@inproceedings {327, title = {Automatic registration of inter-band and inter-sensor images using robust complex wavelet feature representations}, booktitle = {5th Workshop on Pattern Recognition for Remote Sensing}, year = {2008}, month = {12/2008}, address = {Tampa, Florida, USA}, abstract = {

A robust method for registering inter-band and inter-sensor remote sensing images has been designed and implemented. The proposed method introduces noise-resilient and contrast invariant control point detection and control point matching schemes based on robust complex wavelet feature representations. Furthermore, an iterative refinement scheme is introduced for achieving improved control point pair localization and mapping function estimation between the images being registered. The registration accuracy of the proposed method was demonstrated on the registration of multi-spectral optical and synthetic aperture radar (SAR) images. The proposed method achieves better registration accuracy when compared with the state-of-the-art MSSD and ARRSI registration algorithms.

}, keywords = {ARRSI, automatic registration, contrast invariant control point detection, control point matching schemes, control point pair localization, estimation theory, Feature Extraction, image matching, image registration, image representation, images registration, inter-band images, inter-sensor images, iterative methods, iterative refinement scheme, mapping function estimation, MSSD, multispectral optical images, registration algorithms, Remote Sensing, remote sensing images, robust complex wavelet feature representations, synthetic aperture radar, synthetic aperture radar images, wavelet transforms}, doi = {http://dx.doi.org/10.1109/PRRS.2008.4783164}, author = {A Wong and D A. Clausi and P Fieguth} } @inproceedings {338, title = {Robust edge detection based on non-local contribution of local frequency characteristics}, booktitle = {IEEE International Symposium on Multimedia}, year = {2008}, abstract = {

This paper introduces a robust approach to the problem of edge detection in situations characterized by high levels of image degradation and illumination non-uniformity. Popular edge detection methods are dependent solely on local image characteristics to localize edge information. In the proposed algorithm, we demonstrate that superior edge detection performance can be achieved by utilizing nonlocal image characteristics. This is accomplished by estimating the edge strength of a given pixel based on the local frequency characteristics of every pixel within an image. The proposed method is highly robust to scenarios characterized by illumination non-uniformity and low signal-to-noise ratios. Experimental results show that using the proposed method, edge detection can be qualitatively improved over existing methods on a small set of test images.

}, keywords = {edge detection, edge strength estimation, estimation theory, illumination nonuniformity, image degradation, local frequency characteristics, robust edge detection, signal-to-noise ratio}, doi = {http://dx.doi.org/10.1109/ISM.2008.79}, author = {A Wong and W Bishop} } @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 {453, title = {Multiresolution model development for overlapping trees via canonical correlation analysis}, booktitle = {International Conference on Image Processing}, year = {1995}, abstract = {

A class of multiscale stochastic models has been introduced in which Gaussian random processes are described by scale-recursive dynamics that are indexed by the nodes of a tree. One of the primary reasons the framework is useful is that it leads to an extremely fast, statistically optimal algorithm for least-squares estimation in the context of 2-D images. We refine this approach to estimation by eliminating the visually distracting blockiness that has been observed in the previous work. We eliminate the blockiness by discarding the standard assumption that distinct nodes at a given level of our tree correspond to disjoint portions of the image domain; as a consequence of this simple idea, a given image pixel may now correspond to several tree nodes. We develop tools for systematically building overlapping-tree multiscale representations of prespecified statistics, and we develop a corresponding estimation algorithm for this processes. In this way, we achieve nearly optimal estimation results, we generate corresponding error covariance information, and we eliminate blockiness without sacrificing the resolution of fine-scale detail

}, keywords = {canonical correlation analysis, correlation methods, covariance analysis, error analysis, error covariance information, estimation algorithm, estimation theory, fast statistically optimal algorithm, Gaussian processes, Gaussian random processes, image domain, image pixel, image representation, image resolution, multiresolution model, multiscale representations, multiscale stochastic models, optimal estimation results, overlapping trees, random processes, scale recursive dynamics, statistics, tree nodes, trees (mathematics)}, doi = {http://dx.doi.org/10.1109/ICIP.1995.529035}, author = {P Fieguth and W W. Irving and A S. Willsky} }