@conference {13766,
	title = {Evaluating visual and statistical exploration of scientific literature networks},
	booktitle = {2011 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)},
	year = {2011},
	month = {2011/09/18/22},
	pages = {217 - 224},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Action Science Explorer (ASE) is a tool designed to support users in rapidly generating readily consumable summaries of academic literature. It uses citation network visualization, ranking and filtering papers by network statistics, and automatic clustering and summarization techniques. We describe how early formative evaluations of ASE led to a mature system evaluation, consisting of an in-depth empirical evaluation with four domain experts. The evaluation tasks were of two types: predefined tasks to test system performance in common scenarios, and user-defined tasks to test the system{\textquoteright}s usefulness for custom exploration goals. The primary contribution of this paper is a validation of the ASE design and recommendations to provide: easy-to-understand metrics for ranking and filtering documents, user control over which document sets to explore, and overviews of the document set in coordinated views along with details-on-demand of specific papers. We contribute a taxonomy of features for literature search and exploration tools and describe exploration goals identified by our participants.},
	keywords = {abstracting, academic literature, action science explorer, automatic clustering, citation analysis, citation network visualization, Communities, Context, custom exploration goal, Data visualization, Databases, Document filtering, document handling, document ranking, easy-to-understand metrics, empirical evaluation, Google, Graphical user interfaces, Information filtering, Information Visualization, Libraries, literature exploration, network statistics, paper filtering, paper ranking, scientific literature network, statistical exploration, summarization technique, user-defined tasks, visual exploration, Visualization},
	isbn = {978-1-4577-1246-3},
	doi = {10.1109/VLHCC.2011.6070403},
	author = {Gove,R. and Dunne,C. and Shneiderman, Ben and Klavans,J. and Dorr, Bonnie J}
}
@article {12493,
	title = {A Learning Approach Towards Detection and Tracking of Lane Markings},
	journal = {IEEE Transactions on Intelligent Transportation Systems},
	volume = {PP},
	year = {2010},
	month = {2010/02/17/},
	pages = {1 - 12},
	abstract = {Road scene analysis is a challenging problem that has applications in autonomous navigation of vehicles. An integral component of this system is the robust detection and tracking of lane markings. It is a hard problem primarily due to large appearance variations in lane markings caused by factors such as occlusion (traffic on the road), shadows (from objects like trees), and changing lighting conditions of the scene (transition from day to night). In this paper, we address these issues through a learning-based approach using visual inputs from a camera mounted in front of a vehicle. We propose the following: 1) a pixel-hierarchy feature descriptor to model the contextual information shared by lane markings with the surrounding road region; 2) a robust boosting algorithm to select relevant contextual features for detecting lane markings; and 3) particle filters to track the lane markings, without knowledge of vehicle speed, by assuming the lane markings to be static through the video sequence and then learning the possible road scene variations from the statistics of tracked model parameters. We investigate the effectiveness of our algorithm on challenging daylight and night-time road video sequences.},
	keywords = {Boosting, Context, Context modeling, Feature extraction, lane marking detection, outlier robustness, Roads, tracking and learning, Training, Vehicles},
	isbn = {1524-9050},
	doi = {10.1109/TITS.2012.2184756},
	author = {Gopalan,R. and Hong, T. and Shneier, M. and Chellapa, Rama}
}
@article {12540,
	title = {Synthesis of Silhouettes and Visual Hull Reconstruction for Articulated Humans},
	journal = {Multimedia, IEEE Transactions on},
	volume = {10},
	year = {2008},
	month = {2008/12//},
	pages = {1565 - 1577},
	abstract = {In this paper, we propose a complete framework for improved synthesis and understanding of the human pose from a limited number of silhouette images. It combines the active image-based visual hull (IBVH) algorithm and a contour-based body part segmentation technique. We derive a simple, approximate algorithm to decide the extrinsic parameters of a virtual camera, and synthesize the turntable image collection of the person using the IBVH algorithm by actively moving the virtual camera on a properly computed circular trajectory around the person. Using the turning function distance as the silhouette similarity measurement, this approach can be used to generate the desired pose-normalized images for recognition applications. In order to overcome the inability of the visual hull (VH) method to reconstruct concave