@conference {14193,
	title = {Active segmentation for robotics},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009. IROS 2009},
	year = {2009},
	month = {2009/10/10/15},
	pages = {3133 - 3139},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The semantic robots of the immediate future are robots that will be able to find and recognize objects in any environment. They need the capability of segmenting objects in their visual field. In this paper, we propose a novel approach to segmentation based on the operation of fixation by an active observer. Our approach is different from current approaches: while existing works attempt to segment the whole scene at once into many areas, we segment only one image region, specifically the one containing the fixation point. Furthermore, our solution integrates monocular cues (color, texture) with binocular cues (stereo disparities and optical flow). Experiments with real imagery collected by our active robot and from the known databases demonstrate the promise of the approach.},
	keywords = {binocular cues, image colour analysis, Image segmentation, Image texture, Intelligent robots, Layout, Machine vision, monocular cues, Navigation, optical flow, Orbital robotics, Robot sensing systems, robot vision, Robot vision systems, robotics active segmentation, Robotics and automation, semantic robot, Simultaneous localization and mapping, stereo disparity, stereo image processing},
	isbn = {978-1-4244-3803-7},
	doi = {10.1109/IROS.2009.5354325},
	author = {Mishra,A. and Aloimonos, J. and Ferm{\"u}ller, Cornelia}
}
@conference {11975,
	title = {Robust Contrast Invariant Stereo Correspondence},
	booktitle = {Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005. ICRA 2005},
	year = {2005},
	month = {2005/04/18/22},
	pages = {819 - 824},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A stereo pair of cameras attached to a robot will inevitably yield images with different contrast. Even if we assume that the camera hardware is identical, due to slightly different points of view, the amount of light entering the two cameras is also different, causing dynamically adjusted internal parameters such as aperture, exposure and gain to be different. Due to the difficulty of obtaining and maintaining precise intensity or color calibration between the two cameras, contrast invariance becomes an extremely desirable property of stereo correspondence algorithms. The problem of achieving point correspondence between a stereo pair of images is often addressed by using the intensity or color differences as a local matching metric, which is sensitive to contrast changes. We present an algorithm for contrast invariant stereo matching which relies on multiple spatial frequency channels for local matching. A fast global framework uses the local matching to compute the correspondences and find the occlusions. We demonstrate that the use of multiple frequency channels allows the algorithm to yield good results even in the presence of significant amounts of noise.},
	keywords = {Apertures, Calibration, CAMERAS, Computer science, contrast invariance, diffusion, Educational institutions, Frequency, gabor, Hardware, occlusions, Robot vision systems, Robotics and automation, Robustness, stereo},
	isbn = {0-7803-8914-X},
	doi = {10.1109/ROBOT.2005.1570218},
	author = {Ogale, A. S and Aloimonos, J.}
}
@article {14263,
	title = {The Argus eye: a new imaging system designed to facilitate robotic tasks of motion},
	journal = {IEEE Robotics \& Automation Magazine},
	volume = {11},
	year = {2004},
	month = {2004/12//},
	pages = {31 - 38},
	abstract = {This article describes an imaging system that has been designed to facilitate robotic tasks of motion. The system consists of a number of cameras in a network, arranged so that they sample different parts of the visual sphere. This geometric configuration has provable advantages compared to small field of view cameras for the estimation of the system{\textquoteright}s own motion and, consequently, the estimation of shape models from the individual cameras. The reason is, inherent ambiguities of confusion between translation and rotation disappear. Pairs of cameras may also be arranged in multiple stereo configurations, which provide additional advantages for segmentation. Algorithms for the calibration of the system and the three-dimensional (3-D) motion estimation are provided.},
	keywords = {Argus eye, Calibration, CAMERAS, computational geometry, Design automation, Eyes, image formation, imaging system, Information geometry, Layout, Motion estimation, multiple stereo configurations, panoramic robots, robot vision, Robot vision systems, robotic motion tasks, Robotics and automation, SHAPE, shape model estimation, system calibration},
	isbn = {1070-9932},
	doi = {10.1109/MRA.2004.1371606},
	author = {Baker, P. and Ogale, A. S and Ferm{\"u}ller, Cornelia}
}
@conference {11896,
	title = {Kernel snakes: non-parametric active contour models},
	booktitle = {IEEE International Conference on Systems, Man and Cybernetics, 2003},
	volume = {1},
	year = {2003},
	month = {2003/10//},
	pages = {240- 244 vol.1 - 240- 244 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, a new non-parametric generalized formulation to statistical pressure snakes is presented. We discuss the shortcomings of the traditional pressure snakes. We then introduce a new generic pressure model that alleviates these shortcomings, based on the Bayesian decision theory. Non-parametric techniques are used to obtain the statistical models that drive the snake. We discuss the advantages of using the proposed non-parametric model compared to other parametric techniques. Multi-colored-target tracking is used to demonstrate the performance of the proposed approach. Experimental results show enhanced, real-time performance.},
	keywords = {Active contours, Artificial intelligence, Bayes methods, Bayesian decision theory, Bayesian methods, decision theory, Deformable models, Image edge detection, Image segmentation, Intelligent robots, Kernel, kernel snakes, Laboratories, multicolored target tracking, nonparametric active contour models, nonparametric generalized formulation, nonparametric model, nonparametric statistics, nonparametric techniques, real time performance, Robot vision systems, statistical pressure snakes, target tracking},
	isbn = {0-7803-7952-7},
	doi = {10.1109/ICSMC.2003.1243822},
	author = {Abd-Almageed, Wael and Smith,C.E. and Ramadan,S.}
}
@conference {14235,
	title = {New eyes for robotics},
