@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 {14251,
	title = {Real-time shape retrieval for robotics using skip Tri-Grams},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009. IROS 2009},
	year = {2009},
	month = {2009/10/10/15},
	pages = {4731 - 4738},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The real time requirement is an additional constraint on many intelligent applications in robotics, such as shape recognition and retrieval using a mobile robot platform. In this paper, we present a scalable approach for efficiently retrieving closed contour shapes. The contour of an object is represented by piecewise linear segments. A skip Tri-Gram is obtained by selecting three segments in the clockwise order while allowing a constant number of segments to be {\~A}{\^A}{\textquestiondown}skipped{\~A}{\^A}{\textquestiondown} in between. The main idea is to use skip Tri-Grams of the segments to implicitly encode the distant dependency of the shape. All skip Tri-Grams are used for efficiently retrieving closed contour shapes without pairwise matching feature points from two shapes. The retrieval is at least an order of magnitude faster than other state-of-the-art algorithms. We score 80\% in the Bullseye retrieval test on the whole MPEG 7 shape dataset. We further test the algorithm using a mobile robot platform in an indoor environment. 8 objects are used for testing from different viewing directions, and we achieve 82\% accuracy.},
	keywords = {Bullseye retrieval test, Clocks, closed contour shape retrieval, Image retrieval, Image segmentation, Indexing, Information retrieval, Intelligent robots, Jacobian matrices, mobile robot, Mobile robots, MPEG 7 shape dataset, piecewise linear segments, Piecewise linear techniques, Real time systems, real-time shape retrieval, robot vision, SHAPE, shape recognition, shape representation, skip Tri-Grams, Testing},
	isbn = {978-1-4244-3803-7},
	doi = {10.1109/IROS.2009.5354738},
	author = {Li,Yi and Bitsakos,K. and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@article {19661,
	title = {How Multirobot Systems Research will Accelerate our Understanding of Social Animal Behavior},
	journal = {Proceedings of the IEEE},
	volume = {94},
	year = {2006},
	month = {2006/07//},
	pages = {1445 - 1463},
	abstract = {Our understanding of social insect behavior has significantly influenced artificial intelligence (AI) and multirobot systems{\textquoteright} research (e.g., ant algorithms and swarm robotics). In this work, however, we focus on the opposite question: "How can multirobot systems research contribute to the understanding of social animal behavior?" As we show, we are able to contribute at several levels. First, using algorithms that originated in the robotics community, we can track animals under observation to provide essential quantitative data for animal behavior research. Second, by developing and applying algorithms originating in speech recognition and computer vision, we can automatically label the behavior of animals under observation. In some cases the automatic labeling is more accurate and consistent than manual behavior identification. Our ultimate goal, however, is to automatically create, from observation, executable models of behavior. An executable model is a control program for an agent that can run in simulation (or on a robot). The representation for these executable models is drawn from research in multirobot systems programming. In this paper we present the algorithms we have developed for tracking, recognizing, and learning models of social animal behavior, details of their implementation, and quantitative experimental results using them to study social insects},
	keywords = {Acceleration, Animal behavior, ant movement tracking, Artificial intelligence, biology computing, Computer vision, control engineering computing, Insects, Intelligent robots, Labeling, monkey movement tracking, multi-robot systems, multirobot systems, robotics algorithms, Robotics and automation, social animal behavior, social animals, social insect behavior, Speech recognition, tracking},
	isbn = {0018-9219},
	author = {Balch, T. and Dellaert, F. and Feldman, A. and Guillory, A. and Isbell, C.L. and Zia Khan and Pratt, S.C. and Stein, A.N. and Wilde, H.}
}
@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 {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.}
}
