@conference {14831,
	title = {Wide-baseline stereo for face recognition with large pose variation},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on},
	year = {2011},
	month = {2011/06//},
	pages = {537 - 544},
	abstract = {2-D face recognition in the presence of large pose variations presents a significant challenge. When comparing a frontal image of a face to a near profile image, one must cope with large occlusions, non-linear correspondences, and significant changes in appearance due to viewpoint. Stereo matching has been used to handle these problems, but performance of this approach degrades with large pose changes. We show that some of this difficulty is due to the effect that foreshortening of slanted surfaces has on window-based matching methods, which are needed to provide robustness to lighting change. We address this problem by designing a new, dynamic programming stereo algorithm that accounts for surface slant. We show that on the CMU PIE dataset this method results in significant improvements in recognition performance.},
	keywords = {2D, algorithm;frontal, dataset;dynamic, estimation;stereo, Face, image, image;near, image;pose, MATCHING, matching;pose, matching;surface, method;dynamic, performance;stereo, PIE, processing;, profile, Programming, programming;face, recognition;CMU, recognition;image, slant;wide-baseline, stereo, stereo;window-based, variation;recognition},
	doi = {10.1109/CVPR.2011.5995559},
	author = {Castillo,C. D and Jacobs, David W.}
}
@conference {12471,
	title = {Automatic target recognition based on simultaneous sparse representation},
	booktitle = {Image Processing (ICIP), 2010 17th IEEE International Conference on},
	year = {2010},
	month = {2010/09//},
	pages = {1377 - 1380},
	abstract = {In this paper, an automatic target recognition algorithm is presented based on a framework for learning dictionaries for simultaneous sparse signal representation and feature extraction. The dictionary learning algorithm is based on class supervised simultaneous orthogonal matching pursuit while a matching pursuit-based similarity measure is used for classification. We show how the proposed framework can be helpful for efficient utilization of data, with the possibility of developing real-time, robust target classification. We verify the efficacy of the proposed algorithm using confusion matrices on the well known Comanche forward-looking infrared data set consisting of ten different military targets at different orientations.},
	keywords = {(artificial, algorithm;feature, based, classification;iterative, classification;learning, Comanche, data, dictionary;matching, extraction;image, forward-looking, infrared, intelligence);military, learning, MATCHING, matrix;dictionary, measure;military, methods;learning, orthogonal, pursuit, pursuit;confusion, recognition;class, recognition;target, representation;feature, representation;sparse, set;automatic, signal, similarity, simultaneous, sparse, supervised, systems;object, target, target;simultaneous, tracking;},
	doi = {10.1109/ICIP.2010.5652306},
	author = {Patel, Vishal M. and Nasrabadi,N.M. and Chellapa, Rama}
}
@article {12477,
	title = {An Efficient and Robust Algorithm for Shape Indexing and Retrieval},
	journal = {Multimedia, IEEE Transactions on},
	volume = {12},
	year = {2010},
	month = {2010/08//},
	pages = {372 - 385},
	abstract = {Many shape matching methods are either fast but too simplistic to give the desired performance or promising as far as performance is concerned but computationally demanding. In this paper, we present a very simple and efficient approach that not only performs almost as good as many state-of-the-art techniques but also scales up to large databases. In the proposed approach, each shape is indexed based on a variety of simple and easily computable features which are invariant to articulations, rigid transformations, etc. The features characterize pairwise geometric relationships between interest points on the shape. The fact that each shape is represented using a number of distributed features instead of a single global feature that captures the shape in its entirety provides robustness to the approach. Shapes in the database are ordered according to their similarity with the query shape and similar shapes are retrieved using an efficient scheme which does not involve costly operations like shape-wise alignment or establishing correspondences. Depending on the application, the approach can be used directly for matching or as a first step for obtaining a short list of candidate shapes for more rigorous matching. We show that the features proposed to perform shape indexing can be used to perform the rigorous matching as well, to further improve the retrieval performance.},
