@inproceedings {186,
	title = {Multi-Scale 3D representation via volumetric quasi-random scale space},
	booktitle = {18th IEEE International Conference on Image Processing (ICIP 2011)},
	year = {2011},
	month = {09/2011},
	abstract = {<p>A novel nonlinear volumetric scale-space framework is proposed for multi-scale volumetric data representation. The problem is formulated as a Bayesian least-squares estimator, and a quasi-random density estimation approach is introduced for estimating the posterior distribution between consecutive volumetric scale space realizations. Experimental results using both synthetic and real MR volumetric data demonstrate the effectiveness of the proposed scale-space framework for three-dimensional representation with significantly better structural separation and localization across all scales when compared to existing volumetric scale-space frameworks such as volumetric anisotropic diffusion and volumetric linear Gaussian scale-space, especially under scenarios with high noise levels.</p>
},
	keywords = {multi-scale, random sampling, Scale space, volumetric representation},
	author = {A Mishra and A Wong and P Fieguth and D A. Clausi}
}
@article {227,
	title = {Quasi-random nonlinear scale space},
	journal = {Pattern Recognition Letters},
	volume = {31},
	year = {2010},
	pages = {1850 - 1859},
	abstract = {<p>A novel nonlinear scale space framework is proposed for the purpose of multi-scale image representation. The scale space decomposition problem is formulated as a general Bayesian least-squares estimation problem. A quasi-random density estimation approach is introduced for estimating the posterior distribution between consecutive scale space realizations. In addition, the application of the proposed nonlinear scale space framework for edge detection is proposed. Experimental results demonstrate the effectiveness of the proposed scale space framework for constructing scale space representations with significantly better structural localization across all scales when compared to state-of-the-art scale space frameworks such as anisotropic diffusion, regularized nonlinear diffusion, complex nonlinear diffusion, and iterative bilateral scale space methods, especially under scenarios with high noise levels</p>
},
	keywords = {Anisotropic diffusion, Bayesian estimation, edge detection, multi-scale, Nonlinear scale space, Quasi-random},
	author = {A Mishra and A Wong and D A. Clausi and P Fieguth}
}
