@conference {12250,
	title = {Robust Routing with Unknown Traffic Matrices},
	booktitle = {INFOCOM 2007. 26th IEEE International Conference on Computer Communications. IEEE},
	year = {2007},
	month = {2007/05//},
	pages = {2436 - 2440},
	abstract = {In this paper, we present an algorithm for intra-domain traffic engineering. We assume that the traffic matrix, which specifies traffic load between every source-destination pair in the network, is unknown and varies with time, but that always lies inside an explicitly defined region. Our goal is to compute a fixed robust routing with best worst case performance for all traffic matrices inside the bounding region. We formulate this problem as a semi-infinite programming problem. Then, we focus on a special case with practical merits, where (1) the traffic matrix region is assumed to be a polytope specified by a finite set of linear inequalities, and (2) our objective is to find the routing that minimizes the maximum link utilization. Under these assumptions, the problem can be formulated as a polynomial size linear programming (LP) problem with finite number of constraints. We further consider two specific set of constraints for the traffic matrix region. The first set is based on the hose model and limits the total traffic rate of network point of presence (PoP) nodes. The second set is based on the pipe model and limits the traffic between source-destination pairs. We study the effectiveness of each set of constraints using extensive simulations.},
	keywords = {engineering;linear, inequalities;maximum, intradomain, linear, link, matrices;linear, matrix, network, nodes;polynomial, of, point, presence, problem;traffic, Programming, programming;semiinfinite, programming;telecommunication, region;unknown, routing;telecommunication, size, traffic, traffic;, utilization;network},
	doi = {10.1109/INFCOM.2007.296},
	author = {Tabatabaee,V. and Kashyap,A. and Bhattacharjee, Bobby and La,R.J. and Shayman,M.A.}
}
@conference {12611,
	title = {Invariant Geometric Representation of 3D Point Clouds for Registration and Matching},
	booktitle = {Image Processing, 2006 IEEE International Conference on},
	year = {2006},
	month = {2006/10//},
	pages = {1209 - 1212},
	abstract = {Though implicit representations of surfaces have often been used for various computer graphics tasks like modeling and morphing of objects, it has rarely been used for registration and matching of 3D point clouds. Unlike in graphics, where the goal is precise reconstruction, we use isosurfaces to derive a smooth and approximate representation of the underlying point cloud which helps in generalization. Implicit surfaces are generated using a variational interpolation technique. Implicit function values on a set of concentric spheres around the 3D point cloud of object are used as features for matching. Geometric-invariance is achieved by decomposing implicit values based feature set into various spherical harmonics. The decomposition provides a compact representation of 3D point clouds while achieving rotation invariance},
	keywords = {3D, cloud;computer, function, geometric, graphics;geophysical, graphics;image, Interpolation, matching;image, point, processing;image, reconstruction;image, registration;image, registration;implicit, representation;interpolation;, representation;variational, signal, technique;clouds;computer, value;invariant},
	doi = {10.1109/ICIP.2006.312542},
	author = {Biswas,S. and Aggarwal,G. and Chellapa, Rama}
}
@conference {13563,
	title = {Handwriting matching and its application to handwriting synthesis},
	booktitle = {Document Analysis and Recognition, 2005. Proceedings. Eighth International Conference on},
	year = {2005},
	month = {2005/09/01/aug},
	pages = {861 - 865 Vol. 2 - 861 - 865 Vol. 2},
	abstract = {Since it is extremely expensive to collect a large volume of handwriting samples, synthesized data are often used to enlarge the training set. We argue that, in order to generate good handwriting samples, a synthesis algorithm should learn the shape deformation characteristics of handwriting from real samples. In this paper, we present a point matching algorithm to learn the deformation, and apply it to handwriting synthesis. Preliminary experiments show the advantages of our approach.},
	keywords = {(artificial, deformation, deformation;, handwriting, image, intelligence);, learning, learning;, matching;, point, recognition;, sampling;, SHAPE, synthesis;},
