@article {169, title = {A Bayesian theoretic approach to multi-scale complex phase order representations}, journal = {IEEE Transactions on Image Processing}, year = {2011}, abstract = {

This paper explores a Bayesian theoretic approach to constructing multi-scale complex phase order representations. We formulate the construction of complex phase order representations at different structural scales based on scale space theory. Linear and nonlinear deterministic approaches are explored, and a Bayesian theoretic approach is introduced for constructing representations in such a way that strong structure localization and noise resilience is achieved. Experiments illustrate its potential for constructing robust multi-scale complex phase order representations with well-localized structures across all scales under high noise situations. Illustrative examples of applications of the proposed approach is presented in the form of multimodal image registration and feature extraction.

}, keywords = {Bayesian, complex phase order, Feature Extraction, registration}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/TIP.2011.2160352}, author = {A Wong} } @article {172, title = {Efficient globally optimal registration of remote sensing imagery via quasi-random scale-space structural correlation energy functional}, journal = {IEEE Geoscience and Remote Sensing Letters}, volume = {8}, year = {2011}, pages = {997 - 1001}, abstract = {

A novel energy functional for automatic registration of remote sensing imagery based on quasi-random scale-space structural correlation is presented. The structural correlation energy functional takes advantage of the fact that, for many types of remote sensing imagery, there exist common structures at different scales even if the acquired images have very different intensity characteristics. The proposed energy functional also takes advantage of the noise robustness and feature localization properties of quasi-random scale-space theory. An efficient globally exhaustive optimization strategy in the frequency domain is developed for registering remote sensing imagery based on the proposed energy functional. Promising test results on interband, intraband, and intermodal remote sensing image sets show that the proposed method has the advantage of being robust to differing sensing conditions and large misalignments.

}, keywords = {Energy functional, quasi-random scale space, registration, structural correlation}, issn = {1545-598X}, doi = {http://dx.doi.org/10.1109/LGRS.2011.2142172}, author = {W Zhang and A Wong and A Mishra and P Fieguth and D A. Clausi} } @inproceedings {344, title = {Efficient and robust approach to simultaneous non-rigid image registration and gamma estimation in the frequency domain}, booktitle = {2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition}, year = {2008}, abstract = {

This paper presents an efficient and robust method for performing non-rigid image registration and relative gamma estimation in a simultaneous manner. The proposed method identifies regions of interest within the target image and determines the optimal correspondence and relative gamma between each region of interest and the reference image. The problem of finding the optimal correspondence and relative gamma between each region of interest and the reference image is combined and formulated such that both problems can be solved as a single minimum squared distance problem efficiently using the Fast Fourier Transform (FFT). The resulting correspondence and relative gamma information is used to estimate the geometric transformation between the target image and the reference image, as well as perform gamma correction between the images. Experimental results demonstrate that good registration and relative gamma estimation accuracy can be achieved using the proposed method, making it well-suited for registering images captured under different levels of exposure.

}, keywords = {frequency domain, gamma estimation, Non-rigid, registration, simultaneous}, author = {A Wong and W Bishop} } @inproceedings {377, title = {Simultaneous gamma correction and registration in the frequency domain}, booktitle = {International Conference on Image Processing, Computer Vision, and Pattern Recognition}, year = {2007}, abstract = {

This paper presents a novel method for performing the registration and gamma correction of images in a simultaneous manner. The image registration and relative gamma estimation problems are formulated such that both problems are solved at the same time by solving a single minimum squared distance problem. The proposed method utilizes the Fast Fourier Transform (FFT) to solve the combined registration and relative gamma estimation problem in an efficient manner. This method is invariant to gamma differences between images, making it suitable for registering images acquired under different exposure levels and illumination conditions. Experimental results demonstrate that high registration accuracy and high relative gamma estimation accuracy can be achieved using the proposed algorithm.

}, keywords = {frequency domain, gamma correction, registration, simultaneous}, author = {A Wong and W Bishop} } @inproceedings {402, title = {Efficient and robust non-rigid least-squares rectification of medical images}, booktitle = {2006 International Conference on Image Processing, Computer Vision, and Pattern Recognition}, year = {2006}, address = {Las Vegas, Nevada}, abstract = {

One of the major problems facing medical imaging is the presence of geometric distortions inherent in an imaging technique. Image registration techniques are often used to correct for such geometric perturbations. Recently, it was proposed that the SSD cost function can be evaluated efficiently using the Fast Fourier Transform (FFT) to determine the optimal translation between two images. However, spatial distortions in medical images can be highly non-rigid in nature. This paper extends this efficient approach to allow for non-rigid alignment between two images through the use of patch correspondence and robust statistical model estimation techniques. This feature-based method is designed to be highly robust, making it suitable for aligning medical images with various forms of geometric distortions. Experimental results demonstrate high overall image rectification performance.

}, keywords = {image rectification, least-squares, medical imaging, registration, robust statistics}, author = {A Wong and J Orchard} }