Detecting Changes by Learning No Changes: Data-Enclosing-Ball Minimizing Autoencoders for One-Class Change Detection in Multispectral Imagery

Context changes detection by one-class SVMs

We are interested in a low level part of user modeling, which is the change detection in the context of the user. We present a method to detect on line changes in the context of a user equipped with non invasive sensors. Our point is to provide, in real time, series of unlabeled contexts that can be classi ed and analyzed on a higher level.

Urban Building Damage Detection from Very High Resolution Imagery by One-class Svm and Shadow Changes

CGL Computational Geometric Learning Smallest enclosing ball for probabilistic data

This technical report deals with the smallest enclosing ball problem subject to probabilistic data. In our setting, any of n points may not or may occur at one of finitely many locations, following its own discrete probability distribution. The objective is therefore considered to be a random variable and we aim at finding a center minimizing the expected maximum distance to the points accordin...

Unsupervised Classification of Changes in Multispectral Satellite Imagery

The statistical techniques of multivariate alteration detection, maximum autocorrelation factor transformation, expectation maximization, fuzzy maximum likelihood estimation and probabilistic label relaxation are combined in a unified scheme to classify changes in multispectral satellite data. An example involving bitemporal LANDSAT TM imagery is given.

Detecting Anomalous Faces with 'No Peeking' Autoencoders

Detecting anomalous faces has important applications. For example, a system might tell when a train driver is incapacitated by a medical event, and assist in adopting a safe recovery strategy. These applications are demanding, because they require accurate detection of rare anomalies that may be seen only at runtime. Such a setting causes supervised methods to perform poorly. We describe a meth...