@conference {18276,
	title = {Secure video processing: Problems and challenges},
	booktitle = {Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on},
	year = {2011},
	month = {2011/05//},
	pages = {5856 - 5859},
	abstract = {Secure signal processing is an emerging technology to enable signal processing tasks in a secure and privacy-preserving fashion. It has attracted a great amount of research attention due to the increasing demand to enable rich functionalities for private data stored online. Desirable functionalities may include search, analysis, clustering, etc. In this paper, we discuss the research issues and challenges in secure video processing with focus on the application of secure online video management. Video is different from text due to its large data volume and rich content diversity. To be practical, secure video processing requires efficient solutions that may involve a trade-off between security and complexity. We look at three representative video processing tasks and review existing techniques that can be applied. Many of the tasks do not have efficient solutions yet, and we discuss the challenges and research questions that need to be addressed.},
	keywords = {data;video, fashion;secure, management;secure, of, online, privacy-preserving, processing;, processing;security, signal, video},
	doi = {10.1109/ICASSP.2011.5947693},
	author = {Lu,Wenjun and Varna,A. and M. Wu}
}
@article {18549,
	title = {Autonomous traffic engineering with self-configuring topologies},
	journal = {SIGCOMM Comput. Commun. Rev.},
	volume = {41},
	year = {2010},
	month = {2010/08//},
	pages = {{\textendash} - {\textendash}},
	abstract = {Network operators use traffic engineering (TE) to control the flow of traffic across their networks. Existing TE methods require manual configuration of link weights or tunnels, which is difficult to get right, or prior knowledge of traffic demands and hence may not be robust to link failures or traffic fluctuations. We present a self-configuring TE scheme, SculpTE, which automatically adapts the network-layer topology to changing traffic demands. SculpTE is responsive, stable, and achieves excellent load balancing.},
	keywords = {multi-path routing, online, sculpte, self-configuring, traffic engineering},
	isbn = {0146-4833},
	url = {http://dl.acm.org/citation.cfm?id=2043164.1851239},
	author = {Sundaresan,Srikanth and Lumezanu,Cristian and Feamster, Nick and Francois,Pierre}
}
@conference {13214,
	title = {Object tracking by adaptive feature extraction},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/10//},
	pages = {1501 - 1504 Vol. 3 - 1501 - 1504 Vol. 3},
	abstract = {Tracking objects in the high-dimensional feature space is not only computationally expensive but also functionally inefficient. Selecting a low-dimensional discriminative feature set is a critical step to improve tracker performance. A good feature set for tracking can differ from frame to frame due to the changes in the background against the tracked object, and due to an on-line algorithm that adaptively determines a advantageous distinctive feature set. In this paper, multiple heterogeneous features are assembled, and likelihood images are constructed for various subspaces of the combined feature space. Then, the most discriminative feature is extracted by principal component analysis (PCA) based on those likelihood images. This idea is applied to the mean-shift tracking algorithm [D. Comaniciu et al., June 2000], and we demonstrate its effectiveness through various experiments.},
	keywords = {adaptive, algorithm;, analysis;, colour, component, extraction;, feature, feature;, heterogeneous, image, image;, likelihood, Mean-shift, object, online, principal, tracking, tracking;},
	doi = {10.1109/ICIP.2004.1421349},
	author = {Han,Bohyung and Davis, Larry S.}
}
@conference {13660,
	title = {Temporal clues in handwriting},
	booktitle = {Pattern Recognition, 1992. Vol.II. Conference B: Pattern Recognition Methodology and Systems, Proceedings., 11th IAPR International Conference on},
	year = {1992},
	month = {1992/09/03/aug},
	pages = {317 - 320},
	abstract = {Handwritten character recognition is typically classified as online or offline depending on the nature of the input data. Online data consists of a temporal sequence of instrument positions while offline data is in the form of a 2D image of the writing sample. Online recognition techniques have been relatively successful but have the disadvantage of requiring the data to be gathered during the writing process. This paper presents work on the extraction of temporal information from static images of handwriting and its implications for character recognition},
	keywords = {character, extraction;, feature, handwriting, information, offline, online, recognition;, techniques;, temporal},
	doi = {10.1109/ICPR.1992.201781},
	author = {David Doermann and Rosenfeld, A.}
}
