@conference {12729,
	title = {Activity recognition using the dynamics of the configuration of interacting objects},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {2},
	year = {2003},
	month = {2003/06//},
	pages = {II - 633-40 vol.2 - II - 633-40 vol.2},
	abstract = {Monitoring activities using video data is an important surveillance problem. A special scenario is to learn the pattern of normal activities and detect abnormal events from a very low resolution video where the moving objects are small enough to be modeled as point objects in a 2D plane. Instead of tracking each point separately, we propose to model an activity by the polygonal {\textquoteright}shape{\textquoteright} of the configuration of these point masses at any time t, and its deformation over time. We learn the mean shape and the dynamics of the shape change using hand-picked location data (no observation noise) and define an abnormality detection statistic for the simple case of a test sequence with negligible observation noise. For the more practical case where observation (point locations) noise is large and cannot be ignored, we use a particle filter to estimate the probability distribution of the shape given the noisy observations up to the current time. Abnormality detection in this case is formulated as a change detection problem. We propose a detection strategy that can detect both {\textquoteright}drastic{\textquoteright} and {\textquoteright}slow{\textquoteright} abnormalities. Our framework can be directly applied for object location data obtained using any type of sensors - visible, radar, infrared or acoustic.},
	keywords = {2D, abnormal, abnormality, abnormality;, acoustic, activity, analysis;, change;, Computer, configuration, configuration;, data;, DETECTION, detection;, distribution;, drastic, dynamics;, event;, filter;, hand-picked, image, infrared, interacting, learning;, location, low, mean, model;, monitoring;, MOTION, moving, noise;, noisy, object, object;, observation, observation;, particle, pattern, plane;, point, polygonal, probability, probability;, problem;, processing;, radar, recognition;, resolution, sensor;, sensors;, sequence;, SHAPE, shape;, signal, slow, statistic;, strategy;, Surveillance, surveillance;, target, test, tracking;, video, video;, visible, vision;},
	doi = {10.1109/CVPR.2003.1211526},
	author = {Vaswani, N. and RoyChowdhury, A. and Chellapa, Rama}
}
@conference {15592,
	title = {Robust matching of wavelet features for sub-pixel registration of Landsat data},
	booktitle = {Geoscience and Remote Sensing Symposium, 2001. IGARSS {\textquoteright}01. IEEE 2001 International},
	volume = {2},
	year = {2001},
	month = {2001///},
	pages = {706 -708 vol.2 - 706 -708 vol.2},
	abstract = {For many Earth and space science applications, automatic geo-registration at sub-pixel accuracy has become a necessity. In this work, we are focusing on building an operational system, which will provide a sub-pixel accuracy registration of Landsat-5 and Landsat-7 data. The input to our registration method consists of scenes that have been geometrically and radiometrically corrected. Such preprocessed scenes are then geo-registered relative to a database of Landsat chips. The method assumes a transformation composed of a rotation and a translation, and utilizes rotation- and translation-invariant wavelets to extract image features that are matched using statistically robust feature matching and a partial Hausdorff distance metric. The registration process is described and results on four Landsat input scenes of the Washington, D.C., area are presented},
	keywords = {Feature extraction, geo-registration, geophysical measurement technique, geophysical signal processing, geophysical techniques, Hausdorff distance metric, image registration, infrared, IR, land surface, Landsat, Landsat-5, Landsat-7, multispectral remote sensing, robust feature matching, robust matching, sub pixel registration, subpixel accuracy, terrain mapping, visible, wavelet feature, wavelet method, Wavelet transforms},
	doi = {10.1109/IGARSS.2001.976609},
	author = {Le Moigne,J. and Netanyahu,N. S and Masek,J. G and Mount, Dave and Goward, S.N.}
}
