@article {219, title = {Extended knowledge-based reasoning approach to spectrum sensing for cognitive radio}, journal = {IEEE Transactions on Mobile Computing}, volume = {9}, year = {2010}, pages = {465 - 478}, abstract = {

In this paper, a novel scheme for cognitive radio (CR) spectrum sensing in medium access control (MAC) layer, called as extended knowledge-based reasoning (EKBR), is proposed. The target of EKBR is to improve the fine sensing efficiency by jointly considering a number of network states and environmental statistics, including fast sensing results, short-term statistical information, channel quality, data transmission rate, and channel contention characteristics. This is for a better estimation on the optimal range of spectrum for fine sensing so as to adaptively reduce the overall channel sensing time. Performance analysis is conducted on the proposed EKBR scheme using a multidimensional absorbing Markov chain to evaluate various performance metrics of interest, such as average sensing delay (or referred to as sensing overhead in the study), average data transmission rate, and percentage of missed spectrum opportunities. Numerical results show that the proposed EKBR scheme achieves better performance than that by the state-or-the-art techniques while yielding less computation complexity and sensing overhead.

}, keywords = {access protocols, channel quality, cognitive radio, computation complexity, computational complexity, data transmission rate, environmental statistics, extended knowledge based reasoning, MAC layer, Markov chain, Markov processes, medium access control layer, network states, sensing overhead, short term statistical information, spectrum sensing, spread spectrum communication}, issn = {1536-1233}, doi = {http://dx.doi.org/10.1109/TMC.2009.148}, author = {X Wang and A Wong and P-H. Ho} } @inproceedings {263, title = {Texture classification using compressed sensing}, booktitle = {This paper presents a simple, novel, yet very powerful approach for texture classification based on compressed sensing and bag of words model, suitable for large texture database applications with images obtained under unknown viewpoint and illumination. }, year = {2010}, abstract = {

This paper presents a simple, novel, yet very powerful approach for texture classification based on compressed sensing and bag of words model, suitable for large texture database applications with images obtained under unknown viewpoint and illumination. At the feature extraction stage, a small set of random features are extracted from local image patches. The random features are embedded into the bag of words model to perform texture classification. Random feature extraction surpasses many conventional feature extraction methods, despite their careful design and complexity. We conduct extensive experiments on the CUReT database to evaluate the performance of the proposed approach. It is demonstrated that excellent performance can be achieved by the proposed approach using a small number of random features, as long as the dimension of the feature space is above certain threshold. Our approach is compared with recent state-of-the-art methods: the Patch method (Varma and Zisserman, TPAMI 09), the MR8 filter bank method (Varma and Zisserman, IJCV 05) and the LBP method (Ojala et al., TPAMI 02). It is shown that the proposed method significantly outperforms MR8 and LBP and is at least as good as the Patch method with drastic reduction in storage and computational complexity.

}, keywords = {bag of words model, band-pass filters, Compressed Sensing, computational complexity, Feature Extraction, image classification, image texture, LBP method, MR8 filter bank method, patch method, Texture classification, texture database applications}, doi = {http://dx.doi.org/10.1109/CRV.2010.16}, author = {L Liu and P Fieguth} } @inproceedings {370, title = {Toward an optimal solution for multitarget tracking}, booktitle = {2007 IEEE International Conference on Image Processing}, year = {2007}, abstract = {

There are ever increasing number of applications of multi-target tracking and considerable research has been conducted to solve this problem. Multi-target tracking is a NP-hard problem and almost all of the present multi-target tracking algorithms are sub-optimal by finding the solution in a reduced hypothesis space. In this paper we introduce a new approach toward finding the optimal single frame solution for general multi-target tracking problem. Our proposed method finds the optimal solution using linear programming optimization method. The proposed method has been successfully applied to synthetic and real data.

}, keywords = {computational complexity, joint probabilistic data association, linear programming, multitarget tracking, NP-hard problem, optimal solution, optimization method, probability, sensor fusion, target tracking}, doi = {http://dx.doi.org/10.1109/ICIP.2007.4379588}, author = {N Nezamoddin Kachouie and P Fieguth} } @article {418, title = {Statistical processing of large image sequences}, journal = {IEEE Transactions on Image Processing}, volume = {14}, year = {2005}, pages = {80 - 93}, abstract = {

The dynamic estimation of large-scale stochastic image sequences, as frequently encountered in remote sensing, is important in a variety of scientific applications. However, the size of such images makes conventional dynamic estimation methods, for example, the Kalman and related filters, impractical. We present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a 512 times; 512 image sequence of ocean surface temperature. The static estimation step, the primary contribution here, uses a mixture of stationary models to accurately mimic the effect of a nonstationary prior, simplifying both computational complexity and modeling. Our approach provides an efficient, stable, positive-definite model which is consistent with the given correlation structure. Thus, the methods of this paper may find application in modeling and single-frame estimation.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Biological, Cluster Analysis, computational complexity, Computer Graphics, Computer Simulation, Computer-Assisted, correlation methods, correlation structure, dynamic estimation method, image enhancement, image interpretation, image sequences, Imaging, Information Storage and Retrieval, Kalman filter, Kalman filters, large-scale stochastic image sequence estimation, Models, Movement, Numerical Analysis, ocean surface temperature, Pattern Recognition, Remote Sensing, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, single-frame estimation, static estimation step, Statistical, statistical analysis, statistical image processing, stochastic processes, Subtraction Technique, Three-Dimensional, Video Recording}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/TIP.2004.838703}, author = {F M. Khellah and P Fieguth and M J. Murray and M R. Allen} } @inproceedings {760, title = {Hierarchical annealing for scientific models}, booktitle = {37th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {2004}, address = {Montreal}, abstract = {

