@inproceedings {339,
	title = {Simultaneous multi-modal registration of multiple images based on multi-dimensional joint phase moment distributions},
	booktitle = {19th International Conference on Pattern Recognition},
	year = {2008},
	abstract = {<p>In this paper, a novel method for simultaneously registering multiple images acquired from different imaging modalities is presented. The optimal alignment is computed as the set of transformations that minimize the dispersion of the multi-dimensional joint phase moment distribution. Dispersion is measured as the cumulative quadratic orthogonal distance between samples from the joint phase moment distribution and the corresponding multi-dimensional fitted hyperline. The proposed method is designed to be computationally efficient, robust to signal non-homogeneities and noise, and maintains internal consistency amongst all images. Experimental results using real-world medical and remote sensing images show that the proposed method achieves a high level of registration accuracy when simultaneously registering multiple multimodal images.</p>
},
	keywords = {cumulative quadratic orthogonal distance, image dispersion minimisation, image registration, imaging modality, minimisation, multidimensional fitted hyperline, multidimensional joint phase moment distribution, simultaneous multimodal multiple image registration},
	doi = {http://dx.doi.org/10.1109/ICPR.2008.4761912},
	author = {A Wong}
}
@inproceedings {491,
	title = {Multiscale Methods for the Segmentation of Images},
	booktitle = {ICASSP {\textquoteright}96},
	year = {1996},
	abstract = {<p>This work presents a method for segmenting images based on gradients in the intensity function. Past approaches have centered on formulating the problem in the context of variational calculus as the minimization of a functional involving the image intensity and edge functions. Computational methods for finding the minima of such variational problems are prone to two shortfalls: they are often computationally intensive and almost always incapable of computing error statistics associated with the segmentation. Using a particular variational formulation as a starting point, this paper presents a derivation of an associated statistical formulation using multiscale models. The result is an algorithm which is fast and capable of computing error statistics</p>
},
	keywords = {error statistics, functional, functional equations, gradients, image segmentation, images, intensity function, minimisation, minimization, multiscale models, Segmentation, statistical formulation, variational calculus, variational techniques},
	doi = {http://dx.doi.org/10.1109/ICASSP.1996.545869},
	author = {M Schneider and P Fieguth and W C. Karl and A S. Willsky}
}
