@conference {14232,
	title = {Multi-camera networks: eyes from eyes},
	booktitle = {IEEE Workshop on Omnidirectional Vision, 2000. Proceedings},
	year = {2000},
	month = {2000///},
	pages = {11 - 18},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Autonomous or semi-autonomous intelligent systems, in order to function appropriately, need to create models of their environment, i.e., models of space time. These are descriptions of objects and scenes and descriptions of changes of space over time, that is, events and actions. Despite the large amount of research on this problem, as a community we are still far from developing robust descriptions of a system{\textquoteright}s spatiotemporal environment using video input (image sequences). Undoubtedly, some progress has been made regarding the understanding of estimating the structure of visual space, but it has not led to solutions to specific applications. There is, however, an alternative approach which is in line with today{\textquoteright}s {\textquotedblleft}zeitgeist.{\textquotedblright} The vision of artificial systems can be enhanced by providing them with new eyes. If conventional video cameras are put together in various configurations, new sensors can be constructed that have much more power and the way they {\textquotedblleft}see{\textquotedblright} the world makes it much easier to solve problems of vision. This research is motivated by examining the wide variety of eye design in the biological world and obtaining inspiration for an ensemble of computational studies that relate how a system sees to what that system does (i.e. relating perception to action). This, coupled with the geometry of multiple views that has flourished in terms of theoretical results in the past few years, points to new ways of constructing powerful imaging devices which suit particular tasks in robotics, visualization, video processing, virtual reality and various computer vision applications, better than conventional cameras. This paper presents a number of new sensors that we built using common video cameras and shows their superiority with regard to developing models of space and motion},
	keywords = {Biosensors, CAMERAS, Computer vision, Eyes, Image sequences, intelligent systems, Layout, Machine vision, Robot vision systems, Robustness, Spatiotemporal phenomena, video cameras, Virtual reality},
	isbn = {0-7695-0704-2},
	doi = {10.1109/OMNVIS.2000.853797},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J. and Baker, P. and Pless, R. and Neumann, J. and Stuart, B.}
}
@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 {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.}
}
