@book {16975,
	title = {Analyzing Social Media Networks with NodeXL},
	year = {2010},
	month = {2010/08/27/},
	publisher = {Morgan Kaufmann},
	organization = {Morgan Kaufmann},
	abstract = {"Analyzing Social Media Networks with NodeXL provides a much needed resource for the social media research community, as it describes network theory, provides compelling examples using data sources like Twitter and Flickr, and highlights how to use a free sophisticated tool for analysis. This is the perfect book for anyone trying to analyze the behavior of online social networks and beyond." ---Adam Perer, Research Scientist, IBM Research  "This book provides a basic introduction to social network analysis, followed by practical instruction and examples on gathering data from online sources, importing into Excel, and then analyzing the data through Excel. The book will be important for promoting research in the area for those in information science, sociology, cultural studies, virtual community, and e-commerce."---Caroline Haythornthwaite, PhD, Professor, Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign  Businesses, entrepreneurs, individuals, and government agencies alike are looking to social network analysis (SNA) tools for insight into trends, connections, and fluctuations in social media. Microsoft{\textquoteright}s NodeXL is a free, open-source SNA plug-in for use with Excel. It provides instant graphical representation of relationships of complex networked data. But it goes further than other SNA tools - NodeXL was developed by a multidisciplinary team of experts that bring together information studies, computer science, sociology, human-computer interaction, and over 20 years of visual analytic theory and information visualization into a simple tool anyone can use. This makes NodeXL of interest not only to end-users but also to researchers and students studying visual and network analytics and their application in the real world.},
	keywords = {Business \& Economics / Marketing / Research, Computers / Computer Science, Computers / Data Processing, Computers / Database Management / Data Mining, Computers / General, Computers / Information Theory, Computers / Interactive \& Multimedia, Computers / Social Aspects / General, Computers / Social Aspects / Human-Computer Interaction, Computers / User Interfaces, data mining, Data mining - Computer programs, Data mining/ Computer programs, Information Visualization, Information visualization - Computer programs, Information visualization/ Computer programs, NodeXL, online social networks, Social Science / General, Social Science / Reference, Social Science / Sociology / General, Technology \& Engineering / General, Technology \& Engineering / Social Aspects, Webometrics, Webometrics - Computer programs, Webometrics/ Computer programs},
	isbn = {9780123822291},
	author = {Hansen,Derek and Shneiderman, Ben and Smith,Marc A.}
}
@book {12505,
	title = {Statistical Methods and Models for Video-Based Tracking, Modeling, and Recognition},
	year = {2010},
	month = {2010///},
	publisher = {Now Publishers Inc},
	organization = {Now Publishers Inc},
	abstract = {Computer vision systems attempt to understand a scene and its components from mostly visual information. The geometry exhibited by the real world, the influence of material properties on scattering of incident light, and the process of imaging introduce constraints and properties that are key to solving some of these tasks. In the presence of noisy observations and other uncertainties, the algorithms make use of statistical methods for robust inference. Statistical Methods and Models for Video-based Tracking, Modeling, and Recognition highlights the role of geometric constraints in statistical estimation methods, and how the interplay of geometry and statistics leads to the choice and design of algorithms. In particular, it illustrates the role of imaging, illumination, and motion constraints in classical vision problems such as tracking, structure from motion, metrology, activity analysis and recognition, and appropriate statistical methods used in each of these problems.},
	keywords = {Computers / Computer Vision \& Pattern Recognition, Computers / Image Processing, Technology \& Engineering / Electronics / General, Technology \& Engineering / General},
	isbn = {9781601983145},
	author = {Chellapa, Rama and Sankaranarayanan,Aswin C. and Veeraraghavan,Ashok}
}
