@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}
}
@inproceedings {448,
	title = {Using the Canny edge detector for feature extraction and enhancement of remote sensing images},
	booktitle = {IEEE Geoscience and Remote Sensing Symposium},
	year = {2001},
	abstract = {<p>Edges are important features in an image since they represent significant local intensity changes. They provide important clues to separate regions within an object or to identify changes in illumination. Most remote sensing applications, such as image registration, image segmentation, region separation, object description, and recognition, use edge detection as a preprocessing stage for feature extraction. Real images, such as remote sensing images, can be corrupted with point noise. The real problem is how to enhance noisy remote sensing images and simultaneously extract the edges. Using the implemented Canny edge detector for feature extraction and as an enhancement tool for remote sensing images, the result was robust with a very high enhancement level</p>
},
	keywords = {Canny edge detector, edge detection, enhancement, Feature Extraction, geophysical signal processing, illumination, image enhancement, local intensity changes, noise, point noise, preprocessing stage, real images, remote sensing images, terrain mapping},
	doi = {http://dx.doi.org/10.1109/IGARSS.2001.977981},
	author = {M Ali and D A. Clausi}
}