	keywords = {activity, algorithm;shape, classification;human, databases;robust, estimation;large, indexing;shape, MATCHING, matching;image, methods;shape, pose, recognition;, retrieval;image, retrieval;indexing;shape},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2010.2050735},
	author = {Biswas,S. and Aggarwal,G. and Chellapa, Rama}
}
@conference {12483,
	title = {Fast directional chamfer matching},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on},
	year = {2010},
	month = {2010/06//},
	pages = {1696 - 1703},
	abstract = {We study the object localization problem in images given a single hand-drawn example or a gallery of shapes as the object model. Although many shape matching algorithms have been proposed for the problem over the decades, chamfer matching remains to be the preferred method when speed and robustness are considered. In this paper, we significantly improve the accuracy of chamfer matching while reducing the computational time from linear to sublinear (shown empirically). Specifically, we incorporate edge orientation information in the matching algorithm such that the resulting cost function is piecewise smooth and the cost variation is tightly bounded. Moreover, we present a sublinear time algorithm for exact computation of the directional chamfer matching score using techniques from 3D distance transforms and directional integral images. In addition, the smooth cost function allows to bound the cost distribution of large neighborhoods and skip the bad hypotheses within. Experiments show that the proposed approach improves the speed of the original chamfer matching upto an order of 45 $\#$x00D7;, and it is much faster than many state of art techniques while the accuracy is comparable.},
	keywords = {3D, algorithm;edge, algorithms;single, chamfer, cost, detection;image, directional, distance, distribution;cost, example;smooth, function;sublinear, hand-drawn, images;edge, information;fast, integral, localization, MATCHING, matching;gallery, matching;transforms;, model;piecewise, of, orientation, problem;object, score;directional, shapes;object, smooth;shape, TIME, time;cost, transforms;computational, variation;directional},
	doi = {10.1109/CVPR.2010.5539837},
	author = {Ming-Yu Liu and Tuzel, O. and Veeraraghavan,A. and Chellapa, Rama}
}
@conference {13108,
	title = {Assigning cameras to subjects in video surveillance systems},
	booktitle = {Robotics and Automation, 2009. ICRA {\textquoteright}09. IEEE International Conference on},
	year = {2009},
	month = {2009/05//},
	pages = {837 - 843},
	abstract = {We consider the problem of tracking multiple agents moving amongst obstacles, using multiple cameras. Given an environment with obstacles, and many people moving through it, we construct a separate narrow field of view video for as many people as possible, by stitching together video segments from multiple cameras over time. We employ a novel approach to assign cameras to people as a function of time, with camera switches when needed. The problem is modeled as a bipartite graph and the solution corresponds to a maximum matching. As people move, the solution is efficiently updated by computing an augmenting path rather than by solving for a new matching. This reduces computation time by an order of magnitude. In addition, solving for the shortest augmenting path minimizes the number of camera switches at each update. When not all people can be covered by the available cameras, we cluster as many people as possible into small groups, then assign cameras to groups using a minimum cost matching algorithm. We test our method using numerous runs from different simulators.},
	keywords = {agent, algorithm;multiple, assignment;computation, augmenting, cameras;video, cost, detection;video, graph;camera, MATCHING, matching;minimum, matching;target, path;bipartite, reduction;maximum, segment;video, Surveillance, surveillance;, system;graph, theory;image, TIME, tracking;obstacle, tracking;video, video},
	doi = {10.1109/ROBOT.2009.5152753},
	author = {El-Alfy,H. and Jacobs, David W. and Davis, Larry S.}
}
@conference {12512,
	title = {Recognition of quantized still face images},
	booktitle = {Biometrics: Theory, Applications, and Systems, 2009. BTAS {\textquoteright}09. IEEE 3rd International Conference on},
	year = {2009},
	month = {2009/09//},
	pages = {1 - 6},
