@conference {15916,
	title = {A new multiresolution generalized directional filter bank design and application in image enhancement},
	booktitle = {15th IEEE International Conference on Image Processing, 2008. ICIP 2008},
	year = {2008},
	month = {2008/10//},
	pages = {2816 - 2819},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we present an image enhancement technique based on a new multiscale generalized directional filter bank design. The design presented is a shift-invariant overcomplete representation, which is well suited to extracting geometric features such as edges. Special cases of this design method can be made to reduce to different and improved implementations of the shearlet and the contourlet transforms, which are known to represent certain classes of images optimally. Use of this new filter bank design has proven itself competitive in image restoration for noisy images and is well suited for distinguishing noise from weak edges. Experimental results show that our unique image enhancement technique out-performs wavelet and contourlet based enhancement methods.},
	keywords = {Algorithm design and analysis, Approximation error, Channel bank filters, contourlet transform, Design methodology, Discrete transforms, Feature extraction, Filter bank, Frequency, geometric feature extraction, Image Enhancement, IMAGE PROCESSING, image resolution, image restoration, Multidimensional digital filters, Multidimensional systems, multiresolution generalized directional filter bank design, shearlet transform, shift-invariant overcomplete representation, transforms, Wavelet transforms},
	isbn = {978-1-4244-1765-0},
	doi = {10.1109/ICIP.2008.4712380},
	author = {Patel, Vishal M. and Easley,G. R and Healy,D. M}
}
@conference {17877,
	title = {DiST: fully decentralized indexing for querying distributed multidimensional datasets},
	booktitle = {Parallel and Distributed Processing Symposium, 2006. IPDPS 2006. 20th International},
	year = {2006},
	month = {2006/04//},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Grid computing and peer-to-peer (P2P) systems are emerging as new paradigms for managing large scale distributed resources across wide area networks. While grid computing focuses on managing heterogeneous resources and relies on centralized managers for resource and data discovery, P2P systems target scalable, decentralized methods for publishing and searching for data. In large distributed systems, a centralized resource manager is a potential performance bottleneck and decentralization can help avoid this bottleneck, as is done in P2P systems. However, the query functionality provided by most existing P2P systems is very rudimentary, and is not directly applicable to grid resource management. In this paper, we propose a fully decentralized multidimensional indexing structure, called DiST, that operates in a fully distributed environment with no centralized control. In DiST, each data server only acquires information about data on other servers from executing and routing queries. We describe the DiST algorithms for maintaining the decentralized network of data servers, including adding and deleting servers, the query routing algorithm, and failure recovery algorithms. We also evaluate the performance of the decentralized scheme against a more structured hierarchical indexing scheme that we have previously shown to perform well in distributed grid environments},
	keywords = {Computer network management, distributed multidimensional dataset querying, failure recovery, fault tolerant computing, fully decentralized multidimensional indexing, grid computing, Indexing, large scale distributed resource management, Large-scale systems, Multidimensional systems, Network servers, P2P systems, Peer to peer computing, peer-to-peer computing, peer-to-peer systems, Publishing, Query processing, query routing, resource allocation, Resource management, telecommunication network routing, wide area networks},
	isbn = {1-4244-0054-6},
	doi = {10.1109/IPDPS.2006.1639280},
	author = {Nam,Beomseok and Sussman, Alan}
}
@conference {17888,
	title = {Spatial indexing of distributed multidimensional datasets},
	booktitle = {IEEE International Symposium on Cluster Computing and the Grid, 2005. CCGrid 2005},
	volume = {2},
	year = {2005},
	month = {2005/05/09/12},
	pages = {743- 750 Vol. 2 - 743- 750 Vol. 2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {While declustering methods for distributed multidimensional indexing of large datasets have been researched widely in the past, replication techniques for multidimensional indexes have not been investigated deeply. In general, a centralized index server may become the performance bottleneck in a wide area network rather than the data servers, since the index is likely to be accessed more often than any of the datasets in the servers. In this paper, we present two different multidimensional indexing algorithms for a distributed environment - a centralized global index and a two-level hierarchical index. Our experimental results show that the centralized scheme does not scale well for either insertion or searching the index. In order to improve the scalability of the index server, we have employed a replication protocol for both the centralized and two-level index schemes that allows some inconsistency between replicas without affecting correctness. Our experiments show that the two-level hierarchical index scheme shows better scalability for both building and searching the index than the non-replicated centralized index, but replication can make the centralized index faster than the two-level hierarchical index for searching in some cases.},
	keywords = {centralized global index algorithm, centralized index server, Computer science, database indexing, distributed databases, distributed multidimensional dataset, Educational institutions, File servers, Indexing, Large-scale systems, Multidimensional systems, Network servers, replication protocol, replication techniques, scalability, Sensor systems, spatial data structures, spatial indexing, two-level hierarchical index algorithm, wide area networks},
