@article {14182,
	title = {A 3D shape constraint on video},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {28},
	year = {2006},
	month = {2006/06//},
	pages = {1018 - 1023},
	abstract = {We propose to combine the information from multiple motion fields by enforcing a constraint on the surface normals (3D shape) of the scene in view. The fact that the shape vectors in the different views are related only by rotation can be formulated as a rank = 3 constraint. This constraint is implemented in an algorithm which solves 3D motion and structure estimation as a practical constrained minimization. Experiments demonstrate its usefulness as a tool in structure from motion providing very accurate estimates of 3D motion.},
	keywords = {3D motion estimation, algorithms, Artificial intelligence, CAMERAS, decoupling translation from rotation, Estimation error, Fluid flow measurement, Image Enhancement, Image Interpretation, Computer-Assisted, Image reconstruction, Imaging, Three-Dimensional, Information Storage and Retrieval, integration of motion fields, Layout, minimisation, Minimization methods, Motion estimation, multiple motion fields, parameter estimation, Pattern Recognition, Automated, Photography, practical constrained minimization, SHAPE, shape and rotation., shape vectors, stability, structure estimation, surface normals, Three-dimensional motion estimation, video 3D shape constraint, Video Recording, video signal processing},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2006.109},
	author = {Hui Ji and Ferm{\"u}ller, Cornelia}
}
@conference {14224,
	title = {Integration of motion fields through shape},
	booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005},
	volume = {2},
	year = {2005},
	month = {2005/06/20/25},
	pages = {663- 669 vol. 2 - 663- 669 vol. 2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Structure from motion from single flow fields has been studied intensively, but the integration of information from multiple flow fields has not received much attention. Here we address this problem by enforcing constraints on the shape (surface normals) of the scene in view, as opposed to constraints on the structure (depth). The advantage of integrating shape is two-fold. First, we do not need to estimate feature correspondences over multiple frames, but we only need to match patches. Second, the shape vectors in the different views are related only by rotation. This constraint on shape can be combined easily with motion estimation, thus formulating motion and structure estimation from multiple views as a practical constrained minimization problem using a rank-3 constraint. Based on this constraint, we develop a 3D motion technique, which locates through color and motion segmentation, planar patches in the scene, matches patches over multiple frames, and estimates the motion between multiple frames and the shape of the selected scene patches using the image gradients. Experiments evaluate the accuracy of the 3D motion estimation and demonstrate the motion and shape estimation of the technique by super-resolving an image sequence.},
	keywords = {3D motion estimation, Automation, CAMERAS, computational geometry, Computer vision, constrained minimization problem, decoupling translation from rotation, Educational institutions, image colour analysis, image gradients, image resolution, Image segmentation, image sequence, Image sequences, integration of motion fields, Layout, minimisation, Motion estimation, motion field integration, motion segmentation, parameter estimation, planar patches, rank-3 constraint, scene patches, SHAPE, shape and rotation, shape estimation, structure estimation},
	isbn = {0-7695-2372-2},
	doi = {10.1109/CVPR.2005.190},
	author = {Ji,H. and Ferm{\"u}ller, Cornelia}
}
@conference {19665,
	title = {Multitarget tracking with split and merged measurements},
	booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005},
	volume = {1},
	year = {2005},
	month = {2005/06//},
	pages = {605 - 610 vol. 1},
	abstract = {In many multitarget tracking applications in computer vision, a detection algorithm provides locations of potential targets. Subsequently, the measurements are associated with previously estimated target trajectories in a data association step. The output of the detector is often imperfect and the detection data may include multiple, split measurements from a single target or a single merged measurement from several targets. To address this problem, we introduce a multiple hypothesis tracker for interacting targets that generate split and merged measurements. The tracker is based on an efficient Markov chain Monte Carlo (MCMC) based auxiliary variable particle filter. The particle filter is Rao-Blackwellized such that the continuous target state parameters are estimated analytically, and an MCMC sampler generates samples from the large discrete space of data associations. In addition, we include experimental results in a scenario where we track several interacting targets that generate these split and merged measurements.},
	keywords = {Application software, Computer vision, Detection algorithms, Detectors, filtering theory, Markov chain Monte Carlo based auxiliary variable particle filter, Markov processes, merged measurements, Monte Carlo methods, multiple hypothesis tracker, multitarget tracking, parameter estimation, Particle filters, Particle tracking, Rao-Blackwellized filter, split measurements, target tracking, Trajectory},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@conference {11905,
	title = {Non-parametric expectation maximization: a learning automata approach},
	booktitle = {IEEE International Conference on Systems, Man and Cybernetics, 2003},
