@article {14506,
	title = {Statistical Relational Learning as an Enabling Technology for Data Acquisition and Data Fusion in Heterogeneous Sensor Networks},
	year = {2008},
	month = {2008/06/29/},
	institution = {OFFICE OF RESEARCH ADMINISTRATION AND ADVANCEMENT, UNIVERSITY OF MARYLAND COLLEGE PARK},
	abstract = {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.},
	keywords = {*ALGORITHMS, *CLASSIFICATION, data acquisition, DATA FUSION, Detectors, Feature extraction, HMM(HIDDEN MARKOV MODELS), NETWORKS, NUMERICAL MATHEMATICS, PE611102, RANDOM FIELDS, STATISTICS AND PROBABILITY, TEST SETS, VIDEO SIGNALS},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA500520},
	author = {Jacobs, David W. and Getoor, Lise}
}
