@conference {18207,
	title = {Efficient bandwidth resource allocation for low-delay multiuser MPEG-4 video transmission},
	booktitle = {Communications, 2004 IEEE International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/06//},
	pages = {1308 - 1312 Vol.3 - 1308 - 1312 Vol.3},
	abstract = {An efficient bandwidth resource allocation algorithm with low delay and low fluctuation of quality to transmit multiple MPEG-4 fine granularity scalability (FGS) video programs to multiple users is proposed in this paper. By exploring the variation in the scene complexity of each video program and jointly redistributing available system resources among users, our proposed algorithm provides low fluctuation of quality for each user and consistent quality among all users. Experimental results show that compared to a traditional look-ahead sliding-window approach, our scheme can achieve comparable perceptual quality and channel utilization at a much lower cost of delay, computation, and storage.},
	keywords = {algorithm;, allocation, allocation;, approach;, Bandwidth, broadband, channel, channels;, coding;, delays;, expert, FGS;, fine, granularity, group;, look-ahead, moving, MPEG-4, multiuser, networks;, of, picture, program;, QUALITY, resource, scalability;, service;, sliding-window, transmission;, utilization;, video},
	doi = {10.1109/ICC.2004.1312724},
	author = {Su,Guan-Ming and M. Wu}
}
@conference {14836,
	title = {Optimal matching of planar models in 3D scenes},
	booktitle = {Computer Vision and Pattern Recognition, 1991. Proceedings CVPR {\textquoteright}91., IEEE Computer Society Conference on},
	year = {1991},
	month = {1991/06//},
	pages = {269 - 274},
	abstract = {The problem of matching a model consisting of the point features of a flat object to point features found in an image that contains the object in an arbitrary three-dimensional pose is addressed. Once three points are matched, it is possible to determine the pose of the object. Assuming bounded sensing error, the author presents a solution to the problem of determining the range of possible locations in the image at which any additional model points may appear. This solution leads to an algorithm for determining the largest possible matching between image and model features that includes this initial hypothesis. The author implements a close approximation to this algorithm, which is <e1>O</e1>( <e1>nm</e1> isin;<sup>6</sup>), where <e1>n</e1> is the number of image points, <e1>m</e1> is the number of model points, and isin; is the maximum sensing error. This algorithm is compared to existing methods, and it is shown that it produces more accurate results},
	keywords = {3D, approximation;flat, error;close, error;model, features;computerised, features;optimal, matching;planar, models;point, object;image;maximum, pattern, picture, processing;, recognition;computerised, scenes;bounded, sensing},
	doi = {10.1109/CVPR.1991.139700},
	author = {Jacobs, David W.}
}
@article {14888,
	title = {Space and time bounds on indexing 3D models from 2D images},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {13},
	year = {1991},
	month = {1991/10//},
	pages = {1007 - 1017},
	abstract = {Model-based visual recognition systems often match groups of image features to groups of model features to form initial hypotheses, which are then verified. In order to accelerate recognition considerably, the model groups can be arranged in an index space (hashed) offline such that feasible matches are found by indexing into this space. For the case of 2D images and 3D models consisting of point features, bounds on the space required for indexing and on the speedup that such indexing can achieve are demonstrated. It is proved that, even in the absence of image error, each model must be represented by a 2D surface in the index space. This places an unexpected lower bound on the space required to implement indexing and proves that no quantity is invariant for all projections of a model into the image. Theoretical bounds on the speedup achieved by indexing in the presence of image error are also determined, and an implementation of indexing for measuring this speedup empirically is presented. It is found that indexing can produce only a minimal speedup on its own. However, when accompanied by a grouping operation, indexing can provide significant speedups that grow exponentially with the number of features in the groups},
	keywords = {2D, bounds;time, bounds;visual, extraction;grouping, features;model, features;model-based, images;3D, indexing;feature, model, operation;image, pattern, picture, processing;, recognition, recognition;computerised, recognition;space, systems;computerised},
	isbn = {0162-8828},
	doi = {10.1109/34.99235},
	author = {Clemens,D. T and Jacobs, David W.}
}
