@conference {12576,
	title = {Fast Bilinear SfM with Side Information},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {We study the beneficial effect of side information on the Structure from Motion (SfM) estimation problem. The side information that we consider is measurement of a {\textquoteright}reference vector{\textquoteright} and distance from fixed plane perpendicular to that reference vector. Firstly, we show that in the presence of this information, the SfM equations can be rewritten similar to a bilinear form in its unknowns. Secondly, we describe a fast iterative estimation procedure to recover the structure of both stationary scenes and moving objects that capitalizes on this information. We also provide a refinement procedure in order to tackle incomplete or noisy side information. We characterize the algorithm with respect to its reconstruction accuracy, memory requirements and stability. Finally, we describe two classes of commonly occurring real-world scenarios in which this algorithm will be effective: (a) presence of a dominant ground plane in the scene and (b) presence of an inertial measurement unit on board. Experiments using both real data and rigorous simulations show the efficacy of the algorithm.},
	keywords = {bilinear, Estimation, estimation;, estimation;reference, information;image, methods;motion, procedure;motion, procedure;side, reconstruction;iterative, recovery;image, structure, vector;refinement},
	doi = {10.1109/ICCV.2007.4408874},
	author = {Ramachandran, M. and Veeraraghavan,A. and Chellapa, Rama}
}
@conference {12703,
	title = {Appearance-based tracking and recognition using the 3D trilinear tensor},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/05//},
	pages = {iii - 613-16 vol.3 - iii - 613-16 vol.3},
	abstract = {The paper presents an appearance-based adaptive algorithm for simultaneous tracking and recognition by generalizing the transformation model to 3D perspective transformation. A trilinear tensor operator is used to represent the 3D geometrical structure. The tensor is estimated by predicting the corresponding points using the existing affine-transformation based algorithm. The estimated tensor is used to synthesize novel views to update the appearance templates. Some experimental results using airborne video are presented.},
	keywords = {3D, adaptive, affine-transformation, airborne, algorithm;, appearance, appearance-based, based, estimation;, geometrical, image, mathematical, novel, object, operator;, operators;, perspective, prediction;, processing;, recognition;, representation;, signal, structure, synthesis;, template, tensor, tensor;, tensors;, tracking;, transformation;, trilinear, updating;, video, video-based, video;, view},
	doi = {10.1109/ICASSP.2004.1326619},
	author = {Jie Shao and Zhou,S. K and Chellapa, Rama}
}
@article {12719,
	title = {Accurate dense optical flow estimation using adaptive structure tensors and a parametric model},
	journal = {Image Processing, IEEE Transactions on},
	volume = {12},
	year = {2003},
	month = {2003/10//},
	pages = {1170 - 1180},
	abstract = {An accurate optical flow estimation algorithm is proposed in this paper. By combining the three-dimensional (3D) structure tensor with a parametric flow model, the optical flow estimation problem is converted to a generalized eigenvalue problem. The optical flow can be accurately estimated from the generalized eigenvectors. The confidence measure derived from the generalized eigenvalues is used to adaptively adjust the coherent motion region to further improve the accuracy. Experiments using both synthetic sequences with ground truth and real sequences illustrate our method. Comparisons with classical and recently published methods are also given to demonstrate the accuracy of our algorithm.},
	keywords = {3D, accuracy;, adaptive, and, coherent, confidence, dense, eigenfunctions;, eigenvalue, eigenvalues, eigenvectors;, estimation;, flow, generalized, ground, image, measure;, model;, MOTION, optical, parameter, parametric, problem;, real, region;, sequences;, structure, synthetic, tensor;, tensors;, three-dimensional, truth;},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2003.815296},
	author = {Liu,Haiying and Chellapa, Rama and Rosenfeld, A.}
}
@conference {12765,
	title = {3D face reconstruction from video using a generic model},
	booktitle = {Multimedia and Expo, 2002. ICME {\textquoteright}02. Proceedings. 2002 IEEE International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {449 - 452 vol.1 - 449 - 452 vol.1},
