We address the problem of determining where a photo was taken by estimating a full 6-DOF-plus-intrincs camera pose with respect to a large geo-registered 3D point cloud, bringing together research on image localization, landmark recognition, and 3D pose estimation. Our method scales to datasets with hundreds of thousands of images and tens of millions of 3D points through the use of two new tec...

We develop a method of estimating change{points of a function in the case of indirect noisy observations. As two paradigmatic problems we consider de-convolution and errors-in-variables regression. We estimate the scalar products of our indirectly observed function with appropriate test functions, which are shifted over the interval of interest. An estimator of the change point is obtained by t...

Determinantal point processes (DPPs) have wide-ranging applications in machine learning, where they are used to enforce the notion of diversity in subset selection problems. Many estimators have been proposed, but surprisingly the basic properties of the maximum likelihood estimator (MLE) have received little attention. In this paper, we study the local geometry of the expected log-likelihood f...

In robust regression theory the estimators, which can “resist” contamination of nearly fifty percent of the data, due to the fact that they are highly important in practice, were intensively studied. In this paper we describe three methods with high breakdown point: the least trimmed squares (LTS), the least median of squares (LMS) and their generalization the least weighted squares (LWS) estim...

Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where ...

Autonomous navigation of robot for on-road driving has gained growing importance in automobile research area. Image based path tracking is being considered for future driving assistance. Image from a monocular camera directing in front side is used for tracking the drivable road area and road direction. Vanishing Point estimation has been considered very important in case of driver assistance s...

It is a conventional belief that line-based approaches perform better than point-based ones for homography estimation, as the linefitting is generally more noise resistant than point detection. In this note, we show that blithely using line-based estimation is a risky business. More specifically, we show that when the image line(s) is (are) passing through or close to the origin, the line-based...

This paper is concerned with combined inference for point processes on the real line observed in a broken interval. For such processes, the classic history-based approach cannot be used. Instead, we adapt tools from sequential spatial point processes. For a range of models, the marginal and conditional distributions are derived. We discuss likelihood based inference as well as parameter estimat...

Consider observing a collection of discrete events within a network that reflect how network nodes influence one another. Such data are common in spike trains recorded from biological neural networks, interactions within a social network, and a variety of other settings. Data of this form may be modeled as self-exciting point processes, in which the likelihood of future events depends on the pa...

This technical report presents a normal estimation method for point clouds with low sampling density and sharp features. To achieve the best trade-off between quality and performance, normal of a point on smooth regions is estimated using a isotropic neighborhood with constant size; but normal of a point near features are evaluated with an anisotropic neighborhood from which the local tangent p...