@conference {19636,
	title = {Geometrical Characteristics of Regular Polyhedra: Application to EXAFS Studies of Nanoclusters},
	booktitle = {X-RAY ABSORPTION FINE STRUCTURE - XAFS13: 13th International Conference},
	volume = {882},
	year = {2007},
	month = {2007/02/02/},
	pages = {746 - 748},
	publisher = {AIP Publishing},
	organization = {AIP Publishing},
	abstract = {EXAFS data modeling of nanoparticles can be done at a very high level of detail if regular polyhedral geometries are considered as possible structural models. Such geometries can be characterized by unique sequence of indices (coordination numbers) of the first few coordination shells that serve as a signature of a shape, size, morphology and packing model of atoms in the cluster. We wrote a suite of programs for cluster geometry generation and its pair radial distribution function analysis. The programs generate x,y,z coordinates and calculate the coordination numbers of the corresponding clusters for any order of a large number of regular polyhedra. We present the results for the first few nearest neighboring shells in the icosahedral, cuboctahedral, hcp structures for the small, 10{\textendash}1000 atom clusters.},
	keywords = {Atomic and molecular clusters, Cluster Analysis, Cluster geometric structure, Extended X-ray absorption fine structure spectroscopy, nanoparticles, Numerical modeling, Particle distribution functions, Polyhedra, Signal generators},
	url = {http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.2644651},
	author = {Dana Dachman-Soled and Frenkel, Anatoly I.}
}
@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 {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.}
}
@article {15594,
	title = {A Fast Implementation of the ISOCLUS Algorithm},
	journal = {IEEE 2003 International Geoscience and Remote Sensing Symposium},
	year = {2003},
	month = {2003///},
	abstract = {Unsupervised clustering is a fundamental building block in numerous image processing applications. One of the most popular and widely used clustering schemes for remote sensing applications is the ISOCLUS algorithm, which is based on the ISODATA method. The algorithm is given a set of n data points in d-dimensional space, an integer k indicating the initial number of clusters, and a number of additional parameters. The general goal is to compute the coordinates of a set of cluster centers in d-space, such that those centers minimize the mean squared distance from each data point to its nearest center. This clustering algorithm is similar to another well-known clustering method, called k-means. One significant feature of ISOCLUS over k-means is that the actual number of clusters reported might be fewer or more than the number supplied as part of the input. The algorithm uses different heuristics to determine whether to merge lor split clusters. As ISOCLUS can run very slowly, particularly on large data sets, there has been a growing .interest in the remote sensing community in computing it efficiently. We have developed a faster implementation of the ISOCLUS algorithm. Our improvement is based on a recent acceleration to the k-means algorithm of Kanungo, et al. They showed that, by using a kd-tree data structure for storing the data, it is possible to reduce the running time of k-means. We have adapted this method for the ISOCLUS algorithm, and we show that it is possible to achieve essentially the same results as ISOCLUS on large data sets, but with significantly lower running times. This adaptation involves computing a number of cluster statistics that are needed for ISOCLUS but not for k-means. Both the k-means and ISOCLUS algorithms are based on iterative schemes, in which nearest neighbors are calculated until some convergence criterion is satisfied. Each iteration requires that the nearest center for each data point be computed. Naively, this requires O(kn) time, where k denotes the current number of centers. Traditional techniques for accelerating nearest neighbor searching involve storing the k centers in a data structure. However, because of the iterative nature of the algorithm, this data structure would need to be rebuilt with each new iteration. Our approach is to store the data points in a kd-tree data structure. The assignment of points to nearest neighbors is carried out by a filtering process, which successively eliminates centers that can not possibly be the nearest neighbor for a given region of space. This algorithm is significantly faster, because large groups of data points can be assigned to their nearest center in a single operation. Preliminary results on a number of real Landsat datasets show that our revised ISOCLUS-like scheme runs about twice as fast.},
	keywords = {algorithms, Cluster Analysis, Data structures, HEURISTIC METHODS, IMAGE PROCESSING, INTEGERS, LANDSAT SATELLITES, MEAN SQUARE VALUES, Remote sensing},
	url = {http://ntrs.nasa.gov/search.jsp?R=20030053175},
	author = {Memarsadeghi,Nargess and Mount, Dave and Netanyahu,Nathan S. and LeMoigne,Jacqueline}
}
