@article {12433,
	title = {A Blur-robust Descriptor with Applications to Face Recognition},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {PP},
	year = {2012},
	month = {2012/01/10/},
	pages = {1 - 1},
	abstract = {Understanding the effect of blur is an important problem in unconstrained visual analysis. We address this problem in the context of image-based recognition, by a fusion of image-formation models, and differential geometric tools. First, we discuss the space spanned by blurred versions of an image and then under certain assumptions, provide a differential geometric analysis of that space. More specifically, we create a subspace resulting from convolution of an image with a complete set of orthonormal basis functions of a pre-specified maximum size (that can represent an arbitrary blur kernel within that size), and show that the corresponding subspaces created from a clean image and its blurred versions are equal under the ideal case of zero noise, and some assumptions on the properties of blur kernels. We then study the practical utility of this subspace representation for the problem of direct recognition of blurred faces, by viewing the subspaces as points on the Grassmann manifold and present methods to perform recognition for cases where the blur is both homogenous and spatially varying. We empirically analyze the effect of noise, as well as the presence of other facial variations between the gallery and probe images, and provide comparisons with existing approaches on standard datasets.},
	keywords = {Blur, convolution, Face, face recognition, Grassmann manifold, Kernel, Manifolds, NOISE, PROBES},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2012.15},
	author = {Gopalan,R. and Taheri, S. and Turaga,P. and Chellapa, Rama}
}
@conference {19269,
	title = {Applying graphics processor acceleration in a software defined radio prototyping environment},
	booktitle = {2011 22nd IEEE International Symposium on Rapid System Prototyping (RSP)},
	year = {2011},
	month = {2011},
	pages = {67 - 73},
	abstract = {With higher bandwidth requirements and more complex protocols, software defined radio (SDR) has ever growing computational demands. SDR applications have different levels of parallelism that can be exploited on multicore platforms, but design and programming difficulties have inhibited the adoption of specialized multicore platforms like graphics processors (GPUs). In this work we propose a new design flow that augments a popular existing SDR development environment (GNU Radio), with a dataflow foundation and a stand-alone GPU accelerated library. The approach gives an SDR developer the ability to prototype a GPU accelerated application and explore its design space fast and effectively. We demonstrate this design flow on a standard SDR benchmark and show that deciding how to utilize a GPU can be non-trivial for even relatively simple applications.},
	keywords = {Acceleration, coprocessors, dataflow foundation, GNU radio, Graphics processing unit, graphics processor acceleration, Kernel, Libraries, multicore platforms, Multicore processing, PARALLEL PROCESSING, Pipelines, Protocols, software defined radio prototyping environment, software radio, stand-alone GPU accelerated library},
	author = {Plishker,W. and Zaki, G.F. and Bhattacharyya, Shuvra S. and Clancy, C. and Kuykendall, J.}
}
@conference {13095,
	title = {Combining multiple kernels for efficient image classification},
	booktitle = {Applications of Computer Vision (WACV), 2009 Workshop on},
	year = {2009},
	month = {2009/12//},
	pages = {1 - 8},
	abstract = {We investigate the problem of combining multiple feature channels for the purpose of efficient image classification. Discriminative kernel based methods, such as SVMs, have been shown to be quite effective for image classification. To use these methods with several feature channels, one needs to combine base kernels computed from them. Multiple kernel learning is an effective method for combining the base kernels. However, the cost of computing the kernel similarities of a test image with each of the support vectors for all feature channels is extremely high. We propose an alternate method, where training data instances are selected, using AdaBoost, for each of the base kernels. A composite decision function, which can be evaluated by computing kernel similarities with respect to only these chosen instances, is learnt. This method significantly reduces the number of kernel computations required during testing. Experimental results on the benchmark UCI datasets, as well as on a challenging painting dataset, are included to demonstrate the effectiveness of our method.},
