@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 {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}
}
