Bounding overwatch

About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.

Shape sensitive geometric complexity

Abstract: The traditional algorithm analysis for geometric problems has focused on worst-case asymptotic complexity. Such analyses have often concluded that certain simple algorithms are worthless because they have poor worst-case performance. However, empirical experience shows that these algorithms tend to perform very well in practice. How does one explain this disparity? This dissertation uses shape sensitive analysis to explores this phenomenon by investigating two problems: the use of bounding boxes in collision detection, and the complexity of geometric permutations in visibility computation. Bounding boxes are used widely in computer graphics as simple approximations of complex objects. Because of their simpler shape, computing with boxes is almost always easier and faster than with the original objects. Experience has shown that the use of bounding boxes greatly improves the performance of geometric algorithms in collision detection, rendering and modeling. However, the goal of proving that bounding boxes maintain high performance in the worst case has remained elusive. In fact, traditional analysis of algorithms has concluded that in the worst-case bounding boxes add nothing but overhead. We will show how to reconcile this discrepancy using shape-sensitive analysis. Our proof shows that the performance of bounding boxes depends on two natural shape parameters of objects, and the observed efficiency of boxes can be attributed to the fact that objects in practice tend to have small values of these parameters. The second part of this thesis focuses on the complexity of geometric permutations. Geometric permutations are a natural analog of the more familiar numerical permutation. Given a set of disjoint convex bodies in some fixed d-dimensional space, a geometric permutation is the order in which these objects can be intersected by a line. We will be interested in the “combinatorics” of such permutations—namely, given n objects in d-space, how many combinatorially distinct geometric permutations are possible. Our breakthrough result is that a set of n unit balls in Rd admits at most a constant number of geometric permutations. The constant bound significantly improves upon the Θ(n d−1) bound for the balls of arbitrary radii. Intrigued by this large gap between the two bounds, we then study how the number of geometric permutations varies as a function of shape, size, and spacing of objects.

Mechanisms for Hiding Sensitive Genotypes with Information-Theoretic Privacy

Abstract: Motivated by the growing availability of personal genomics services, we study an information-theoretic privacy problem that arises when sharing genomic data: a user wants to share his or her genome sequence while keeping the genotypes at certain positions hidden, which could otherwise reveal critical health-related information. A straightforward solution of erasing (masking) the chosen genotypes does not ensure privacy, because the correlation between nearby positions can leak the masked genotypes. We introduce an erasure-based privacy mechanism with perfect information-theoretic privacy, whereby the released sequence is statistically independent of the sensitive genotypes. Our mechanism can be interpreted as a locally-optimal greedy algorithm for a given processing order of sequence positions, where utility is measured by the number of positions released without erasure. We show that finding an optimal order is NP-hard in general and provide an upper bound on the optimal utility. For sequences from hidden Markov models, a standard modeling approach in genetics, we propose an efficient algorithmic implementation of our mechanism with complexity polynomial in sequence length. Moreover, we illustrate the robustness of the mechanism by bounding the privacy leakage from erroneous prior distributions. Our work is a step towards more rigorous control of privacy in genomic data sharing.

First-Hitting Times Under Additive Drift

Abstract: For the last ten years, almost every theoretical result concerning the expected run time of a randomized search heuristic used drift theory, making it the arguably most important tool in this domain. Its success is due to its ease of use and its powerful result: drift theory allows the user to derive bounds on the expected first-hitting time of a random process by bounding expected local changes of the process – the drift. This is usually far easier than bounding the expected first-hitting time directly.

Detecting Self-Collisions Using a Hybrid Bounding Volume Algorithm

Abstract: A discrete collision detection algorithm to detect self-collisions between deformable objects is presented, this is built up using a Bounding Volume Hierarchy (BVH) and a Feature-based method. The deformations are represented by the features of the mesh, which are withinthe bounding volumes and consequently the updating time for the BVH is reduced. The algorithm compares the minimum bounded geometry, the 1-ring, with the other spheres of the hierarchy in order to cull away Bounding Volumes (BV) that are far apart. The 3D objects utilised are surface-based and are deformed by warping, control points of splines, and a mass-spring model.

An Efficient Cascaded Model for Ship Segmentation in Aerial Images

Abstract: In this work, we propose a new method for real-time ship segmentation during maritime surveillance missions using aircrafts with onboard video cameras. We propose a cascade model with a detection stage followed by a segmentation stage. The detection stage selects candidate regions (bounding boxes) likely to contain ships. These bounding boxes are passed to the segmentation stage, where the ship segmentation mask is then obtained. By focusing the segmentation effort only in the image regions recommended by the previous detection stage, it is possible to improve the overall image processing time and, simultaneously, to obtain a better segmentation score than the one obtained by monolithic segmentation models that are trained in an end-to-end way. Additionally, we test the viability of using a Conditional Random Field model as final boundary refinement stage: although such model can improve the segmentation results when a full segmentation approach is used, our experiments did not show any significant improvements when using our proposed cascade model. We trained the detection and segmentation models with aerial ship images from publicly available maritime datasets. We tested the cascade model on the Airbus ship detection challenge, showing real-time performance and accurate maritime ship segmentation, comparable to state-of-the-art results.