The computational complexity of simulated annealing makes it an impractical tool in many applications, particularly for complex, non-local models on very large 2D and 3D domains as desired in many scientific contexts. In particular, it is very difficult to produce large scale structure from a fine, pixellated lattice. Thus a hierarchical approach is intuitively attractive. However, existing approaches are few and limited. Motivated by a current problem in porous media, we develop a hierarchical approach to complex model sampling. In experiments, this approach results in 1-2 orders of magnitude computational gain, and significant gains in convergence as well.

}, keywords = {2D domains, 3D domains, complex model sampling, computational complexity, data visualisation, hierarchical annealing, hierarchical sampling, image resolution, image sampling, pixellated lattice, porous materials, porous media, sampled images, scientific models, simulated annealing}, doi = {http://dx.doi.org/10.1109/ICASSP.2004.1326474}, author = {S K. Alexander and P Fieguth and E Vrscay} } @article {524, title = {Multiply-rooted multiscale models for large-scale estimation}, journal = {IEEE Transactions on Image Processing}, volume = {10}, year = {2001}, pages = {1676 - 1686}, abstract = {

Divide-and-conquer or multiscale techniques have become popular for solving large statistical estimation problems. The methods rely on defining a state which conditionally decorrelates the large problem into multiple subproblems, each more straightforward than the original. However this step cannot be carried out for asymptotically large problems since the dimension of the state grows without bound, leading to problems of computational complexity and numerical stability. In this paper, we propose a new approach to hierarchical estimation in which the conditional decorrelation of arbitrarily large regions is avoided, and the problem is instead addressed piece-by-piece. The approach possesses promising attributes: it is not a local method-the estimate at every point is based on all measurements; it is numerically stable for problems of arbitrary size; and the approach retains the benefits of the multiscale framework on which it is based: a broad class of statistical models, a stochastic realization theory, an algorithm to calculate statistical likelihoods, and the ability to fuse local and nonlocal measurements

}, keywords = {asymptotically large problems, computational complexity, conditional decorrelation, estimation theory, geophysical signal processing, hierarchical estimation, image processing, large statistical estimation problems, large-scale estimation, local measurements fusion, multiply-rooted multiscale models, multiscale framework, nonlocal measurements fusion, numerical stability, oceanographic techniques, Remote Sensing, statistical analysis, statistical likelihoods, statistical models, stochastic realization theory}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/83.967396}, author = {P Fieguth} } @inproceedings {511, title = {Foveated multiscale models for large-scale estimation}, booktitle = {International Conference on Image Processing}, year = {1999}, abstract = {

Efficient, large-scale estimation methods such as nested dissection or multiscale estimation rely on a divide-and-conquer strategy, in which a statistical problem is conditionally broken into smaller pieces. This conditional decorrelation is not possible for arbitrarily large problems due to issues of computational complexity and numerical stability. Given the growing interest in global-scale-remote sensing problems (or even three-dimensional problems), in this summary we develop a class of estimators with more promising asymptotic computational properties

}, keywords = {asymptotic computational properties, computational complexity, conditional decorrelation, decorrelation, divide and conquer methods, divide-and-conquer strategy, foveated multiscale models, global-scale-remote sensing problems, image processing, large-scale estimation methods, multiscale estimation, nested dissection, numerical stability, statistical problem, three-dimensional problems}, doi = {http://dx.doi.org/10.1109/ICIP.1999.823022}, author = {P Fieguth} } @inproceedings {514, title = {Recursive multiscale estimation of space-time random fields}, booktitle = {International Conference on Image Processing}, year = {1999}, abstract = {

We recently developed a multiscale recursive estimation procedure for the estimation of large-scale dynamic systems. The procedure propagates multiscale models for the estimation errors more efficiently than the Kalman filter\&$\#$39;s propagation of the error covariances, with a resulting computational complexity of Oscr;(N) and Oscr;(N3/2 ), where N is the number of variables estimated, for 1-D and 2-D dynamic systems, respectively. To further reduce the computational cost, we introduce in this paper a new class of reduced-order spatially-interpolated multiscale models, and demonstrate their use in remote

}, keywords = {computational complexity, estimation errors, image processing, large-scale dynamic systems, multiscale models, recursive estimation, recursive multiscale estimation, reduced-order spatially-interpolated multiscale models, space-time adaptive processing, space-time random fields}, doi = {http://dx.doi.org/10.1109/ICIP.1999.823021}, author = {T T. Ho and P Fieguth and A S. Wilsky} }