@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 {12103,
	title = {Security for virtual private intranets},
	journal = {Computer},
	volume = {31},
	year = {1998},
	month = {1998/09//},
	pages = {48 - 55},
	abstract = {As telecommuting grows, businesses must consider security when extending their network environment to employees{\textquoteright} homes. Researchers at the University of Pennsylvania have addressed the problem with smart cards, operating system modifications, and network authentication. We note the distinction between trust and integrity: trust is determined through the verification of components and the dependencies among them, while integrity demonstrates that components haven{\textquoteright}t been modified. Thus integrity checking in a trustworthy system is about preserving an established trust or trust relationship. Our solution to the challenge of isolating functional roles that may share a single hardware platform is called secure identity based lending (SIBL). SIBL provides multiple personalities by partitioning the hard drive into n+1 partitions, where n is the number of supported personalities. All personalities use the system partition for core operating system components and shared applications. Each of the personalities is also associated with one of the remaining partitions, which are encrypted using a symmetric algorithm},
	keywords = {businesses, Clouds, Companies, core operating system components, cryptography, Data security, employee homes, encryption, functional roles, hard drive, Home computing, home working, integrity checking, Internet, Local area networks, multiple personalities, network authentication, network environment, operating system modifications, Operating systems, Roads, secure identity based lending, security management, security of data, shared applications, SIBL, single hardware platform, smart cards, symmetric algorithm, system partition, telecommuting, Teleworking, trust relationship, trustworthy system, virtual private intranets},
	isbn = {0018-9162},
	doi = {10.1109/2.708450},
	author = {Arbaugh, William A. and Davin,J. R and Farber,D. J and Smith,J. M}
}
