@conference {14207,
	title = {Combining motion from texture and lines for visual navigation},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007. IROS 2007},
	year = {2007},
	month = {2007/11/29/Oct. },
	pages = {232 - 239},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Two novel methods for computing 3D structure information from video for a piecewise planar scene are presented. The first method is based on a new line constraint, which clearly separates the estimation of distance from the estimation of slant. The second method exploits the concepts of phase correlation to compute from the change of image frequencies of a textured plane, distance and slant information. The two different estimates together with structure estimates from classical image motion are combined and integrated over time using an extended Kalman filter. The estimation of the scene structure is demonstrated experimentally in a motion control algorithm that allows the robot to move along a corridor. We demonstrate the efficacy of each individual method and their combination and show that the method allows for visual navigation in textured as well as un-textured environments.},
	keywords = {3D structure information, CAMERAS, Computer vision, extended Kalman filter, Frequency, image frequencies, Image motion analysis, Image texture, Kalman filters, Layout, motion control, Motion estimation, Navigation, Optical computing, phase correlation, piecewise planar scene, Robustness, Simultaneous localization and mapping, Speech processing, textured plane, video signal processing, visual navigation},
	isbn = {978-1-4244-0912-9},
	doi = {10.1109/IROS.2007.4399568},
	author = {Bitsakos,K. and Li Yi and Ferm{\"u}ller, Cornelia}
}
@conference {11986,
	title = {Independent motion: the importance of history},
	booktitle = {Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.},
	volume = {2},
	year = {1999},
	month = {1999///},
	pages = {97 Vol. 2 - 97 Vol. 2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We consider a problem central in aerial visual surveillance applications-detection and tracking of small, independently moving objects in long and noisy video sequences. We directly use spatiotemporal image intensity gradient measurements to compute an exact model of background motion. This allows the creation of accurate mosaics over many frames and the definition of a constraint violation function which acts as an indication of independent motion. A novel temporal integration method maintains confidence measures over long subsequences without computing the optic flow, requiring object models, or using a Kalman filler. The mosaic acts as a stable feature frame, allowing precise localization of the independently moving objects. We present a statistical analysis of the effects of image noise on the constraint violation measure and find a good match between the predicted probability distribution function and the measured sample frequencies in a test sequence},
	keywords = {aerial visual surveillance, background image, Fluid flow measurement, Frequency measurement, History, Motion detection, Motion estimation, Motion measurement, Noise measurement, Optical computing, Optical noise, spatiotemporal image intensity gradient measurements, Spatiotemporal phenomena, Surveillance, Video sequences},
	isbn = {0-7695-0149-4},
	doi = {10.1109/CVPR.1999.784614},
	author = {Pless, R. and Brodsky, T. and Aloimonos, J.}
}
@conference {11965,
	title = {Statistical biases in optic flow},
	booktitle = {Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.},
	volume = {1},
	year = {1999},
	month = {1999///},
	pages = {566 Vol. 1 - 566 Vol. 1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The computation of optical flow from image derivatives is biased in regions of non uniform gradient distributions. A least-squares or total least squares approach to computing optic flow from image derivatives even in regions of consistent flow can lead to a systematic bias dependent upon the direction of the optic flow, the distribution of the gradient directions, and the distribution of the image noise. The bias a consistent underestimation of length and a directional error. Similar results hold for various methods of computing optical flow in the spatiotemporal frequency domain. The predicted bias in the optical flow is consistent with psychophysical evidence of human judgment of the velocity of moving plaids, and provides an explanation of the Ouchi illusion. Correction of the bias requires accurate estimates of the noise distribution; the failure of the human visual system to make these corrections illustrates both the difficulty of the task and the feasibility of using this distorted optic flow or undistorted normal flow in tasks requiring higher lever processing},
	keywords = {Distributed computing, Frequency domain analysis, HUMANS, image derivatives, Image motion analysis, Image sequences, Least squares methods, Motion estimation, Optical computing, Optical distortion, optical flow, Optical noise, Ouchi illusion, perception of motion, Psychology, Spatiotemporal phenomena, statistical analysis, systematic bias, total least squares},
	isbn = {0-7695-0149-4},
	doi = {10.1109/CVPR.1999.786994},
	author = {Ferm{\"u}ller, Cornelia and Pless, R. and Aloimonos, 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.}
}
@article {18317,
	title = {Recognizing human facial expressions from long image sequences using optical flow},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {18},
	year = {1996},
	month = {1996/06//},
	pages = {636 - 642},
