Matthew Brown

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The problem considered in this paper is the fully automatic construction of panoramas. Fundamentally, this problem requires recognition, as we need to know which parts of the panorama join up. Previous approaches have used human input or restrictions on the image sequence for the matching step. In this work we use object recognition techniques based on(More)
This paper approaches the problem of finding correspondences between images in which there are large changes in viewpoint, scale and illumination. Recent work has shown that scale-space ‘interest points’ may be found with good repeatability in spite of such changes. Furthermore, the high entropy of the surrounding image regions means that local descriptors(More)
This paper presents a system for fully automatic recognition and reconstruction of 3D objects in image databases. We pose the object recognition problem as one of finding consistent matches between all images, subject to the constraint that the images were taken from a perspective camera. We assume that the objects or scenes are rigid. For each image, we(More)
Streaming programs represent an in reasingly important and widespread lass of appli ations that holds unpre edented opportunities for high-impa t ompiler te hnology. Unlike sequential programs with obs ured dependen e information and omplex ommuni ation patterns, a stream program is naturally written as a set of on urrent lters with regular steady-state(More)
In creating an evacuation simulation for training and planning, realistic agents that reproduce known phenomenon are required. Evacuation simulation in the airport domain requires additional features beyond most simulations, including the unique behaviors of firsttime visitors who have incomplete knowledge of the area and families that do not necessarily(More)
Boundedly rational human adversaries pose a serious challenge to security because they deviate from the classical assumption of perfect rationality. An emerging trend in security game research addresses this challenge by using behavioral models such as quantal response (QR) and subjective utility quantal response (SUQR). These models improve the quality of(More)
Stackelberg games have been used in several deployed applications to allocate limited resources for critical infrastructure protection. These resource allocation strategies are randomized to prevent a strategic attacker from using surveillance to learn and exploit patterns in the allocation. Past work has typically assumed that the attacker has perfect(More)
An innovative implementation of attitude estimation in 3 degrees of freedom (3-DOF) combining the TRIAD algorithm [1] and a time-varying nonlinear complementary filter (TVCF) is derived. This work is inspired by the good performance of the TVCF in 1-DOF [2] developed for applications limited to small mobile platforms with low computational power. To(More)
We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences. In common with recent work [10, 14, 16], we use an end-to-end learning approach with view synthesis as the supervisory signal. In contrast to the previous work, our method is completely unsupervised, requiring only(More)
To dissuade reckless driving and mitigate accidents, cities deploy resources to patrol roads. In this paper, we present STREETS, an application developed for the city of Singapore, which models the problem of computing randomized traffic patrol strategies as a defenderattacker Stackelberg game. Previous work on Stackelberg security games has focused(More)