	doi = {10.1109/ICDAR.2005.122},
	author = {Yefeng Zheng and David Doermann}
}
@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 {16896,
	title = {Simultaneous pose and correspondence determination using line features},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {2},
	year = {2003},
	month = {2003/06//},
	pages = {II-424 - II-431 vol.2 - II-424 - II-431 vol.2},
	abstract = {We present a new robust line matching algorithm for solving the model-to-image registration problem. Given a model consisting of 3D lines and a cluttered perspective image of this model, the algorithm simultaneously estimates the pose of the model and the correspondences of model lines to image lines. The algorithm combines softassign for determining correspondences and POSIT for determining pose. Integrating these algorithms into a deterministic annealing procedure allows the correspondence and pose to evolve from initially uncertain values to a joint local optimum. This research extends to line features the SoftPOSIT algorithm proposed recently for point features. Lines detected in images are typically more stable than points and are less likely to be produced by clutter and noise, especially in man-made environments. Experiments on synthetic and real imagery with high levels of clutter, occlusion, and noise demonstrate the robustness of the algorithm.},
	keywords = {algorithm;, algorithms;, annealing;, clutter;, cluttered, Computer, correspondence, detection;, determination;, deterministic, environment;, extraction;, feature, feature;, image, image;, imagery;, images;, joint, line, local, man-made, MATCHING, matching;, measurement;, model-to-image, noise;, occlusion;, optimum;, perspective, point, pose, position, problem;, processing;, real, realistic, registration, simulated, softassign;, SoftPOSIT, stereo, synthetic, vision;},
	doi = {10.1109/CVPR.2003.1211499},
	author = {David,P. and DeMenthon,D. and Duraiswami, Ramani and Samet, Hanan}
}
@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}
}
@conference {14881,
	title = {Segmenting independently moving, noisy points},
	booktitle = {Motion of Non-Rigid and Articulated Objects, 1994., Proceedings of the 1994 IEEE Workshop on},
	year = {1994},
	month = {1994/11//},
	pages = {96 - 103},
	abstract = {There has been much work on using point features tracked through a video sequence to determine structure and motion. In many situations, to use this work, we must first isolate subsets of points that share a common motion. This is hard because we must distinguish between independent motions and apparent deviations from a single motion due to noise. We propose several methods of searching for point-sets with consistent 3D motions. We analyze the potential sensitivity of each method for detecting independent motions, and experiment with each method on a real image sequence},
	keywords = {3D, common, consistent, estimation;, features;, image, independently, linear, MOTION, motion;, moving, noisy, point, points;, programming;, real, segmentation;, sequence;, sequences;, video},
	doi = {10.1109/MNRAO.1994.346249},
	author = {Jacobs, David W. and Chennubhotla,C.}
}
@conference {14837,
	title = {Space efficient 3-D model indexing},
	booktitle = {Computer Vision and Pattern Recognition, 1992. Proceedings CVPR {\textquoteright}92., 1992 IEEE Computer Society Conference on},
	year = {1992},
	month = {1992/06//},
	pages = {439 - 444},
	abstract = {It is shown that the set of 2-D images produced by a group of 3-D point features of a rigid model can be optimally represented with two lines in two high-dimensional spaces. This result is used to match images and model groups by table lookup. The table is efficiently built and accessed through analytic methods that account for the effect of sensing error. In real images, it reduces the set of potential matches by a factor of several thousand. This representation of a model{\textquoteright}s images is used to analyze two other approaches to recognition. It is determined when invariants exist in several domains, and it is shown that there is an infinite set of qualitatively similar nonaccidental properties},
	keywords = {3-D, error;table, features;sensing, indexing;3-D, lookup;, lookup;image, model, point, processing;table},
	doi = {10.1109/CVPR.1992.223153},
	author = {Jacobs, David W.}
}