	keywords = {(artificial, AdaBoost;base, channels;multiple, classification;kernel, classification;learning, decision, feature, function;discriminative, intelligence);support, Kernel, kernel;image, kernels;composite, learning;support, machine;image, machines;, similarity;multiple, vector},
	doi = {10.1109/WACV.2009.5403040},
	author = {Siddiquie,B. and Vitaladevuni,S.N. and Davis, Larry S.}
}
@conference {14664,
	title = {Automated detection of persistent kernel control-flow attacks},
	booktitle = {Proceedings of the 14th ACM conference on Computer and communications security},
	series = {CCS {\textquoteright}07},
	year = {2007},
	month = {2007///},
	pages = {103 - 115},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {This paper presents a new approach to dynamically monitoring operating system kernel integrity, based on a property called state-based control-flow integrity (SBCFI). Violations of SBCFI signal a persistent, unexpected modification of the kernel{\textquoteright}s control-flow graph. We performed a thorough analysis of 25 Linux rootkits and found that 24 (96\%) employ persistent control-flow modifications; an informal study of Windows rootkits yielded similar results. We have implemented SBCFI enforcement as part of the Xen and VMware virtual machine monitors. Our implementation detected all the control-flow modifying rootkits we could install, while imposing unnoticeable overhead for both a typical web server workload and CPU-intensive workloads when operating at 10 second intervals.},
	keywords = {CFI, integrity, Kernel, rootkit, virtualization},
	isbn = {978-1-59593-703-2},
	doi = {10.1145/1315245.1315260},
	url = {http://doi.acm.org/10.1145/1315245.1315260},
	author = {Petroni,Jr.,Nick L. and Hicks, Michael W.}
}
@conference {13138,
	title = {An Interactive Approach to Pose-Assisted and Appearance-based Segmentation of Humans},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {An interactive human segmentation approach is described. Given regions of interest provided by users, the approach iteratively estimates segmentation via a generalized EM algorithm. Specifically, it encodes both spatial and color information in a nonparametric kernel density estimator, and incorporates local MRF constraints and global pose inferences to propagate beliefs over image space iteratively to determine a coherent segmentation. This ensures the segmented humans resemble the shapes of human poses. Additionally, a layered occlusion model and a probabilistic occlusion reasoning method are proposed to handle segmentation of multiple humans in occlusion. The approach is tested on a wide variety of images containing single or multiple occluded humans, and the segmentation performance is evaluated quantitatively.},
	keywords = {algorithm;appearance-based, algorithm;hidden, approach;layered, density, EM, Estimation, estimation;, estimator;pose-assisted, feature, human, Kernel, mechanisms;pose, method;expectation-maximisation, model;nonparametric, occlusion, reasoning, removal;image, segmentation;inference, segmentation;interactive, segmentation;probabilistic},
	doi = {10.1109/ICCV.2007.4409123},
	author = {Zhe Lin and Davis, Larry S. and David Doermann and DeMenthon,D.}
}
@conference {13196,
	title = {Efficient mean-shift tracking via a new similarity measure},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {1},
	year = {2005},
	month = {2005/06//},
	pages = {176 - 183 vol. 1 - 176 - 183 vol. 1},
	abstract = {The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the model and target image. The most typically used similarity measures are the Bhattacharyya coefficient or the Kullback-Leibler divergence. In practice, these approaches face three difficulties. First, the spatial information of the target is lost when the color histogram is employed, which precludes the application of more elaborate motion models. Second, the classical similarity measures are not very discriminative. Third, the sample-based classical similarity measures require a calculation that is quadratic in the number of samples, making real-time performance difficult. To deal with these difficulties we propose a new, simple-to-compute and more discriminative similarity measure in spatial-feature spaces. The new similarity measure allows the mean shift algorithm to track more general motion models in an integrated way. To reduce the complexity of the computation to linear order we employ the recently proposed improved fast Gauss transform. This leads to a very efficient and robust nonparametric spatial-feature tracking algorithm. The algorithm is tested on several image sequences and shown to achieve robust and reliable frame-rate tracking.},