	abstract = {In applications such as document understanding, only binary face images may be available as inputs to a face recognition (FR) algorithm. In this paper, we investigate the effects of the number of grey levels on PCA, multiple exemplar discriminant analysis (MEDA) and the elastic bunch graph matching (EBGM) FR algorithms. The inputs to these FR algorithms are quantized images (binary images or images with small number of grey levels) modified by distance and Box-Cox transforms. The performances of PCA and MEDA algorithms are at 87.66\% for images in FRGC version 1 experiment 1 after they are thresholded and transformed while the EBGM algorithm achieves only 37.5\%. In many document understanding applications, it is also required to verify a degraded low-quality image against a high-quality image, both of which are from the same source. For this problem, the performances of PCA and MEDA are stable when the images were degraded by noise, downsampling or different thresholding parameters.},
	keywords = {(signal);, algorithm;grey, algorithms;face, analysis;quantisation, analysis;quantized, Box-Cox, bunch, component, discriminant, exemplar, Face, FR, graph, images;distance, images;face, levels;multiple, MATCHING, recognition, recognition;principal, transforms;document, transforms;PCA;binary, understanding;elastic},
	doi = {10.1109/BTAS.2009.5339030},
	author = {Tao Wu and Chellapa, Rama}
}
@conference {12556,
	title = {Multi-biometric cohort analysis for biometric fusion},
	booktitle = {Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on},
	year = {2008},
	month = {2008/04/31/4},
	pages = {5224 - 5227},
	abstract = {Biometric matching decisions have traditionally been made based solely on a score that represents the similarity of the query biometric to the enrolled biometric(s) of the claimed identity. Fusion schemes have been proposed to benefit from the availability of multiple biometric samples (e.g., multiple samples of the same fingerprint) or multiple different biometrics (e.g., face and fingerprint). These commonly adopted fusion approaches rarely make use of the large number of non-matching biometric samples available in the database in the form of other enrolled identities or training data. In this paper, we study the impact of combining this information with the existing fusion methodologies in a cohort analysis framework. Experimental results are provided to show the usefulness of such a cohort-based fusion of face and fingerprint biometrics.},
	keywords = {(access, analysis;biometrics, biometric, biometrics;fingerprint, biometrics;multi-biometric, cohort, control);face, data;, decisions;face, fusion;biometric, identification;security, MATCHING, of, recognition;fingerprint},
	doi = {10.1109/ICASSP.2008.4518837},
	author = {Aggarwal,G. and Ratha,N. K and Bolle, R.M. and Chellapa, Rama}
}
@conference {12589,
	title = {Symmetric Objects are Hardly Ambiguous},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 7},
	abstract = {Given any two images taken under different illumination conditions, there always exist a physically realizable object which is consistent with both the images even if the lighting in each scene is constrained to be a known point light source at infinity. In this paper, we show that images are much less ambiguous for the class of bilaterally symmetric Lambertian objects. In fact, the set of such objects can be partitioned into equivalence classes such that it is always possible to distinguish between two objects belonging to different equivalence classes using just one image per object. The conditions required for two objects to belong to the same equivalence class are very restrictive, thereby leading to the conclusion that images of symmetric objects are hardly ambiguous. The observation leads to an illumination-invariant matching algorithm to compare images of bilaterally symmetric Lambertian objects. Experiments on real data are performed to show the implications of the theoretical result even when the symmetry and Lambertian assumptions are not strictly satisfied.},
	keywords = {algorithm;symmetric, bilaterally, images;image, Lambertian, MATCHING, matching;, objects, objects;illumination-invariant, objects;symmetric, symmetric},
	doi = {10.1109/CVPR.2007.383289},
	author = {Aggarwal,G. and Biswas,S. and Chellapa, Rama}
}
@conference {13209,
	title = {Multi-level fast multipole method for thin plate spline evaluation},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/10//},
	pages = {1683 - 1686 Vol. 3 - 1683 - 1686 Vol. 3},
	abstract = {Image registration is an important problem in image processing and computer vision. Much recent work in image registration is on matching non-rigid deformations. Thin plate splines are an effective image registration method when the deformation between two images can be modeled as the bending of a thin metal plate on point constraints such that the topology is preserved (non-rigid deformation). However, because evaluating the computed TPS model at all the image pixels is computationally expensive, we need to speed it up. We introduce the use of multi-level fast muitipole method (MLFMM) for this purpose. Our contribution lies in the presentation of a clear and concise MLFMM framework for TPS, which will be useful for future application developments. The achieved speedup using MLFMM is an improvement from O(N<sup>2</sup>) to O(N log N). We show that the fast evaluation outperforms the brute force method while maintaining acceptable error bound.},
