main memory. We introduce quantized bounding spheres (QBSs) that approximate bounding spheres (BSs) or data points. We analyze the respective pros and cons of both QBSs and the previously proposed quantized bounding rectangles (QBRs), and take the best of both worlds by carefully incorporating both of them into the CSR+-tree. We further propose a novel distance computation scheme that eliminates the need for decompressing QBSs or QBRs, which results in significant cost savings. We present an extensive experimental evaluation and analysis of the CSR+-tree, and compare its performance against that of other representative indexes in the literature. Our results show that the CSR+-tree consistently outperforms other index structures.

New upper bounds on the quality of the PCA bounding boxes in r2 and r3

Abstract: Principal component analysis (PCA) is commonly used to compute a bounding box of a point set in Rd. The popularity of this heuristic lies in its speed, easy implementation and in the fact that usually, PCA bounding boxes quite well approximate the minimum-volume bounding boxes.Since there are examples of discrete points sets in the plane, showing that the worst case ratio of the volume ofthe PCA bounding box and the volume of the minimum-volume bounding box tends to infinity,we consider PCA bounding boxes for continuous sets, especially for the convex hull of a point set. Here, we contributenew upper bounds on the approximation factor of PCA bounding boxesof convex sets in R2 and R3.

Adjustable Approach for Collision Detection in Virtual Prototype Environments

Abstract: Collision detection is an important task for the simulation of interactions in virtual assembly. This paper presents a novel collision detection algorithm that uses bounding volumes (BV) of different complexity to find intersecting surfaces interactively. This approach uses the surface knowledge available from the CAD model efficiently to build a hierarchical collision detection algorithm. This method also uses different types of bounding volumes with the advantage that is possible to choose the bounding volumes that are more suitable for each type of application. In this way, the user can allow the existence of different types of bounding volumes in simultaneous and he/her can adjust the selection of BVs to the most effective for the determination of intersecting surfaces.

A New Scalable Estimation for the Logarithmic Moment Generating Function of Network Traffic Flows

Abstract: Summary Logarithmic moment generating functions play important role in network performance evaluation, especially in the application of large deviation type statistical inequalities for bounding and approximating quality of service measures (like transmission link saturation, buffer overflow rate, or traffic loss ratio due to these resource saturations). In this paper we present a new scalable method for upper bounding the logarithmic moment generating functions, primarily based on conditional zero order and first order statistics of the underlying traffic flows. The power of this new method compared to previously known ones from the literature is also demonstrated through numerical examples.

Analysis on Bounding Box Technology in Collision Detection

Abstract: This paper discusses two kinds of collision detection algorithm widely used in present virtual environment: Space Decomposition method and Bounding Box method,elaborates on the intersection detect and effectiveness of axis-aligned bounding boxes(AABB) method,oriented bounding box(OBB) method,fixed directions hulls(FDH) method,and compares the three methods in terms of computing complexity,application scope,etc.

MATLAB Realization of Branch and Bound Method

Abstract: The branch delimitation law may ask the pure integer or the linear programming question,the solution method is composed by the branching step and bounding step."Branching step" has created the condition for the integer linear programming appearance,but "bounding step" may enhance the search the efficiency.Compiles the procedure with MATLAB,completes this complex process through the computer.

Design and implementation of a parameter filter based on virtual bounding rectangles

Abstract: In this paper, the authors propose a high-speed implementation of a parameter filter, using a multidimensional space search algorithm with the introduction of virtual bounding rectangles. The authors have proposed and implemented a KUPF architecture for the problem of packet classification reflecting the complex policies of a router. Because parameter filters generally have filters for multiple parameters, the conventional methods for increasing speed are not applicable to KUPF. R-tree and R*-tree, which construct a search tree using minimum bounding rectangles and accelerate the search, are known search methods for multidimensional spaces. Meanwhile, this proposal represents filter rules in multidimensional space with the introduction of virtual bounding rectangles, while normalizing differences in the properties of each parameter and applying an R*-tree. KUPF-VR, in which this proposal is implemented, had an improved search speed compared to conventional KUPF and was clearly superior in the case of a large number of filter rules, compared to methods specialized for IP packet processing. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(13): 92103, 2007; Published online in Wiley InterScience (). DOI 10.1002sscj.20287

Tunable Bounding Volumes for Elliptic Paraboloids

Abstract: The tightness of the bounding volume is often difficult to adjust to suit different applications. In this paper, we present a technique to derive a tunable bounding volume for elliptic paraboloids, where the tightness can easily be controlled and altered at several levels. Our technique develops such a tunable bounding volume through the optimization process. Bounding volumes thus developed contain the minimal volume at the corresponding level. We implement a geometric application for the elliptic paraboloid, and analyze the tightness of the bounding volumes at different levels. Finally, we demonstrate the feasibility of using our technique in creating new types of geometries as well as several rendered images.