	keywords = {algorithm;, analysis;, Bhattacharyya, coefficient;, Color, colour, density, divergence;, estimates;, extraction;, fast, feature, frame-rate, Gauss, Gaussian, histograms;, image, Kernel, Kullback-Leibler, matching;, Mean-shift, measures;, nonparametric, processes;, sample-based, sequences;, similarity, spaces;, spatial-feature, tracking, tracking;, transform;},
	doi = {10.1109/CVPR.2005.139},
	author = {Yang,Changjiang and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {12700,
	title = {Intra-personal kernel space for face recognition},
	booktitle = {Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on},
	year = {2004},
	month = {2004/05//},
	pages = {235 - 240},
	abstract = {Intra-personal space modeling proposed by Moghaddam et al. has been successfully applied in face recognition. In their work the regular principal subspaces are derived from the intra-personal spacce using a principal componen analysis and embedded in a probabilistic formulation. In this paper, we derive the principal subspace from the intro-personal kernel space by developing a probabilistic analysis for kernel principal components for face recognition. We test this algorithm on a subset of the FERET database with illumination and facial expression variations. The recognition performance demonstrates its advantage over other traditional subspace approaches.},
	keywords = {analysis;, component, Expression, Face, facial, illumination, intra-personal, Kernel, lighting;, principal, probabilistic, probability;, recognition;, space;, variation;},
	doi = {10.1109/AFGR.2004.1301537},
	author = {Zhou,Shaohua and Chellapa, Rama and Moghaddam, B.}
}
@conference {13216,
	title = {Iterative figure-ground discrimination},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {67 - 70 Vol.1 - 67 - 70 Vol.1},
	abstract = {Figure-ground discrimination is an important problem in computer vision. Previous work usually assumes that the color distribution of the figure can be described by a low dimensional parametric model such as a mixture of Gaussians. However, such approach has difficulty selecting the number of mixture components and is sensitive to the initialization of the model parameters. In this paper, we employ non-parametric kernel estimation for color distributions of both the figure and background. We derive an iterative sampling-expectation (SE) algorithm for estimating the color, distribution and segmentation. There are several advantages of kernel-density estimation. First, it enables automatic selection of weights of different cues based on the bandwidth calculation from the image itself. Second, it does not require model parameter initialization and estimation. The experimental results on images of cluttered scenes demonstrate the effectiveness of the proposed algorithm.},
	keywords = {algorithm;, analysis;, Bandwidth, calculation;, Color, colour, Computer, density, dimensional, discrimination;, distribution;, distributions;, Estimation, estimation;, expectation, figure, Gaussian, ground, image, initialization;, iterative, Kernel, low, methods;, mixture;, model, model;, nonparametric, parameter, parametric, processes;, sampling, sampling;, segmentation, segmentation;, statistics;, theory;, vision;},
	doi = {10.1109/ICPR.2004.1334006},
	author = {Zhao, L. and Davis, Larry S.}
}
@article {14768,
	title = {Resource policing to support fine-grain cycle stealing in networks of workstations},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	volume = {15},
	year = {2004},
	month = {2004/10//},
	pages = {878 - 892},
	abstract = {We present the design, implementation, and performance evaluation of a suite of resource policing mechanisms that allow guest processes to efficiently and unobtrusively exploit otherwise idle workstation resources. Unlike traditional policies that harvest cycles only from unused machines, we employ fine-grained cycle stealing to exploit resources even from machines that have active users. We developed a suite of kernel extensions that enable these policies to operate without significantly impacting host processes: 1) a new starvation-level CPU priority for guest jobs, 2) a new page replacement policy that imposes hard bounds on physical memory usage by guest processes, and 3) a new I/O scheduling mechanism called rate windows that throttle guest processes{\textquoteright} usage of I/O and network bandwidth. We evaluate both the individual impacts of each mechanism, and their utility for our fine-grain cycle stealing.},
	keywords = {65, Application software, Bandwidth, cluster computing, Computer networks, Computer Society, Concurrent computing, cycle stealing, cycle stealing., grid computing, I/O scheduling, Intelligent networks, Kernel, network bandwidth, networks of workstations, page replacement policy, parallel computing, performance evaluation, Processor scheduling, resource allocation, resource scheduling, starvation-level CPU priority, workstation clusters, workstation resources, Workstations},
