Bounding overwatch

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

Combined branch-and-bound and cutting plane methods for solving a class of nonlinear programming problems

Abstract: We propose unified branch-and-bound and cutting plane algorithms for global minimization of a functionf(x, y) over a certain closed set By formulating the problem in terms of two groups of variables and two groups of constraints we obtain new relaxation bounding and adaptive branching operations The branching operation takes place in y-space only and uses the iteration points obtained through the bounding operation The cutting is performed in parallel with the branch-and-bound procedure The method can be applied implementably for a certain class of nonconvex programming problems

Approximate Decomposition: a method for bounding and estimating probabilistic and deterministic queries

Abstract: In this paper, we introduce a method for approximating the solution to inference and optimization tasks in uncertain and deterministic reasoning Such tasks are in general intractable for exact algorithms because of the large number of dependency relationships in their structure Our method effectively maps such a dense problem to a sparser one which is in some sense "closest" Exact methods can be run on the sparser problem to derive bounds on the original answer, which can be quite sharp On one large CPCS network, for example, we were able to calculate upper and lower bounds on the conditional probability of a variable, given evidence, that were almost identical in the average case

Daedalus: Breaking Nonmaximum Suppression in Object Detection via Adversarial Examples

Abstract: This article demonstrates that nonmaximum suppression (NMS), which is commonly used in object detection (OD) tasks to filter redundant detection results, is no longer secure. Considering that NMS has been an integral part of OD systems, thwarting the functionality of NMS can result in unexpected or even lethal consequences for such systems. In this article, an adversarial example attack that triggers malfunctioning of NMS in OD models is proposed. The attack, namely, Daedalus, compresses the dimensions of detection boxes to evade NMS. As a result, the final detection output contains extremely dense false positives. This can be fatal for many OD applications, such as autonomous vehicles and surveillance systems. The attack can be generalized to different OD models, such that the attack cripples various OD applications. Furthermore, a way of crafting robust adversarial examples is developed by using an ensemble of popular detection models as the substitutes. Considering the pervasive nature of model reuse in real-world OD scenarios, Daedalus examples crafted based on an ensemble of substitutes can launch attacks without knowing the parameters of the victim models. The experimental results demonstrate that the attack effectively stops NMS from filtering redundant bounding boxes. As the evaluation results suggest, Daedalus increases the false positive rate in detection results to 99.9% and reduces the mean average precision scores to 0, while maintaining a low cost of distortion on the original inputs. It also demonstrates that the attack can be practically launched against real-world OD systems via printed posters.

Verifying and Validating Multirobot Missions

Abstract: We have developed an approach that can be used by mission designers to determine whether or not a performance guarantee for their mission software, when carried out under the uncertain conditions of a real-world environment, will hold within a threshold probability. In this paper we demonstrate its utility for verifying multirobot missions, in particular a bounding overwatch mission.

Multidimensional segmentation based on adaptive bounding box and ellipsoid models

Abstract: A seed point is selected inside a structure that is to be segmented in image data. An adaptive model is defined around the seed point, and a preprocessing filter is applied only within the bounding region. A presegmentation of the preprocessed result is performed, and the bounding region is expanded if necessary to accommodate the presegmentation result. An adaptive model for post-processing may be used. The model is translated, rotated and scaled to find a best fit with the pre-segmented data.