	keywords = {(mathematics);, Computer, deformation;, evaluation;, fast, image, MATCHING, matching;, metal, method;, multilevel, multipole, nonrigid, pixel;, plate, plate;, processing;, registration;, resolution;, spline, splines, thin, vision;},
	doi = {10.1109/ICIP.2004.1421395},
	author = {Zandifar,A. and Lim,S. and Duraiswami, Ramani and Gumerov, Nail A. and Davis, Larry S.}
}
@conference {13217,
	title = {Window-based, discontinuity preserving stereo},
	booktitle = {Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on},
	volume = {1},
	year = {2004},
	month = {2004/07/02/june},
	pages = {I-66 - I-73 Vol.1 - I-66 - I-73 Vol.1},
	abstract = {Traditionally, the problem of stereo matching has been addressed either by a local window-based approach or a dense pixel-based approach using global optimization. In this paper we present an algorithm which combines window-based local matching into a global optimization framework. Our local matching algorithm assumes that local windows can have at most two disparities. Under this assumption, the local matching can be performed very efficiently using graph cuts. The global matching is formulated as minimization of an energy term that takes into account the matching constraints induced by the local stereo algorithm. Fast, approximate minimization of this energy is achieved through graph cuts. The key feature of our algorithm is that it preserves discontinuities both during the local as well as global matching phase.},
	keywords = {algorithm;, approach;, based, cuts;, dense, discontinuity, global, graph, image, local, MATCHING, matching;, minimisation;, optimization;, Pixel, preserving, processing;, stereo, theory;, window},
	doi = {10.1109/CVPR.2004.1315015},
	author = {Agrawal,M. and Davis, Larry S.}
}
@conference {13240,
	title = {Exemplar-based tracking and recognition of arm gestures},
	booktitle = {Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the 3rd International Symposium on},
	volume = {2},
	year = {2003},
	month = {2003/09//},
	pages = {656 - 661 Vol.2 - 656 - 661 Vol.2},
	abstract = {This paper presents a probabilistic exemplar-based framework for recognizing gestures. The approach is based on representing each gesture as a sequence of learned body poses. The gestures are recognized through a probabilistic framework for matching these body poses and for imposing temporal constrains between different poses. Matching individual poses to image data is performed using a probabilistic formulation for edge matching to obtain a likelihood measurement for each individual pose. The paper introduces a correspondence-free weighted matching scheme for edge templates that emphasize discriminating features in the matching. The weighting does not require establishing correspondences between the different pose models. The probabilistic framework also imposes temporal constrains between different pose through a learned hidden Markov model (HMM) of each gesture.},
	keywords = {arm, constrains;, correspondence-free, edge, exemplar-based, framework;, gesture, hidden, HMM;, image, logic;, Markov, MATCHING, matching;, model;, models;, probabilistic, recognition;, scheme;, segmentation;, temporal, tracking;, weighted},
	doi = {10.1109/ISPA.2003.1296358},
	author = {Elgammal,A. and Shet,V. and Yacoob,Yaser and Davis, Larry S.}
}
@conference {13245,
	title = {Scalable image-based multi-camera visual surveillance system},
	booktitle = {Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.},
	year = {2003},
	month = {2003/07//},
	pages = {205 - 212},
	abstract = {We describe the design of a scalable and wide coverage visual surveillance system. Scalability (the ability to add and remove cameras easily during system operation with minimal overhead and system degradation) is achieved by utilizing only image-based information for camera control. We show that when a pan-tilt-zoom camera pans and tilts, a given image point moves in a circular and a linear trajectory, respectively. We create a scene model using a plan view of the scene. The scene model makes it easy for us to handle occlusion prediction and schedule video acquisition tasks subject to visibility constraints. We describe a maximum weight matching algorithm to assign cameras to tasks that meet the visibility constraints. The system is illustrated both through simulations and real video from a 6-camera configuration.},
	keywords = {ACQUISITION, algorithm;, camera;, constraints;, feature, hidden, image-based, MATCHING, maximum, multi-camera, occlusion, pan-tilt-zoom, PLAN, prediction;, processing;, removal;, scalable, scheduling;, signal, Surveillance, surveillance;, system;, task, video, view;, visibility, visual, weight},
