@conference {17484,
	title = {Visual Analysis of Temporal Trends in Social Networks Using Edge Color Coding and Metric Timelines},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {549 - 554},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present Net EvViz, a visualization tool for analysis and exploration of a dynamic social network. There are plenty of visual social network analysis tools but few provide features for visualization of dynamically changing networks featuring the addition or deletion of nodes or edges. Our tool extends the code base of the Node XL template for Microsoft Excel, a popular network visualization tool. The key features of this work are (1) The ability of the user to specify and edit temporal annotations to the network components in an Excel sheet, (2) See the dynamics of the network with multiple graph metrics plotted over the time span of the graph, called the Timeline, and (3) Temporal exploration of the network layout using an edge coloring scheme and a dynamic Time slider. The objectives of the new features presented in this paper are to let the data analysts, computer scientists and others to observe the dynamics or evolution in a network interactively. We presented Net EvViz to five users of Node XL and received positive responses.},
	keywords = {Color, computer scientists, data analysts, data visualisation, Data visualization, dynamic social network, dynamic timeslider, edge color coding, excel sheet, Image coding, image colour analysis, Layout, measurement, metric timelines, Microsoft excel, multiple graph metrics, Net EvViz, network components, network layout, network visualization tool, NodeXL template, social networking (online), temporal trends, Twitter, Visualization},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.212},
	author = {Khurana,U. and Nguyen,Viet-An and Hsueh-Chien Cheng and Ahn,Jae-wook and Chen,Xi and Shneiderman, Ben}
}
@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 {15064,
	title = {An improved mean shift tracking method based on nonparametric clustering and adaptive bandwidth},
	booktitle = {Machine Learning and Cybernetics, 2008 International Conference on},
	volume = {5},
	year = {2008},
	month = {2008/07//},
	pages = {2779 - 2784},
	abstract = {An improved mean shift method for object tracking based on nonparametric clustering and adaptive bandwidth is presented in this paper. Based on partitioning the color space of a tracked object by using a modified nonparametric clustering, an appearance model of the tracked object is built. It captures both the color information and spatial layout of the tracked object. The similarity measure between the target model and the target candidate is derived from the Bhattacharyya coefficient. The kernel bandwidth parameters are automatically selected by maximizing the lower bound of a log-likelihood function, which is derived from a kernel density estimate using the bandwidth matrix and the modified weight function. The experimental results show that the method can converge in an average of 2.6 iterations per frame.},
	keywords = {adaptive bandwidth, appearance model, bandwidth matrix, Bhattacharyya coefficient, color information, color space partitioning, image colour analysis, iterative procedure, kernel bandwidth parameter, kernel density estimate, log-likelihood function, mean shift tracking method, modified weight function, nonparametric clustering, Object detection, object representation, object tracking, pattern clustering, similarity measure, spatial layout, target candidate, target model, tracking},
	doi = {10.1109/ICMLC.2008.4620880},
	author = {Zhuolin Jiang and Li,Shao-Fa and Jia,Xi-Ping and Zhu,Hong-Li}
}
@conference {14224,
	title = {Integration of motion fields through shape},
	booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005},
	volume = {2},
	year = {2005},
	month = {2005/06/20/25},
	pages = {663- 669 vol. 2 - 663- 669 vol. 2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Structure from motion from single flow fields has been studied intensively, but the integration of information from multiple flow fields has not received much attention. Here we address this problem by enforcing constraints on the shape (surface normals) of the scene in view, as opposed to constraints on the structure (depth). The advantage of integrating shape is two-fold. First, we do not need to estimate feature correspondences over multiple frames, but we only need to match patches. Second, the shape vectors in the different views are related only by rotation. This constraint on shape can be combined easily with motion estimation, thus formulating motion and structure estimation from multiple views as a practical constrained minimization problem using a rank-3 constraint. Based on this constraint, we develop a 3D motion technique, which locates through color and motion segmentation, planar patches in the scene, matches patches over multiple frames, and estimates the motion between multiple frames and the shape of the selected scene patches using the image gradients. Experiments evaluate the accuracy of the 3D motion estimation and demonstrate the motion and shape estimation of the technique by super-resolving an image sequence.},
	keywords = {3D motion estimation, Automation, CAMERAS, computational geometry, Computer vision, constrained minimization problem, decoupling translation from rotation, Educational institutions, image colour analysis, image gradients, image resolution, Image segmentation, image sequence, Image sequences, integration of motion fields, Layout, minimisation, Motion estimation, motion field integration, motion segmentation, parameter estimation, planar patches, rank-3 constraint, scene patches, SHAPE, shape and rotation, shape estimation, structure estimation},
	isbn = {0-7695-2372-2},
	doi = {10.1109/CVPR.2005.190},
	author = {Ji,H. and Ferm{\"u}ller, Cornelia}
}
@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.}
}
