@article {14430,
	title = {Dynamic Processing Allocation in Video},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {33},
	year = {2011},
	month = {2011/11//},
	pages = {2174 - 2187},
	abstract = {Large stores of digital video pose severe computational challenges to existing video analysis algorithms. In applying these algorithms, users must often trade off processing speed for accuracy, as many sophisticated and effective algorithms require large computational resources that make it impractical to apply them throughout long videos. One can save considerable effort by applying these expensive algorithms sparingly, directing their application using the results of more limited processing. We show how to do this for retrospective video analysis by modeling a video using a chain graphical model and performing inference both to analyze the video and to direct processing. We apply our method to problems in background subtraction and face detection, and show in experiments that this leads to significant improvements over baseline algorithms.},
	keywords = {algorithms;digital, allocation;video, analysis;computer, background, detection;graphical, detection;resource, graphics;face, model;resource, processing;, processing;face, recognition;object, signal, subtraction;baseline, video},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2011.55},
	author = {Daozheng Chen and Bilgic,M. and Getoor, Lise and Jacobs, David W.}
}
@conference {17911,
	title = {Salient Clustering for View-dependent Multiresolution Rendering},
	booktitle = {Computer Graphics and Image Processing (SIBGRAPI), 2009 XXII Brazilian Symposium on},
	year = {2009},
	month = {2009/10//},
	pages = {56 - 63},
	abstract = {Perceptual information is quickly gaining importance in mesh representation, analysis and rendering. User studies, eye tracking and other techniques are able to provide ever more useful insights for many user-centric systems, which form the bulk of computer graphics applications. In this work we build upon the concept of Mesh Saliency - an automatic measure of visual importance for triangle meshes based on models of low-level human visual attention - applying it to the problem of mesh segmentation and view-dependent rendering. We introduce a technique for segmentation that partitions an object into a set of face clusters, each encompassing a group of locally interesting features; Mesh Saliency is incorporated in a propagative mesh clustering framework, guiding cluster seed selection and triangle propagation costs and leading to a convergence of face clusters around perceptually important features. We compare our technique with different fully automatic segmentation algorithms, showing that it provides similar or better segmentation without the need for user input. We illustrate application of our clustering results through a saliency-guided view-dependent rendering system, achieving significant frame rate increases with little loss of visual detail.},
	keywords = {(computer, algorithms;cluster, analysis;mesh, attention;mesh, AUTOMATIC, centred, clustering, clustering;rendering, clustering;user-centric, clusters;low-level, dependent, design;, framework;salient, graphics);user, graphics;face, human, information;propagative, mesh, multiresolution, rendering;image, representation;mesh, resolution;image, saliency;mesh, seed, segmentation, segmentation;pattern, segmentation;perceptual, selection;computer, system;view, visual},
	doi = {10.1109/SIBGRAPI.2009.34},
	author = {Barni,R. and Comba,J. and Varshney, Amitabh}
}