	doi = {10.1109/AVSS.2003.1217923},
	author = {Lim,Ser-Nam and Davis, Larry S. and Elgammal,A.}
}
@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 {18156,
	title = {A recognition algorithm for Chinese characters in diverse fonts},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {3},
	year = {2002},
	month = {2002/06//},
	pages = {981 - 984 vol.3 - 981 - 984 vol.3},
	abstract = {The paper proposes an algorithm for recognizing Chinese characters in many diverse fonts including Song, Fang, Kai, Hei, Yuan, Lishu, Weibei and Xingkai. The algorithm is based on features derived from peripheral direction contributions and utilizes a set of dictionaries. A 3-level matching is first performed with respect to each dictionary. The distance measures associated with these matches are then fed into a central discriminator to output the final recognition result. We propose a new multi-dictionary matching algorithm for use in the central discriminator that utilizes estimated information of neighborhood fonts. Experiments have been performed on a practical OCR software system whose recognition kernel is based on the proposed algorithm. Fast and accurate recognition has been accomplished both in title recognition, involving all of the 8 fonts, and in main-body recognition, that usually involves only the first 4 most commonly used fonts.},
	keywords = {algorithm;, central, character, Chinese, contributions;, direction, discriminator;, diverse, fonts;, image, interfaces;, LANGUAGE, MATCHING, matching;, multi-dictionary, natural, OCR, optical, peripheral, recognition, recognition;, sets;, software, system;},
	doi = {10.1109/ICIP.2002.1039139},
	author = {Wu,Xianli and M. Wu}
}
@article {14891,
	title = {Classification with nonmetric distances: image retrieval and class representation},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {22},
	year = {2000},
	month = {2000/06//},
	pages = {583 - 600},
	abstract = {A key problem in appearance-based vision is understanding how to use a set of labeled images to classify new images. Systems that model human performance, or that use robust image matching methods, often use nonmetric similarity judgments; but when the triangle inequality is not obeyed, most pattern recognition techniques are not applicable. Exemplar-based (nearest-neighbor) methods can be applied to a wide class of nonmetric similarity functions. The key issue, however, is to find methods for choosing good representatives of a class that accurately characterize it. We show that existing condensing techniques are ill-suited to deal with nonmetric dataspaces. We develop techniques for solving this problem, emphasizing two points: First, we show that the distance between images is not a good measure of how well one image can represent another in nonmetric spaces. Instead, we use the vector correlation between the distances from each image to other previously seen images. Second, we show that in nonmetric spaces, boundary points are less significant for capturing the structure of a class than in Euclidean spaces. We suggest that atypical points may be more important in describing classes. We demonstrate the importance of these ideas to learning that generalizes from experience by improving performance. We also suggest ways of applying parametric techniques to supervised learning problems that involve a specific nonmetric distance functions, showing how to generalize the idea of linear discriminant functions in a way that may be more useful in nonmetric spaces},
	keywords = {appearance-based, by, classification;image, correlation;correlation, dataspaces;nonmetric, distances;nonmetric, example;, functions;nonmetric, image, inequality;vector, judgments;robust, MATCHING, methods;image, methods;nonmetric, methods;triangle, points;boundary, points;class, representation;exemplar-based, representation;image, retrieval;learning, retrieval;nearest-neighbor, similarity, vision;atypical},
	isbn = {0162-8828},
	doi = {10.1109/34.862197},
	author = {Jacobs, David W. and Weinshall,D. and Gdalyahu,Y.}
}
@conference {14856,
	title = {Condensing image databases when retrieval is based on non-metric distances},
	booktitle = {Computer Vision, 1998. Sixth International Conference on},
	year = {1998},
	month = {1998/01//},
	pages = {596 - 601},