	abstract = {An approach to the analysis and representation of facial dynamics for recognition of facial expressions from image sequences is presented. The algorithms utilize optical flow computation to identify the direction of rigid and nonrigid motions that are caused by human facial expressions. A mid-level symbolic representation motivated by psychological considerations is developed. Recognition of six facial expressions, as well as eye blinking, is demonstrated on a large set of image sequences},
	keywords = {Computer vision, Eyebrows, face recognition, facial dynamics, Facial features, human facial expression recognition, HUMANS, Image motion analysis, image recognition, image representation, Image sequences, Motion analysis, Motion estimation, Optical computing, optical flow, symbolic representation, tracking},
	isbn = {0162-8828},
	doi = {10.1109/34.506414},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
@conference {12033,
	title = {3D motion representations in visual servo control},
	booktitle = {Proceedings of International Symposium on Computer Vision, 1995},
	year = {1995},
	month = {1995/11/21/23},
	pages = {61 - 66},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A new approach to visual servoing and vision-guided robotics is introduced. This approach uses visual information for autonomous behavior. It amounts to using robust, global spatiotemporal representations easily extracted from the dynamic imagery. Specifically, the geometrical patterns of normal flows are used as the input to the servo mechanism. It is shown that the positions of these patterns are related to the three dimensional motion parameters. By locating the positions of these patterns, we can solve a variety of navigational problems with little computational effort},
	keywords = {3D motion representations, autonomous behavior, CAMERAS, computational effort, Computer vision, dynamic imagery, global spatiotemporal representations, image representation, Mobile robots, motion control, Navigation, navigational problems, Optical computing, Robot kinematics, Robot sensing systems, robot vision, Robot vision systems, Robotics and automation, servomechanisms, Servosystems, Spatiotemporal phenomena, vision-guided robotics, visual information, visual servo control, visual servoing},
	isbn = {0-8186-7190-4},
	doi = {10.1109/ISCV.1995.476978},
	author = {Ferm{\"u}ller, Cornelia and LoongFah Cheong 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 {11990,
	title = {Relative depth from motion using normal flow: an active and purposive solution},
	booktitle = {Proceedings of the IEEE Workshop on Visual Motion, 1991},
	year = {1991},
	month = {1991/10/07/9},
	pages = {196 - 204},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The authors show how an active observer can compute the relative depth of (stationary or moving) objects in the field of view using only the spatiotemporal derivatives of the time varying image intensity function. The solution they propose is purposive in the sense that it solves only the relative depth from motion problem and cannot be used for other problems related to motion; active in the sense that the activity of the observer is essential for the solution of the problem. Results indicate that exact computation of retinal motion does not appear to be a necessary first step for some problems related to visual motion. In addition, optic flow, whose computation is an ill-posed problem, is related to the motion of the scene only under very restrictive assumptions. As a result, the use of optic flow in some quantitative motion analysis studies is questionable},
	keywords = {3D information, Automation, Computer vision, Educational institutions, Image motion analysis, image recognition, Image sequences, Laboratories, Layout, Motion analysis, Motion estimation, Nonlinear optics, normal flow, optic flow, Optical computing, Optical sensors, relative depth, retinal motion, spatiotemporal derivatives, time varying image intensity function, visual motion},
	isbn = {0-8186-2153-2},
	doi = {10.1109/WVM.1991.212807},
	author = {Huang, Liuqing 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.}
}
@conference {11957,
	title = {Optimal Computing Of Structure From Motion Using Point Correspondences In Two Frames},
	booktitle = {Proceedings of Second International Conference on Computer Vision},
	year = {1988},
	month = {1988/12/05/8},
	pages = {449 - 453},
	publisher = {IEEE},
	organization = {IEEE},
	keywords = {Automation, Computer vision, Educational institutions, Gaussian noise, Image motion analysis, Laboratories, Least squares approximation, Least squares methods, Motion estimation, Optical computing},
	isbn = {0-8186-0883-8},
	doi = {10.1109/CCV.1988.590022},
	author = {Spetsakis, M. E and Aloimonos, J.}
}
@conference {12024,
	title = {Determining three dimensional transformation parameters from images: Theory},
	booktitle = {1987 IEEE International Conference on Robotics and Automation. Proceedings},
	volume = {4},
	year = {1987},
	month = {1987/03//},
	pages = {57 - 61},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a theory for the determination of the three dimensional transformation parameters of an object, from its images. The input to this process is the image intensity function and its temporal derivative. In particular, our results are: 1) If the structure of the transforming object in view is known, then the transformation parameters are determined from the solution of a linear system. Rigid motion is a special ease of our theory. 2)If the structure of the object in view is not known, then both the structure and transformation parameters may be computed through a hill climbing or simulated annealing algorithm.},
	keywords = {Automation, Computational modeling, Educational institutions, Image motion analysis, Indium tin oxide, Linear systems, Motion estimation, Optical computing, Robots, Simulated annealing},
	doi = {10.1109/ROBOT.1987.1088011},
	author = {Ito, E. and Aloimonos, J.}
}
