TY - RPRT
T1 - Statistical Relational Learning as an Enabling Technology for Data Acquisition and Data Fusion in Heterogeneous Sensor Networks
Y1 - 2008
A1 - Jacobs, David W.
A1 - Getoor, Lise
KW - *ALGORITHMS
KW - *CLASSIFICATION
KW - data acquisition
KW - DATA FUSION
KW - Detectors
KW - Feature extraction
KW - HMM(HIDDEN MARKOV MODELS)
KW - NETWORKS
KW - NUMERICAL MATHEMATICS
KW - PE611102
KW - RANDOM FIELDS
KW - STATISTICS AND PROBABILITY
KW - TEST SETS
KW - VIDEO SIGNALS
AB - 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.
PB - OFFICE OF RESEARCH ADMINISTRATION AND ADVANCEMENT, UNIVERSITY OF MARYLAND COLLEGE PARK
UR - http://stinet.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA500520
ER -