@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 {18463,
	title = {An audio-video front-end for multimedia applications},
	booktitle = {2000 IEEE International Conference on Systems, Man, and Cybernetics},
	volume = {2},
	year = {2000},
	month = {2000///},
	pages = {786-791 vol.2 - 786-791 vol.2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Applications such as video gaming, virtual reality, multimodal user interfaces and videoconferencing, require systems that can locate and track persons in a room through a combination of visual and audio cues, enhance the sound that they produce, and perform identification. We describe the development of a particular multimodal sensor fusion system that is portable, runs in real time and achieves these objectives. The system employs novel algorithms for acoustical source location, video-based person tracking and overall system control, which are also described},
	keywords = {Acoustic noise, acoustical source location, Application software, audio cues, audio-video front-end, CAMERAS, Computer vision, Microphones, multimedia applications, multimedia systems, multimodal sensor fusion system, multimodal user interfaces, Position measurement, REAL TIME, Real time systems, real-time systems, sensor fusion, sound, Speech recognition, User interfaces, video cameras, video gaming, video-based person tracking, Videoconference, videoconferencing, Virtual reality, visual cues, Working environment noise},
	isbn = {0-7803-6583-6},
	doi = {10.1109/ICSMC.2000.885945},
	author = {Zotkin,Dmitry N and Duraiswami, Ramani and Davis, Larry S. and Haritaoglu,I.}
}
@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.}
}