	volume = {3},
	year = {2003},
	month = {2003/10//},
	pages = {2996- 3001 vol.3 - 2996- 3001 vol.3},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The famous expectation maximization technique suffers two major drawbacks. First, the number of components has to be specified apriori. Also, the expectation maximization is sensitive to initialization. In this paper, we present a new stochastic technique for estimating the mixture parameters. Parzen Window is used to estimate a discrete estimate of the PDF of the given data. Stochastic learning automata is then used to select the mixture parameters that minimize the distance between the discrete estimate of the PDF and the estimate of the expectation maximization. The validity of the proposed approach is verified using bivariate simulation data.},
	keywords = {Automatic speech recognition, bivariate simulation data, Computational modeling, Computer vision, Density functional theory, expectation maximization technique, learning automata, mixture parameters estimation, nonparametric expectation maximization, nonparametric statistics, optimisation, parameter estimation, Parzen Window, PDF, probability, probability density function, Speech processing, Speech recognition, stochastic learning automata, Stochastic processes, stochastic technique},
	isbn = {0-7803-7952-7},
	doi = {10.1109/ICSMC.2003.1244347},
	author = {Abd-Almageed, Wael and El-Osery,A. and Smith,C.E.}
}
@conference {11894,
	title = {Hidden Markov models for silhouette classification},
	booktitle = {Automation Congress, 2002 Proceedings of the 5th Biannual World},
	volume = {13},
	year = {2002},
	month = {2002///},
	pages = {395 - 402},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, a new technique for object classification from silhouettes is presented. Hidden Markov models are used as a classification mechanism. Through a set of experiments, we show the validity of our approach and show its invariance under severe rotation conditions. Also, a comparison with other techniques that use hidden Markov models for object classification from silhouettes is presented.},
	keywords = {Computer vision, Feature extraction, Fourier transforms, hidden Markov models, HMM, image classification, Neural networks, object classification, Object recognition, parameter estimation, pattern recognition, Probability distribution, Shape measurement, silhouette classification, Wavelet transforms},
	isbn = {1-889335-18-5},
	doi = {10.1109/WAC.2002.1049575},
	author = {Abd-Almageed, Wael and Smith,C.}
}
@article {18668,
	title = {Coverage estimation methods for stratified fault-injection},
	journal = {Computers, IEEE Transactions on},
	volume = {48},
	year = {1999},
	month = {1999/07//},
	pages = {707 - 723},
	abstract = {This paper addresses the problem of estimating fault tolerance coverage through statistical processing of observations collected in fault-injection experiments. In an earlier paper, venous estimators based on simple sampling in the complete fault/activity input space and stratified sampling in a partitioned space were studied; frequentist confidence limits were derived based on a normal approximation. In this paper, the validity of this approximation is analyzed. The theory of confidence regions is introduced to estimate coverage without approximation when stratification is used. Three statistics are considered for defining confidence regions. It is shown that one-a vectorial statistic-is often more conservative than the other two. However, only the vectorial statistic is computationally tractable. We then consider Bayesian estimation methods for stratified sampling. Two methods are presented to obtain an approximation of the posterior distribution of the coverage by calculating its moments. The moments are then used to identify the type of the distribution in the Pearson distribution system, to estimate its parameters, and to obtain the coverage confidence limit. Three hypothetical example systems are used to compare the validity and the conservatism of the frequentist and Bayesian estimations},
	keywords = {Bayes methods, Bayesian estimations, confidence regions, coverage estimation methods, fault tolerance coverage, fault tolerant computing, frequentist confidence limits, parameter estimation, parameters estimation, Pearson distribution system, statistical processing, stratified fault-injection, stratified sampling, vectorial statistic},
	isbn = {0018-9340},
	doi = {10.1109/12.780878},
	author = {Michel Cukier and Powell,D. and Ariat,J.}
}
@conference {11993,
	title = {Self-calibration from image derivatives},
	booktitle = {Sixth International Conference on Computer Vision, 1998},
	year = {1998},
	month = {1998/01/04/7},
	pages = {83 - 89},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This study investigates the problem of estimating the calibration parameters from image motion fields induced by a rigidly moving camera with unknown calibration parameters, where the image formation is modeled with a linear pinhole-camera model. The equations obtained show the flow to be clearly separated into a component due to the translation and the calibration parameters and a component due to the rotation and the calibration parameters. A set of parameters encoding the latter component are linearly related to the flow, and from these parameters the calibration can be determined. However, as for discrete motion, in the general case it is not possible, to decouple image measurements from two frames only into their translational and rotational component. Geometrically, the ambiguity takes the form of a part of the rotational component being parallel to the translational component, and thus