@article {12606,
	title = {Vehicle Fingerprinting Using Drive-By Sounds},
	year = {2006},
	month = {2006/11//},
	institution = {CENTER FOR AUTOMATION RESEARCH, UNIVERSITY OF MARYLAND COLLEGE PARK},
	abstract = {We estimate a vehicle{\textquoteright}s speed, width, and length by jointly estimating its acoustic wave-pattern using a single passive acoustic sensor that records the vehicle{\textquoteright}s drive-by noise. The acoustic wave-pattern is estimated using three envelope shape (ES) components, which approximate the shape of the received signal{\textquoteright}s power envelope. We incorporate the parameters of the ES components along with estimates of the vehicle engine RPM and number of cylinders to form a vehicle profile vector. This vector provides a compressed statistics that can be used for vehicle identification and classification. Vehicle speed estimation and classification results are provided using field data.},
	keywords = {*ACOUSTIC DETECTORS, *GROUND VEHICLES, *SIGNAL PROCESSING, ACOUSTIC DETECTION AND DETECTORS, AMPLITUDE, Interference, PASSIVE SYSTEMS, spatial distribution, STATISTICAL PROCESSES, SURFACE TRANSPORTATION AND EQUIPMENT, SYMPOSIA, WAVE PROPAGATION},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA481471},
	author = {Cevher, V. and Chellapa, Rama and Gurbuz, A.C. and Shah, F. and McClellan, J.H.}
}
@article {12681,
	title = {Dimensionality Estimation in Hyperspectral Imagery Using Minimum Description Length},
	year = {2004},
	month = {2004/12//},
	institution = {MARYLAND UNIV COLLEGE PARK CENTER FOR AUTOMATION RESEARCH},
	abstract = {Numerous algorithms have been developed for hyperspectral automatic target recognition (ATR) applications. Many of these algorithms require estimation of a background subspace. The estimation of the background subspace has been addressed using multiple methods. but most of these methods assume a-priori knowledge of the background dimensionality. In order to automate the estimation of the background subspace. we present an algorithm based on minimum description length (MDL) that can identify the background dimension. Results show that the MDL criterion estimates the proper dimension of the background for ATR applications.},
	keywords = {*ALGORITHMS, *HYPERSPECTRAL IMAGERY, *TARGET DETECTION, *TARGET RECOGNITION, AUTOMATIC, BURIED OBJECTS, COMPONENT REPORTS, MINE DETECTION, NUMERICAL MATHEMATICS, SURFACE TARGETS., SYMPOSIA, TARGET DIRECTION, RANGE AND POSITION FINDING},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA431643},
	author = {Broadwater,Joshua B and Meth,Reuven and Chellapa, Rama}
}
@article {12680,
	title = {Video-Based Automatic Target Recognition},
	year = {2004},
	month = {2004/12//},
	institution = {MARYLAND UNIV COLLEGE PARK CENTER FOR AUTOMATION RESEARCH},
	abstract = {We present an approach for vehicle classification in IR video sequences by integrating detection, tracking and recognition. The method has two steps. First, the moving target is automatically detected using a detection algorithm. Next, we perform simultaneous tracking and recognition using an appearance-model based particle filter. The tracking result is evaluated at each frame. Low confidence in tracking performance initiates a new cycle of detection, tracking and classification. We demonstrate the robustness of the proposed method using outdoor IR video sequences.},
	keywords = {*SEQUENCES(MATHEMATICS), *TARGET RECOGNITION, *VEHICLES, *VIDEO RECORDING, algorithms, AUTOMATIC TRACKING., COMPONENT REPORTS, MOVING TARGETS, SURFACE TRANSPORTATION AND EQUIPMENT, SYMPOSIA, TARGET CLASSIFICATION, TARGET DIRECTION, RANGE AND POSITION FINDING, tracking, VIDEO SIGNALS},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA431617},
	author = {Zhou,S. K and Chellapa, Rama and Mei,Xue and Wu,Hao and Qinfen Zheng}
}
@article {16718,
	title = {The Bible, Truth, and Multilingual OCR Evaluation},
	year = {1998},
	month = {1998/12//},
	institution = {University of Maryland, College Park},
	abstract = {Multilingual OCR has emerged as an important information technology, thanks to the increasing need for cross-language information access. While many research groups and companies have developed OCR algorithms for various languages, it is difficult to compare the performance of these OCR algorithms across languages. This difficulty arises because most evaluation methodologies rely on the use of a document image dataset in each of the languages and it is difficult to find document datasets in different languages that are similar in content and layout. In this paper we propose to use the Bible as a dataset for comparing OCR accuracy across languages. Besides being available in a wide range of languages, Bible translation are closely parallel in content, carefully translated, surprisingly relevant with respect to modern-day language, and quite inexpensive. A project at the University of Maryland is currently implementing this idea. We have created a scanned image dataset with groundtruth from an Arabic Bible. We have also used image degradation models to create synthetically degraded images of a French Bible. We hope to generate similar Bible datasets for other languages, and we are exploring alternative corpora such as the Koran and the Bhagavad Gita that have similar properties. Quantitative OCR evaluation based on the Arabic Bible dataset is currently in progress.},
	keywords = {*BIBLE, *CORPUS, *DATASETS, *GROUNDTRUTH, *OPTICAL CHARACTER RECOGNITION, *TEST SETS, *TRANSLATIONS, accuracy, algorithms, CYBERNETICS, DOCUMENT IMAGES, DOCUMENTS, IMAGES, INFORMATION SCIENCE, LANGUAGE, linguistics, MULTILINGUAL OCR(OPTICAL CHARACTER RECOGNITION), SYMPOSIA, TEST AND EVALUATION},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA458666},
	author = {Kanungo,Tapas and Resnik, Philip}
}