	booktitle = {2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings},
	volume = {1},
	year = {2003},
	month = {2003/10/27/31},
	pages = {1018- 1023 vol.1 - 1018- 1023 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper describes an imaging system that has been designed to facilitate robotic tasks of motion. The system consists of a number of cameras in a network arranged so that they sample different parts of the visual sphere. This geometric configuration has provable advantages compared to small field of view cameras for the estimation of the system{\textquoteright}s own motion and consequently the estimation of shape models from the individual cameras. The reason is that inherent ambiguities of confusion between translation and rotation disappear. Pairs of cameras may also be arranged in multiple stereo configurations which provide additional advantages for segmentation. Algorithms for the calibration of the system and the 3D motion estimation are provided.},
	keywords = {3D motion estimation, Argus eye, array signal processing, Birds, Calibration, CAMERAS, Control systems, Eyes, geometric configuration, imaging, imaging system, Layout, Motion estimation, multiple stereo configurations, Robot kinematics, robot vision, Robot vision systems, ROBOTICS, Robotics and automation, SHAPE, shape models},
	isbn = {0-7803-7860-1},
	doi = {10.1109/IROS.2003.1250761},
	author = {Baker, P. and Ogale, A. S and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {11904,
	title = {Contour migration: solving object ambiguity with shape-space visual guidance},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {330- 335 vol.1 - 330- 335 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A fundamental problem in computer vision is the issue of shape ambiguity. Simply stated, a silhouette cannot uniquely identify an object or an object{\textquoteright}s classification since many unique objects can present identical occluding contours. This problem has no solution in the general case for a monocular vision system. This paper presents a method for disambiguating objects during silhouette matching using a visual servoing system. This method identifies the camera motion(s) that gives disambiguating views of the objects. These motions are identified through a new technique called contour migration. The occluding contour{\textquoteright}s shape is used to identify objects or object classes that are potential matches for that shape. A contour migration is then determined that disambiguates the possible matches by purposive viewpoint adjustment. The technique is demonstrated using an example set of objects.},
	keywords = {Artificial intelligence, camera motion, CAMERAS, Computer vision, contour migration, Databases, edge detection, Intelligent robots, Laboratories, Machine vision, object ambiguity, Object recognition, pattern matching, Robot vision systems, servomechanisms, SHAPE, shape matching, shape-space visual guidance, silhouette matching, visual servoing},
	isbn = {0-7803-7398-7},
	doi = {10.1109/IRDS.2002.1041410},
	author = {Abd-Almageed, Wael and Smith,C.E.}
}
@conference {11899,
	title = {Mixture models for dynamic statistical pressure snakes},
	booktitle = {16th International Conference on Pattern Recognition, 2002. Proceedings},
	volume = {2},
	year = {2002},
	month = {2002///},
	pages = {721- 724 vol.2 - 721- 724 vol.2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper introduces a new approach to statistical pressure snakes. It uses statistical modeling for both object and background to obtain a more robust pressure model. The Expectation Maximization (EM) algorithm is used to model the data into a Mixture of Gaussians (MoG). Bayesian theory is then employed as a decision making mechanism. Experimental results using the traditional pressure model and the new mixture pressure model demonstrate the effectiveness of the new models.},
	keywords = {active contour models, Active contours, Artificial intelligence, Bayes methods, Bayesian methods, Bayesian theory, complex colored object, Computer vision, decision making, decision making mechanism, dynamic statistical pressure snakes, Equations, expectation maximization algorithm, Gaussian distribution, image colour analysis, Image edge detection, Image segmentation, Intelligent robots, mixture models, mixture of Gaussians, mixture pressure model, Robot vision systems, robust pressure model, Robustness, segmentation results, statistical analysis, statistical modeling},
	isbn = {0-7695-1695-X},
	doi = {10.1109/ICPR.2002.1048404},
	author = {Abd-Almageed, Wael and Smith,C.E.}
}
@conference {14188,
	title = {A spherical eye from multiple cameras (makes better models of the world)},
	booktitle = {Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001},
	volume = {1},
	year = {2001},
	month = {2001///},
	pages = {I-576- I-583 vol.1 - I-576- I-583 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The paper describes an imaging system that has been designed specifically for the purpose of recovering egomotion and structure from video. The system consists of six cameras in a network arranged so that they sample different parts of the visual sphere. This geometric configuration has provable advantages compared to small field of view cameras for the estimation of the system{\textquoteright}s own motion and consequently the estimation of shape models from the individual cameras. The reason is that inherent ambiguities of confusion between translation and rotation disappear. We provide algorithms for the calibration of the system and 3D motion estimation. The calibration is based on a new geometric constraint that relates the images of lines parallel in space to the rotation between the cameras. The 3D motion estimation uses a constraint relating structure directly to image gradients.},
	keywords = {3D motion estimation, Calibration, camera network, CAMERAS, Computer vision, egomotion recovery, geometric configuration, geometric constraint, image gradients, image sampling, imaging system, Laboratories, Layout, Motion estimation, multiple cameras, Pixel, Robot vision systems, SHAPE, shape models, Space technology, spherical eye, system calibration, video, video cameras, video signal processing, visual sphere sampling},
	isbn = {0-7695-1272-0},
	doi = {10.1109/CVPR.2001.990525},
	author = {Baker, P. and Ferm{\"u}ller, Cornelia and Aloimonos, J. and Pless, R.}
}
@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 {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.}
}
