Registration is a fundamental task in image processing and quite a few registration techniques have been developed in various fields. In this paper we propose a novel graph-representation method for image registration with Renyi entropy as the dissimilarity metric between the images. The image matching is performed by minimizing the length of the minimum spanning tree (MST) which spans the graph generated from the overlapped images. Our method also takes advantage of the minimum k-point spanning tree (k-MST) approach to robustify the registration against spurious discrepancies in the images. The proposed algorithm is tested in two applications: registering magnetic resonance (MR) images, and registering an electro-optical image with a terrain height map. In both cases the algorithm is shown to be accurate and robust.

We develop and investigate several novel multiresolution algorithms for detecting coherent radar targets embedded in clutter. These multiresolution detectors exploit the fact that prominent target scatterers interfere in a characteristic manner as resolution is changed, while multiresolution clutter signatures are random. We show, both on simulated and collected synthetic aperture radar data, that these multiresolution algorithms yield significant detection improvements over single-pixel, single-resolution constant false alarm rate (CFAR) methods that use only the finest available resolution.

Reviews the characteristics of hyperspectral imaging sensors and describes several important data exploitation applications in remote sensing. The authors then focus on a particular signal processing application, material identification, and propose a novel algorithm based on multiresolution wavelet techniques. Finally, they demonstrate the multiresolution material identification algorithm on data collected with a all-band hyperspectral sensor.

We demonstrate the utility of a multiresolution approach for target detection in SAR imagery. Man-made objects exhibit characteristic phase and amplitude fluctuations as the image resolution is varied, while natural terrain has a random signature. We construct a number of detection strategies: an optimal invariant multiresolution detector based on a derived multiresolution increments process; and a generalized likelihood ratio detector to differentiate between a first-order autoregressive multiresolution increments process and white noise. We show that these schemes significantly outperform a standard energy detector operating on the finest available SAR resolution.

We present a novel vector quantizer (VQ) image coding algorithm, called synthetic texture VQ (STVQ), that employs shape-gain techniques to adaptively modify a synthetically-generated codebook to changing image statistics. We recursively apply STVQ to encode high-pass bands of a wavelet transform using the low-pass band as side information. An advantage of our approach is that no training is required to construct codebooks. We demonstrate STVQ on synthetic aperture radar (SAR) imagery and show that it provides efficient coding of the characteristic SAR image texture while preserving perceptual quality. Preliminary comparisons indicate that STVQ's perceptual performance exceeds JPEG at comparable rates, even though the mean-squared error performance of JPEG is better than STVQ.

Examines the problem of detecting a known target in clutter and shows that a multiresolution-based detector significantly outperforms a more conventional single-resolution detector. The authors then apply a sampling strategy that allows one to choose, for each fixed n, those n resolutions that maximize detection performance. This optimal multiresolution sampling strategy suggests that wavelet or wavelet packet decompositions which typically use dyadic or triadic resolution splits are not necessarily optimal bases for coherent radar signature representation.>

Using limiting forms of the Chapman-Robbins (1951) version of the Barankin bound, a study is made of the effect of parameter constraints on local lower bounds on estimator covariance. One such limiting form is the Cramer-Rao bound, for which constraints are seen to induce an oblique projection of the columns of the inverse Fisher information matrix onto a linear subspace tangent to the parameter constraint set. Another limiting form is the Bhattacharyya bound, which is defined in terms of higher-order Fisher information. For the Bhattacharyya bound, parameter constraints induce a transformation of the higher-order Fisher information that depends on the tangent space projection operator and its derivatives.>

A Chapman-Robbins form of the Barankin bound is used to derive a multiparameter Cramer-Rao (CR) type lower bound on estimator error covariance when the parameter theta in R/sup n/ is constrained to lie in a subset of the parameter space. A simple form for the constrained CR bound is obtained when the constraint set Theta /sub C/, can be expressed as a smooth functional inequality constraint. It is shown that the constrained CR bound is identical to the unconstrained CR bound at the regular points of Theta /sub C/, i.e. where no equality constraints are active. On the other hand, at those points theta in Theta /sub C/ where pure equality constraints are active the full-rank Fisher information matrix in the unconstrained CR bound must be replaced by a rank-reduced Fisher information matrix obtained as a projection of the full-rank Fisher matrix onto the tangent hyperplane of the full-rank Fisher matrix onto the tangent hyperplane of the constraint set at theta . A necessary and sufficient condition involving the forms of the constraint and the likelihood function is given for the bound to be achievable, and examples for which the bound is achieved are presented. In addition to providing a useful generalization of the CR bound, the results permit analysis of the gain in achievable MSE performance due to the imposition of particular constraints on the parameter space without the need for a global reparameterization.>