%0 Report
%D 2008
%T Statistical Relational Learning as an Enabling Technology for Data Acquisition and Data Fusion in Heterogeneous Sensor Networks
%A Jacobs, David W.
%A Getoor, Lise
%K *ALGORITHMS
%K *CLASSIFICATION
%K data acquisition
%K DATA FUSION
%K Detectors
%K Feature extraction
%K HMM(HIDDEN MARKOV MODELS)
%K NETWORKS
%K NUMERICAL MATHEMATICS
%K PE611102
%K RANDOM FIELDS
%K STATISTICS AND PROBABILITY
%K TEST SETS
%K VIDEO SIGNALS
%X Our work has focused on developing new cost sensitive feature acquisition and classification algorithms, mapping these algorithms onto camera networks, and creating a test bed of video data and implemented vision algorithms that we can use to implement these. First, we will describe a new algorithm that we have developed for feature acquisition in Hidden Markov Models (HMMs). This is particularly useful for inference tasks involving video from a single camera, in which the relationship between frames of video can be modeled as a Markov chain. We describe this algorithm in the context of using background subtraction results to identify portions of video that contain a moving object. Next, we will describe new algorithms that apply to general graphical models. These can be tested using existing test sets that are drawn from a range of domains in addition to sensor networks.
%I OFFICE OF RESEARCH ADMINISTRATION AND ADVANCEMENT, UNIVERSITY OF MARYLAND COLLEGE PARK
%8 2008/06/29/
%G eng
%U http://stinet.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA500520