	isbn = {1045-9219},
	doi = {10.1109/TPDS.2004.58},
	author = {Ryu, K. D and Hollingsworth, Jeffrey K}
}
@article {13235,
	title = {Efficient kernel density estimation using the fast gauss transform with applications to color modeling and tracking},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {25},
	year = {2003},
	month = {2003/11//},
	pages = {1499 - 1504},
	abstract = {Many vision algorithms depend on the estimation of a probability density function from observations. Kernel density estimation techniques are quite general and powerful methods for this problem, but have a significant disadvantage in that they are computationally intensive. In this paper, we explore the use of kernel density estimation with the fast Gauss transform (FGT) for problems in vision. The FGT allows the summation of a mixture of ill Gaussians at N evaluation points in O(M+N) time, as opposed to O(MN) time for a naive evaluation and can be used to considerably speed up kernel density estimation. We present applications of the technique to problems from image segmentation and tracking and show that the algorithm allows application of advanced statistical techniques to solve practical vision problems in real-time with today{\textquoteright}s computers.},
	keywords = {algorithms;, Color, Computer, density, estimation;, fast, function;, Gauss, image, Kernel, modeling;, segmentation;, tracking;, transform;, transforms;, VISION, vision;},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2003.1240123},
	author = {Elgammal,A. and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {11896,
	title = {Kernel snakes: non-parametric active contour models},
	booktitle = {IEEE International Conference on Systems, Man and Cybernetics, 2003},
	volume = {1},
	year = {2003},
	month = {2003/10//},
	pages = {240- 244 vol.1 - 240- 244 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, a new non-parametric generalized formulation to statistical pressure snakes is presented. We discuss the shortcomings of the traditional pressure snakes. We then introduce a new generic pressure model that alleviates these shortcomings, based on the Bayesian decision theory. Non-parametric techniques are used to obtain the statistical models that drive the snake. We discuss the advantages of using the proposed non-parametric model compared to other parametric techniques. Multi-colored-target tracking is used to demonstrate the performance of the proposed approach. Experimental results show enhanced, real-time performance.},
	keywords = {Active contours, Artificial intelligence, Bayes methods, Bayesian decision theory, Bayesian methods, decision theory, Deformable models, Image edge detection, Image segmentation, Intelligent robots, Kernel, kernel snakes, Laboratories, multicolored target tracking, nonparametric active contour models, nonparametric generalized formulation, nonparametric model, nonparametric statistics, nonparametric techniques, real time performance, Robot vision systems, statistical pressure snakes, target tracking},
	isbn = {0-7803-7952-7},
	doi = {10.1109/ICSMC.2003.1243822},
	author = {Abd-Almageed, Wael and Smith,C.E. and Ramadan,S.}
}
@conference {12763,
	title = {An experimental evaluation of linear and kernel-based methods for face recognition},
	booktitle = {Applications of Computer Vision, 2002. (WACV 2002). Proceedings. Sixth IEEE Workshop on},
	year = {2002},
	month = {2002///},
	pages = {13 - 18},
	abstract = {In this paper we present the results of a comparative study of linear and kernel-based methods for face recognition. The methods used for dimensionality reduction are Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Linear Discriminant Analysis (LDA) and Kernel Discriminant Analysis (KDA). The methods used for classification are Nearest Neighbor (NN) and Support Vector Machine (SVM). In addition, these classification methods are applied on raw images to gauge the performance of these dimensionality reduction techniques. All experiments have been performed on images from UMIST Face Database.},
	keywords = {analysis;, classification;, component, discriminant, Face, image, Kernel, linear, Machine;, nearest, neighbor;, principal, recognition;, Support, vector},
	doi = {10.1109/ACV.2002.1182137},
	author = {Gupta, H. and Agrawala, Ashok K. and Pruthi, T. and Shekhar, C. and Chellapa, Rama}
}
@conference {17532,
	title = {Receiver based management of low bandwidth access links},
	booktitle = {IEEE INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings},
	volume = {1},
	year = {2000},
	month = {2000///},
