@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 {168,
	title = {Automatic skin lesion segmentation via iterative stochastic region merging},
	journal = {IEEE Transactions on Information Technology in Biomedicine},
	volume = {15},
	year = {2011},
	pages = {929 - 936},
	abstract = {<p>An automatic method for segmenting skin lesions in conventional macroscopic images is presented. The images are acquired with conventional cameras, without the use of a dermoscope. Automatic segmentation of skin lesions from macroscopic images is a very challenging problem due to factors such as illumination variations, irregular structural and color variations, the presence of hair, as well as the occurrence of multiple unhealthy skin regions. To address these factors, a novel iterative stochastic region-merging approach is employed to segment the regions corresponding to skin lesions from the macroscopic images, where stochastic region merging is initialized first on a pixel level, and subsequently on a region level until convergence. A region merging likelihood function based on the regional statistics is introduced to determine the merger of regions in a stochastic manner. Experimental results show that the proposed system achieves overall segmentation error of under 10\% for skin lesions in macroscopic images, which is lower than that achieved by existing methods.</p>
},
	keywords = {iterative, lesion, region merging, skin cancer, stochastic},
	issn = {1089-7771},
	doi = {http://dx.doi.org/10.1109/TITB.2011.2157829},
	author = {A Wong and J Scharcanski and P Fieguth}
}