	isbn = {0-7803-9074-1},
	doi = {10.1109/CCGRID.2005.1558637},
	author = {Nam,B. and Sussman, Alan}
}
@conference {15690,
	title = {Automatic video summarization for wireless and mobile environments},
	booktitle = {2004 IEEE International Conference on Communications},
	volume = {3},
	year = {2004},
	month = {2004/06/20/24},
	pages = {1532- 1536 Vol.3 - 1532- 1536 Vol.3},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we propose a novel video summarization technique using which we can automatically generate high quality video summaries suitable for wireless and mobile environments. The significant contribution of this paper lies in the proposed clustering scheme. We use Delaunay diagrams to cluster multidimensional point data corresponding to the frame contents of the video. In contrast to the existing clustering techniques used for summarization, our clustering algorithm is fully automatic and well suited for batch processing. We illustrate the quality of our clustering and summarization scheme in an experiment using several video clips.},
	keywords = {automatic video summarization, batch processing, batch processing (computers), Clustering algorithms, Clustering methods, clustering scheme, Computer science, Delaunay diagram, graph theory, Gunshot detection systems, Image sequences, mesh generation, Mobile computing, mobile radio, multidimensional point data cluster, Multidimensional systems, Multimedia communication, video clip, video frame content, Video sequences, video signal processing, wireless mobile environment},
	isbn = {0-7803-8533-0},
	doi = {10.1109/ICC.2004.1312767},
	author = {Yong Rao and Mundur, Padma and Yesha,Y.}
}
@conference {16944,
	title = {A Rank-by-Feature Framework for Unsupervised Multidimensional Data Exploration Using Low Dimensional Projections},
	booktitle = {IEEE Symposium on Information Visualization, 2004. INFOVIS 2004},
	year = {2004},
	month = {2004///},
	pages = {65 - 72},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Exploratory analysis of multidimensional data sets is challenging because of the difficulty in comprehending more than three dimensions. Two fundamental statistical principles for the exploratory analysis are (1) to examine each dimension first and then find relationships among dimensions, and (2) to try graphical displays first and then find numerical summaries (D.S. Moore, (1999). We implement these principles in a novel conceptual framework called the rank-by-feature framework. In the framework, users can choose a ranking criterion interesting to them and sort 1D or 2D axis-parallel projections according to the criterion. We introduce the rank-by-feature prism that is a color-coded lower-triangular matrix that guides users to desired features. Statistical graphs (histogram, boxplot, and scatterplot) and information visualization techniques (overview, coordination, and dynamic query) are combined to help users effectively traverse 1D and 2D axis-parallel projections, and finally to help them interactively find interesting features},
	keywords = {axis-parallel projections, boxplot, color-coded lower-triangular matrix, computational complexity, computational geometry, Computer displays, Computer science, Computer vision, Data analysis, data mining, data visualisation, Data visualization, Displays, dynamic query, Educational institutions, exploratory data analysis, feature detection, feature detection/selection, Feature extraction, feature selection, graph theory, graphical displays, histogram, Information Visualization, interactive systems, Laboratories, Multidimensional systems, Principal component analysis, rank-by-feature prism, scatterplot, statistical analysis, statistical graphics, statistical graphs, unsupervised multidimensional data exploration, very large databases},
	isbn = {0-7803-8779-3},
	doi = {10.1109/INFVIS.2004.3},
	author = {Seo,J. and Shneiderman, Ben}
}
@conference {17878,
	title = {Improving access to multi-dimensional self-describing scientific datasets},
	booktitle = {3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings. CCGrid 2003},
	year = {2003},
	month = {2003/05/12/15},
	pages = {172 - 179},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Applications that query into very large multidimensional datasets are becoming more common. Many self-describing scientific data file formats have also emerged, which have structural metadata to help navigate the multi-dimensional arrays that are stored in the files. The files may also contain application-specific semantic metadata. In this paper, we discuss efficient methods for performing searches for subsets of multi-dimensional data objects, using semantic information to build multidimensional indexes, and group data items into properly sized chunks to maximize disk I/O bandwidth. This work is the first step in the design and implementation of a generic indexing library that will work with various high-dimension scientific data file formats containing semantic information about the stored data. To validate the approach, we have implemented indexing structures for NASA remote sensing data stored in the HDF format with a specific schema (HDF-EOS), and show the performance improvements that are gained from indexing the datasets, compared to using the existing HDF library for accessing the data.},
	keywords = {Application software, application-specific semantic metadata, Bandwidth, Computer science, database indexing, disk I/O bandwidth, distributed databases, Educational institutions, Indexing, indexing structures, Libraries, meta data, Middleware, multidimensional arrays, multidimensional datasets, Multidimensional systems, NASA, NASA remote sensing data, Navigation, query formulation, self-describing scientific data file formats, structural metadata, very large databases},
	isbn = {0-7695-1919-9},
	doi = {10.1109/CCGRID.2003.1199366},
	author = {Nam,B. and Sussman, Alan}
}
