@inproceedings {34,
	title = {TIGER: A Texture-Illumination Guided Energy Response Model for Illumination Robust Local Saliency},
	booktitle = {IEEE International Conference on Image Processing},
	year = {2015},
	abstract = {<p>Local saliency models are a cornerstone in image processing and computer vision, used in a wide variety of applications ranging from keypoint detection and feature extraction, to image matching and image representation. However, current models exhibit difficulties in achieving consistent results under varying, non-ideal illumination conditions. In this paper, a novel texture-illumination guided energy response (TIGER) model for illumination robust local saliency is proposed. In the TIGER model, local saliency is quantified by a modified Hessian energy response guided by a weighted aggregate of texture and illumination aspects from the image. A stochastic Bayesian disassociation approach via Monte Carlo sampling is employed to decompose the image into its texture and illumination aspects for the saliency computation. Experimental results demonstrate that higher correlation between local saliency maps constructed from the same scene under different illumination conditions can be achieved using the TIGER model when compared to common local saliency approach, i.e., Laplacian of Gaussian, Difference of Gaussians, and Hessian saliency models.</p>
},
	keywords = {Bayesian estimation, illumination, illumination robust, local features, local saliency model, stochastic, texture},
	author = {B Chwyl and A Chung and F Li and A Wong 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}
}
