@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 {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.}
}
