@article {280,
	title = {Robust multimodal registration using local phase-coherence representations},
	journal = {Journal of Signal Processing Systems},
	volume = {54},
	year = {2009},
	pages = {89 - 100},
	abstract = {<p>Automatic registration of multimodal images has proven to be a difficult task. Most existing techniques have difficulty dealing with situations involving highly non-homogeneous image contrast and a small initial overlapping region between the images. This paper presents a robust multi-resolution method for regis tering multimodal images using local phase-coherence representations. The proposed method finds the transformation that minimizes the error residual between the local phase-coherence representations of the two multimodal images. The error residual can be minimized using a combination of efficient globally exhaustive optimization techniques and subpixel-level local optimization techniques to further improve robustness in situations with small initial overlap. The proposed method has been tested on various medical images acquired using different modalities and evaluated based on its registration accuracy. The results show that the proposed method is capable of achieving better accuracy than existing multimodal registration techniques when handling situations where image non-homogeneity and small overlapping regions exist.</p>
},
	keywords = {Local phase-coherence, Multimodal registration, Non homogeneity, Non-rigid},
	issn = {1939-8018},
	doi = {http://dx.doi.org/10.1007/s11265-008-0202-x},
	author = {A Wong and J Orchard}
}
@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 = {<p>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.</p>
},
	keywords = {frequency domain, gamma estimation, Non-rigid, registration, simultaneous},
	author = {A Wong and W Bishop}
}