regions, we propose a contour-based human body part localization algorithm to segment the silhouette images into convex body parts. The body parts observed from the virtual view are generated separately from the corresponding body parts observed from the input views and then assembled together for a more accurate VH reconstruction. Furthermore, the obtained turntable image collection helps to improve the body part segmentation and identification process. By using the inner distance shape context (IDSC) measurement, we are able to estimate the body part locations more accurately from a synthesized view where we can localize the body part more precisely. Experiments show that the proposed algorithm can greatly improve body part segmentation and hence shape reconstruction results.},
	keywords = {active, algorithm;articulated, algorithm;inner, body, camera;visual, collection;virtual, computation;contour-based, Context, detection;image, distance, distance;turntable, estimation;shape, function, hull, human, image, image;approximate, localization, measurement;silhouette, measurement;turning, part, pose;circular, reality;, recognition;virtual, reconstruction;approximation, reconstruction;image, segmentation, segmentation;pose, SHAPE, similarity, synthesis;silhouette, technique;human, theory;cameras;edge, Trajectory},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2008.2007321},
	author = {Yue,Zhanfeng and Chellapa, Rama}
}
@conference {12347,
	title = {A Communication Interface for Multiprocessor Signal Processing Systems},
	booktitle = {Proceedings of the 2006 IEEE/ACM/IFIP Workshop on Embedded Systems for Real Time Multimedia},
	year = {2006},
	month = {2006/10//},
	pages = {127 - 132},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Parallelization of embedded software is often desirable for power/performance-related considerations for computation-intensive applications that frequently occur in the signal-processing domain. Although hardware support for parallel computation is increasingly available in embedded processing platforms, there is a distinct lack of effective software support. One of the most widely known efforts in support of parallel software is the message passing interface (MPI). However, MPI suffers from several drawbacks with regards to customization to specialized parallel processing contexts, and performance degradation for communication-intensive applications. In this paper, we propose a new interface, the signal passing interface (SPI), that is targeted toward signal processing applications and addresses the limitations of MPI for this important domain of embedded software by integrating relevant properties of MPI and coarse-grain dataflow modeling. SPI is much easier and more intuitive to use, and due to its careful specialization, more performance-efficient for the targeted application domain. We present our preliminary version of SPI, along with experiments using SPI on a practical face detection system that demonstrate the capabilities of SPI},
	keywords = {application program interfaces, Application software, coarse-grain dataflow modeling, Computer applications, Concurrent computing, Context, data flow graphs, Embedded computing, Embedded software, face detection system, Hardware, Message passing, message passing interface, MPI, Multiprocessing systems, multiprocessor signal processing system, PARALLEL PROCESSING, signal passing interface, Signal processing, SPI},
	isbn = {0-7803-9783-5},
	doi = {10.1109/ESTMED.2006.321285},
	author = {Sankalita Saha and Bhattacharyya, Shuvra S. and Wayne Wolf}
}
@conference {12797,
	title = {When is {\textquoteleft}partial{\textquoteright} adequate? A logic-based proof technique using partial specifications},
	booktitle = {Logic in Computer Science, 1990. LICS {\textquoteright}90, Proceedings., Fifth Annual IEEE Symposium on e},
	year = {1990},
	month = {1990/06/04/7},
	pages = {440 - 449},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A technique is presented for ascertaining when a (finite-state) partial process specification is adequate, in the sense of being specified enough, for contexts in which it is to be used. The method relies on the automatic generation of a modal formula from the partial specification; if the remainder of the network satisfies this formula, then any process that meets the specification is guaranteed to ensure correct behavior of the overall system. Using the results, the authors develop compositional proof rules for establishing the correctness of networks of parallel processes and illustrate their use with several examples},
	keywords = {Calculus, Carbon capture and storage, compositional proof rules, Computer science, Concurrent computing, Context, correctness, formal specification, logic-based proof technique, modal formula, parallel processes, partial process specification, partial specifications, specification adequacy, State-space methods, Technological innovation},
	isbn = {0-8186-2073-0},
	doi = {10.1109/LICS.1990.113768},
	author = {Cleaveland, Rance and Steffen,B.}
}