	abstract = {Reconstructing a 3D model of a human face from a video sequence is an important problem in computer vision, with applications to recognition, surveillance, multimedia etc. However, the quality of 3D reconstructions using structure from motion (SfM) algorithms is often not satisfactory. One common method of overcoming this problem is to use a generic model of a face. Existing work using this approach initializes the reconstruction algorithm with this generic model. The problem with this approach is that the algorithm can converge to a solution very close to this initial value, resulting in a reconstruction which resembles the generic model rather than the particular face in the video which needs to be modeled. We propose a method of 3D reconstruction of a human face from video in which the 3D reconstruction algorithm and the generic model are handled separately. A 3D estimate is obtained purely from the video sequence using SfM algorithms without use of the generic model. The final 3D model is obtained after combining the SfM estimate and the generic model using an energy function that corrects for the errors in the estimate by comparing local regions in the two models. The optimization is done using a Markov chain Monte Carlo (MCMC) sampling strategy. The main advantage of our algorithm over others is that it is able to retain the specific features of the face in the video sequence even when these features are different from those of the generic model. The evolution of the 3D model through the various stages of the algorithm is presented.},
	keywords = {3D, algorithm;, algorithms;, analysis;, Carlo, chain, Computer, Face, from, function;, generic, human, image, Markov, MCMC, methods;, model;, Monte, MOTION, optimisation;, OPTIMIZATION, processes;, processing;, recognition;, reconstruction, reconstruction;, sampling;, sequence;, sequences;, SfM, signal, structure, surveillance;, video, vision;},
	doi = {10.1109/ICME.2002.1035815},
	author = {Chowdhury, A.R. and Chellapa, Rama and Krishnamurthy, S. and Vo, T.}
}
@conference {13272,
	title = {A Smale-like decomposition for discrete scalar fields},
	booktitle = {Pattern Recognition, 2002. Proceedings. 16th International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {184 - 187 vol.1 - 184 - 187 vol.1},
	abstract = {In this paper we address the problem of representing the structure of the topology of a d-dimensional scalar field as a basis for constructing a multiresolution representation of the structure of such afield. To this aim, we define a discrete decomposition of a triangulated d-dimensional domain, on whose vertices the values of the field are given. We extend a Smale decomposition, defined by Thom (1949) and Smale (1960) for differentiable functions, to the discrete case, to what we call a Smale-like decomposition. We introduce the notion of discrete gradient vector field, which indicates the growth of the scalar field and matches with our decomposition. We sketch an algorithm for building a Smale-like decomposition and a graph-based representation of this decomposition. We present results for the case of two-dimensional fields.},
	keywords = {data, decomposition;, differentiable, discrete, domain;, field;, fields;, functions;, gradient, graph-based, methods;, multidimensional, multiresolution, representation;, scalar, Smale-like, structure, Topology, triangulated, vector, visualisation;},
	doi = {10.1109/ICPR.2002.1044644},
	author = {De Floriani, Leila and Mesmoudi,M. M. and Danovaro,E.}
}
@conference {13638,
	title = {Robust table-form structure analysis based on box-driven reasoning},
	booktitle = {Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on},
	volume = {1},
	year = {1995},
	month = {1995/08//},
	pages = {218 -221 vol.1 - 218 -221 vol.1},
	abstract = {Table form document structure analysis is an important problem in the document processing domain. The paper presents a method called Box Driven Reasoning (BDR) to robustly analyze the structure of table form documents which include touching characters and broken lines. Most previous methods employ a line oriented approach. Real documents are copied repeatedly and overlaid with printed data, resulting in characters which touch cells and lines which are broken. BDR deals with regions directly, in contrast with other previous methods. Experimental tests show that BDR reliably recognizes cells and strings in document images with touching characters and broken lines},
	keywords = {analysis;table, analysis;touching, BDR;box, characters;character, document, domain;robust, driven, form, image, images;document, lines;document, mechanisms;, PROCESSING, processing;inference, reasoning;broken, recognition;data, structure, structures;document, table},
	doi = {10.1109/ICDAR.1995.598980},
	author = {Hori,O. and David Doermann}
}