	abstract = {One of the key problems in appearance-based vision is understanding how to use a set of labeled images to classify new images. Classification systems that can model human performance, or that use robust image matching methods, often make use of similarity judgments that are non-metric but when the triangle inequality is not obeyed, most existing pattern recognition techniques are not applicable. We note that exemplar-based (or nearest-neighbor) methods can be applied naturally when using a wide class of non-metric similarity functions. The key issue, however, is to find methods for choosing good representatives of a class that accurately characterize it. We note that existing condensing techniques for finding class representatives are ill-suited to deal with non-metric dataspaces. We then focus on developing techniques for solving this problem, emphasizing two points: First, we show that the distance between two images is not a good measure of how well one image can represent another in non-metric spaces. Instead, we use the vector correlation between the distances from each image to other previously seen images. Second, we show that in non-metric spaces, boundary points are less significant for capturing the structure of a class than they are in Euclidean spaces. We suggest that atypical points may be more important in describing classes. We demonstrate the importance of these ideas to learning that generalizes from experience by improving performance using both synthetic and real images},
	keywords = {appearance-based, databases;, databases;image, dataspaces;pattern, MATCHING, methods;non-metric, processing;visual, recognition;query, systems;image, vision;classification},
	doi = {10.1109/ICCV.1998.710778},
	author = {Jacobs, David W. and Weinshall,D. and Gdalyahu,Y.}
}
@article {14914,
	title = {Uncertainty Propagation in Model-Based Recognition.},
	year = {1994},
	month = {1994/12//},
	institution = {MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LAB},
	abstract = {Building robust recognition systems requires a careful understanding of the effects of error in sensed features. Error in these image features results in a region of uncertainty in the possible image location of each additional model feature. We present an accurate, analytic approximation for this uncertainty region when model poses are based on matching three image and model points, for both Gaussian and bounded error in the detection of image points, and for both scaled-orthographic and perspective projection models. This result applies to objects that are fully three-dimensional, where past results considered only two-dimensional objects. Further, we introduce a linear programming algorithm to compute the uncertainty region when poses are based on any number of initial matches. Finally, we use these results to extend, from two-dimensional to three-dimensional objects, robust implementations of alignment interpretation-tree search, and transformation clustering. (AN)},
	keywords = {*IMAGE PROCESSING, *PATTERN RECOGNITION, algorithms, APPROXIMATION(MATHEMATICS), CYBERNETICS, ERROR CORRECTION CODES, image registration, Linear programming, MATCHING, MATHEMATICAL MODELS, PIXELS, PROJECTIVE TECHNIQUES., regions, THREE DIMENSIONAL, TWO DIMENSIONAL, Uncertainty},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA295642},
	author = {Jacobs, David W. and Alter,T. D.}
}
@conference {15207,
	title = {Efficient minimum cost matching using quadrangle inequality},
	booktitle = {Foundations of Computer Science, 1992. Proceedings., 33rd Annual Symposium on},
	year = {1992},
	month = {1992/10//},
	pages = {583 - 592},
	abstract = {The authors present efficient algorithms for finding a minimum cost perfect matching, and for solving the transportation problem in bipartite graphs, G = (Red cup; Blue, Red times; Blue), where |Red| = n, |Blue| = m, n les; m, and the cost function obeys the quadrangle inequality. The first results assume that all the red points and all the blue points lie on a curve that is homeomorphic to either a line or a circle and the cost function is given by the Euclidean distance along the curve. They present a linear time algorithm for the matching problem. They generalize the method to solve the corresponding transportation problem in O((m+n)log(m+n)) time. The next result is an O(n log m) algorithm for minimum cost matching when the cost array is a bitonic Monge array. An example of this is when the red points lie on one straight line and the blue points lie on another straight line (that is not necessarily parallel to the first one). Finally, they provide a weakly polynomial algorithm for the transportation problem in which the associated cost array is a bitonic Monge array},
	keywords = {algorithm;, array;, bipartite, bitonic, blue, complexity;, computational, cost, distance;, Euclidean, function;, geometry;, graph, graphs;, inequality;, linear, MATCHING, matching;, minimisation;, minimum, Monge, perfect, points;, polynomial, problem;, quadrangle, red, theory;, TIME, transportation, transportation;, weakly},
	doi = {10.1109/SFCS.1992.267793},
	author = {Aggarwal,A. and Bar-Noy,A. and Khuller, Samir and Kravets,D. and Schieber,B.}
}
