@article {16983,
	title = {Balancing Systematic and Flexible Exploration of Social Networks},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {12},
	year = {2006},
	month = {2006/10//Sept},
	pages = {693 - 700},
	abstract = {Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively. We present SocialAction, a system that uses attribute ranking and coordinated views to help users systematically examine numerous SNA measures. Users can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix overview. For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. SocialAction offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks},
	keywords = {Aggregates, algorithms, attribute ranking, Cluster Analysis, Computer Graphics, Computer simulation, Coordinate measuring machines, coordinated views, Data analysis, data visualisation, Data visualization, exploratory data analysis, Filters, Gain measurement, graph theory, Graphical user interfaces, Information Storage and Retrieval, interactive graph visualization, matrix algebra, matrix overview, Models, Biological, Navigation, network visualization, Pattern analysis, Population Dynamics, Social Behavior, social network analysis, Social network services, social networks, social sciences computing, Social Support, SocialAction, software, statistical analysis, statistical methods, User-Computer Interface},
	isbn = {1077-2626},
	doi = {10.1109/TVCG.2006.122},
	author = {Perer,A. and Shneiderman, Ben}
}
@article {19663,
	title = {MCMC-based particle filtering for tracking a variable number of interacting targets},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {27},
	year = {2005},
	month = {2005/11//},
	pages = {1805 - 1819},
	abstract = {We describe a particle filter that effectively deals with interacting targets, targets that are influenced by the proximity and/or behavior of other targets. The particle filter includes a Markov random field (MRF) motion prior that helps maintain the identity of targets throughout an interaction, significantly reducing tracker failures. We show that this MRF prior can be easily implemented by including an additional interaction factor in the importance weights of the particle filter. However, the computational requirements of the resulting multitarget filter render it unusable for large numbers of targets. Consequently, we replace the traditional importance sampling step in the particle filter with a novel Markov chain Monte Carlo (MCMC) sampling step to obtain a more efficient MCMC-based multitarget filter. We also show how to extend this MCMC-based filter to address a variable number of interacting targets. Finally, we present both qualitative and quantitative experimental results, demonstrating that the resulting particle filters deal efficiently and effectively with complicated target interactions.},
	keywords = {algorithms, Animals, Artificial intelligence, Computer simulation, Computer vision, Filtering, filtering theory, HUMANS, Image Enhancement, Image Interpretation, Computer-Assisted, Index Terms- Particle filters, Information Storage and Retrieval, Insects, interacting targets, Markov chain Monte Carlo sampling step, Markov chain Monte Carlo., Markov chains, Markov processes, Markov random field motion, Markov random fields, Models, Biological, Models, Statistical, Monte Carlo Method, Monte Carlo methods, MOTION, Movement, multitarget filter, multitarget tracking, particle filtering, Particle filters, Particle tracking, Pattern Recognition, Automated, Sampling methods, Subtraction Technique, target tracking, Video Recording},
	isbn = {0162-8828},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@article {12682,
	title = {Visual tracking and recognition using appearance-adaptive models in particle filters},
	journal = {IEEE Transactions on Image Processing},
	volume = {13},
	year = {2004},
	month = {2004/11//},
	pages = {1491 - 1506},
	abstract = {We present an approach that incorporates appearance-adaptive models in a particle filter to realize robust visual tracking and recognition algorithms. Tracking needs modeling interframe motion and appearance changes, whereas recognition needs modeling appearance changes between frames and gallery images. In conventional tracking algorithms, the appearance model is either fixed or rapidly changing, and the motion model is simply a random walk with fixed noise variance. Also, the number of particles is typically fixed. All these factors make the visual tracker unstable. To stabilize the tracker, we propose the following modifications: an observation model arising from an adaptive appearance model, an adaptive velocity motion model with adaptive noise variance, and an adaptive number of particles. The adaptive-velocity model is derived using a first-order linear predictor based on the appearance difference between the incoming observation and the previous particle configuration. Occlusion analysis is implemented using robust statistics. Experimental results on tracking visual objects in long outdoor and indoor video sequences demonstrate the effectiveness and robustness of our tracking algorithm. We then perform simultaneous tracking and recognition by embedding them in a particle filter. For recognition purposes, we model the appearance changes between frames and gallery images by constructing the intra- and extrapersonal spaces. Accurate recognition is achieved when confronted by pose and view variations.},
	keywords = {adaptive filters, adaptive noise variance, algorithms, appearance-adaptive model, Artificial intelligence, Cluster Analysis, Computer Graphics, Computer simulation, Feedback, Filtering, first-order linear predictor, hidden feature removal, HUMANS, Image Enhancement, Image Interpretation, Computer-Assisted, image recognition, Information Storage and Retrieval, Kinematics, Laboratories, Male, Models, Biological, Models, Statistical, MOTION, Movement, Noise robustness, Numerical Analysis, Computer-Assisted, occlusion analysis, Particle filters, Particle tracking, Pattern Recognition, Automated, Predictive models, Reproducibility of results, robust statistics, Sensitivity and Specificity, Signal Processing, Computer-Assisted, State estimation, statistical analysis, Subtraction Technique, tracking, Training data, visual recognition, visual tracking},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2004.836152},
	author = {Zhou,Shaohua Kevin and Chellapa, Rama and Moghaddam, B.}
}