the scene can be reconstructed only up to a projective transformation. In general, for a full calibration at least four successive image frames are necessary with the 3D-rotation changing between the measurements. The geometric analysis gives rise to a direct self-calibration method that avoids computation of optical flow or point correspondences and uses only normal flow measurements. In this technique the direction of translation is estimated employing in a novel way smoothness constraints. Then the calibration parameters are estimated from the rotational components of several flow fields using Levenberg-Marquardt parameter estimation, iterative in the calibration parameters only. The technique proposed does not require calibration objects in the scene or special camera motions and it also avoids the computation of exact correspondence. This makes it suitable for the calibration of active vision systems which have to acquire knowledge about their intrinsic parameters while they perform other tasks, or as a tool for analyzing image sequences in large video databases},
	keywords = {3D-rotation, active vision, Calibration, CAMERAS, discrete motion, Encoding, Equations, image derivatives, image formation, image measurements, Image motion analysis, image motion fields, Image reconstruction, Image sequences, large video databases, Layout, Levenberg-Marquardt parameter estimation, linear pinhole-camera model, Motion estimation, Motion measurement, Optical computing, parameter estimation, projective transformation, rigidly moving camera, self-calibration, smoothness constraints, unknown calibration parameters},
	isbn = {81-7319-221-9},
	doi = {10.1109/ICCV.1998.710704},
	author = {Brodsky, T. and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {14246,
	title = {Perceptual computational advantages of tracking},
	booktitle = {, 11th IAPR International Conference on Pattern Recognition, 1992. Vol.I. Conference A: Computer Vision and Applications, Proceedings},
	year = {1992},
	month = {1992/09/30/Aug-3},
	pages = {599 - 602},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The paradigm of active vision advocates studying visual problems in the form of modules that are directly related to a visual task for observers that are active. It is argued that in many cases when an object is moving in an unrestricted manner (translation and rotation) in the 3D world only the motion{\textquoteright}s translational components are of interest. For a monocular observer, using only the normal flow-the spatiotemporal derivatives of the image intensity function-the authors solve the problem of computing the direction of translation. Their strategy uses fixation and tracking. Fixation simplifies much of the computation by placing the object at the center of the visual field, and the main advantage of tracking is the accumulation of information over time. The authors show how tracking is accomplished using normal flow measurements and use it for two different tasks in the solution process. First, it serves as a tool to compensate for the lack of existence of an optical flow field and thus to estimate the translation parallel to the image plane; and second, it gathers information about the motion component perpendicular to the image plane},
	keywords = {active vision, Automation, Employment, fixation, image intensity function, Image motion analysis, IMAGE PROCESSING, Motion estimation, Nonlinear optics, Optical computing, Optical sensors, parameter estimation, pattern recognition, perceptual computational advantages, spatiotemporal derivatives, Spatiotemporal phenomena, tracking, unrestricted motion, visual flow measurements},
	isbn = {0-8186-2910-X},
	doi = {10.1109/ICPR.1992.201633},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {11974,
	title = {Optimal motion estimation},
	booktitle = {Workshop on Visual Motion, 1989.,Proceedings},
	year = {1989},
	month = {1989/03/20/22},
	pages = {229 - 237},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The problem of using feature correspondences to recover the structure and 3D motion of a moving object from its successive images is analyzed. They formulate the problem as a quadratic minimization problem with a nonlinear constraint. Then they derive the condition for the solution to be optimal under the assumption of Gaussian noise in the input, in the maximum-likelihood-principle sense. The authors present two efficient ways to approximate it and discuss some inherent limitations of the structure-from-motion problem when two frames are used that should be taken into account in robotics applications that involve dynamic imagery. Finally, it is shown that some of the difficulties inherent in the two-frame approach disappear when redundancy in the data is introduced. This is concluded from experiments using a structure-from-motion algorithm that is based on multiple frames and uses only the rigidity assumption},
	keywords = {3D motion interpretation, Automation, Computer vision, computerised pattern recognition, computerised picture processing, constraint minimization, dynamic imagery, Educational institutions, feature correspondences, Gaussian noise, Image motion analysis, Laboratories, maximum-likelihood-principle, Minimization methods, Motion analysis, Motion estimation, motion parameters, moving object, multiple frames, nonlinear constraint, Optical computing, optimal motion estimation, parameter estimation, quadratic minimization, quadratic programming, redundancy, rigidity assumption, robotics applications, structure-from-motion, successive images, two-frame},
	isbn = {0-8186-1903-1},
	doi = {10.1109/WVM.1989.47114},
	author = {Spetsakis, M. E and Aloimonos, J.}
}