	pages = {245-254 vol.1 - 245-254 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we describe a receiver-based congestion control policy that leverages TCP flow control mechanisms to prioritize mixed traffic loads across access links. We manage queueing at the access link to: (1) improve the response time of interactive network applications; (2) reduce congestion-related packet losses; while (3) maintaining high throughput for bulk-transfer applications. Our policy controls queue length by manipulating receive socket buffer sizes. We have implemented this solution in a dynamically loadable Linux kernel module, and tested it over low-bandwidth links. Our approach yields a 7-fold improvement in packet latency over an unmodified system while maintaining 94\% link utilization. In the common case, congestion-related packet losses at the access link can be eliminated. Finally, by prioritizing short flows, we show that our system reduces the time to download a complex Web page during a large background transfer by a factor of two},
	keywords = {Bandwidth, buffer storage, bulk-transfer applications, complex Web page, congestion control policy, Delay, dynamically loadable Linux kernel module, information resources, interactive network, Internet, Kernel, link utilization, Linux, low-bandwidth access links, mixed traffic load, packet latency, queue length, queueing theory, receive socket buffer sizes, receiver-based management, response time, short flow prioritizing, Size control, Sockets, subscriber loops, TCP flow control, telecommunication congestion control, telecommunication network management, Telecommunication traffic, Testing, Throughput, Transport protocols, Unix, Web pages},
	isbn = {0-7803-5880-5},
	doi = {10.1109/INFCOM.2000.832194},
	author = {Spring, Neil and Chesire,M. and Berryman,M. and Sahasranaman,V. and Anderson,T. and Bershad,B.}
}
@article {12089,
	title = {Secure quality of service handling: SQoSH},
	journal = {IEEE Communications Magazine},
	volume = {38},
	year = {2000},
	month = {2000/04//},
	pages = {106 - 112},
	abstract = {Proposals for programmable network infrastructures, such as active networks and open signaling, provide programmers with access to network resources and data structures. The motivation for providing these interfaces is accelerated introduction of new services, but exposure of the interfaces introduces many new security risks. We describe some of the security issues raised by active networks. We then describe our secure active network environment (SANE) architecture. SANE was designed as a security infrastructure for active networks, and was implemented in the SwitchWare architecture. SANE restricts the actions that loaded modules can perform by restricting the resources that can be named; this is further extended to remote invocation by means of cryptographic credentials. SANE can be extended to support restricted control of quality of service in a programmable network element. The Piglet lightweight device kernel provides a {\textquotedblleft}virtual clock{\textquotedblright} type of scheduling discipline for network traffic, and exports several tuning knobs with which the clock can be adjusted. The ALIEN active loader provides safe access to these knobs to modules that operate on the network element. Thus, the proposed SQoSH architecture is able to provide safe, secure access to network resources, while allowing these resources to be managed by end users needing customized networking services. A desirable consequence of SQoSH{\textquoteright}s integration of access control and resource control is that a large class of denial-of-service attacks, unaddressed solely with access control and cryptographic protocols, can now be prevented},
	keywords = {Acceleration, Access control, active networks, ALIEN active loader, Clocks, Computer network management, cryptographic credentials, cryptography, customized networking services, Data security, Data structures, denial-of-service attacks, interfaces, Kernel, loaded modules, network resources, network traffic, open signaling, packet switching, Piglet lightweight device kernel, programmable network element, programmable network infrastructures, Programming profession, Proposals, quality of service, remote invocation, resource control, restricted control of quality of service, SANE, scheduling, scheduling discipline, secure active network environment architecture, secure quality of service handling, security infrastructure, security risks, SQoSH, SwitchWare architecture, telecommunication security, tuning knobs, virtual clock},
	isbn = {0163-6804},
	doi = {10.1109/35.833566},
	author = {Alexander,D. S and Arbaugh, William A. and Keromytis,A. D and Muir,S. and Smith,J. M}
}
