@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 {12733,
	title = {Statistical shape theory for activity modeling},
	booktitle = {Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP {\textquoteright}03). 2003 IEEE International Conference on},
	volume = {3},
	year = {2003},
	month = {2003/04//},
	pages = {III - 493-6 vol.3 - III - 493-6 vol.3},
	abstract = {Monitoring activities in a certain region from video data is an important surveillance problem. The goal is to learn the pattern of normal activities and detect unusual ones by identifying activities that deviate appreciably from the typical ones. We propose an approach using statistical shape theory based on the shape model of D.G. Kendall et al. (see "Shape and Shape Theory", John Wiley and Sons, 1999). In a low resolution video, each moving object is best represented as a moving point mass or particle. In this case, an activity can be defined by the interactions of all or some of these moving particles over time. We model this configuration of the particles by a polygonal shape formed from the locations of the points in a frame and the activity by the deformation of the polygons in time. These parameters are learned for each typical activity. Given a test video sequence, an activity is classified as abnormal if the probability for the sequence (represented by the mean shape and the dynamics of the deviations), given the model, is below a certain threshold The approach gives very encouraging results in surveillance applications using a single camera and is able to identify various kinds of abnormal behavior.},
	keywords = {abnormal, activities, activity, analysis;, behavior;, classification;, data;, image, mass;, matching;, modeling;, monitoring;, moving, normal, particle;, pattern, pattern;, point, polygonal, probability;, problem;, processing;, sequence;, sequences;, SHAPE, shape;, signal, statistical, Surveillance, surveillance;, theory;, video},
	doi = {10.1109/ICASSP.2003.1199519},
	author = {Vaswani, N. and Chowdhury, A.R. and Chellapa, Rama}
}
@article {15596,
	title = {Approximating large convolutions in digital images},
	journal = {Image Processing, IEEE Transactions on},
	volume = {10},
	year = {2001},
	month = {2001/12//},
	pages = {1826 - 1835},
	abstract = {Computing discrete two-dimensional (2-D) convolutions is an important problem in image processing. In mathematical morphology, an important variant is that of computing binary convolutions, where the kernel of the convolution is a 0-1 valued function. This operation can be quite costly, especially when large kernels are involved. We present an algorithm for computing convolutions of this form, where the kernel of the binary convolution is derived from a convex polygon. Because the kernel is a geometric object, we allow the algorithm some flexibility in how it elects to digitize the convex kernel at each placement, as long as the digitization satisfies certain reasonable requirements. We say that such a convolution is valid. Given this flexibility we show that it is possible to compute binary convolutions more efficiently than would normally be possible for large kernels. Our main result is an algorithm which, given an m times;n image and a k-sided convex polygonal kernel K, computes a valid convolution in O(kmn) time. Unlike standard algorithms for computing correlations and convolutions, the running time is independent of the area or perimeter of K, and our techniques do not rely on computing fast Fourier transforms. Our algorithm is based on a novel use of Bresenham{\textquoteright}s (1965) line-drawing algorithm and prefix-sums to update the convolution incrementally as the kernel is moved from one position to another across the image},
	keywords = {2D, algorithm;binary, approximation;mathematical, convolution, convolution;binary, convolutions;Bresenham{\textquoteright}s, convolutions;convex, convolutions;geometric, images;discrete, kernel;convex, kernel;digital, line-drawing, morphology;, morphology;approximation, object;image, polygonal, processing;large, processing;mathematical, theory;convolution;image, two-dimensional},
	isbn = {1057-7149},
	doi = {10.1109/83.974567},
	author = {Mount, Dave and Kanungo,T. and Netanyahu,N. S and Piatko,C. and Silverman,R. and Wu,A. Y}
}
@article {17969,
	title = {Adaptive real-time level-of-detail based rendering for polygonal models},
	journal = {Visualization and Computer Graphics, IEEE Transactions on},
	volume = {3},
	year = {1997},
	month = {1997/06//apr},
	pages = {171 - 183},
	abstract = {We present an algorithm for performing adaptive real-time level-of-detail-based rendering for triangulated polygonal models. The simplifications are dependent on viewing direction, lighting, and visibility and are performed by taking advantage of image-space, object-space, and frame-to-frame coherences. In contrast to the traditional approaches of precomputing a fixed number of level-of-detail representations for a given object, our approach involves statically generating a continuous level-of-detail representation for the object. This representation is then used at run time to guide the selection of appropriate triangles for display. The list of displayed triangles is updated incrementally from one frame to the next. Our approach is more effective than the current level-of-detail-based rendering approaches for most scientific visualization applications, where there are a limited number of highly complex objects that stay relatively close to the viewer. Our approach is applicable for scalar (such as distance from the viewer) as well as vector (such as normal direction) attributes},
	keywords = {(computer, adaptive, attributes;scientific, attributes;view-dependent, coherence;image-space;lighting;multiresolution, direction;visibility;computational, geometry;data, graphics);, hierarchies;object-space;run, hierarchies;triangulated, level-of-detail, models;vector, polygonal, real-time, refinement;triangle, rendering;displayed, simplifications;viewing, systems;rendering, time;scalar, triangles;frame-to-frame, visualisation;real-time, visualization;selective},
	isbn = {1077-2626},
	doi = {10.1109/2945.597799},
	author = {Xia,J. C and El-Sana,J. and Varshney, Amitabh}
}
@conference {17960,
	title = {Dynamic view-dependent simplification for polygonal models},
	booktitle = {Visualization {\textquoteright}96. Proceedings.},
	year = {1996},
	month = {1996///},
	pages = {327 - 334},
	abstract = {Presents an algorithm for performing view-dependent simplifications of a triangulated polygonal model in real-time. The simplifications are dependent on viewing direction, lighting and visibility, and are performed by taking advantage of image-space, object-space and frame-to-frame coherences. A continuous level-of-detail representation for an object is first constructed off-line. This representation is then used at run-time to guide the selection of appropriate triangles for display. The list of displayed triangles is updated incrementally from one frame to the next. Our approach is more effective than the current level-of-detail-based rendering approaches for most scientific visualization applications where there are a limited number of highly complex objects that stay relatively close to the viewer.},
	keywords = {coherence;image-space, coherence;incremental, coherence;real-time, continuous, direction;visibility;data, dynamic, level-of-detail, list, model;viewing, polygonal, representation;displayed, selection;triangulated, simplification;scientific, triangle, updating;frame-to-frame, updating;lighting;object-space, view-dependent, visualisation;, visualization;triangle},
	doi = {10.1109/VISUAL.1996.568126},
	author = {Xia,J. C and Varshney, Amitabh}
}
